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The Design and Verification of Dynamic-sized Nonblocking Data Structures

<p>Modern computer systems often involve multiple processes or threads of control that communicate through shared memory. However, the implementation of correct and efficient data structures that can be shared by several processes is frequently challenging. This thesis is concerned with the design and verification of a class of shared memory algorithms known as nonblocking algorithms, which are implementations of shared data structures that provide strong progress guarantees. Nonblocking algorithms offer an appealing alternative to traditional techniques for the implementation of shared memory data structures, but they are difficult to design, and extant algorithms can often be applied in only a limited range of systems. Furthermore, because of their subtlety, it is notoriously difficult to determine whether a given nonblocking algorithm is correct. This thesis addresses these difficulties in two ways. First, we present techniques for the verification of nonblocking algorithms that dynamically allocate memory. These techniques allow the construction of formal and complete proofs of correctness, so that each proof may be checked by a mechanical proof assistant. Applying techniques first developed for the verification of distributed algorithms, we use labelled-transition systems to model algorithms and their specifications, and simulation relations to prove that an implementation meets its specification. Nonblocking algorithms often require a particular notion of simulation, called backward simulation, that is rarely necessary in other contexts. This thesis contributes to the relatively limited collective experience in the use of backward simulation. The second set of contributions addresses the limitations of many extant nonblocking algorithms. While many nonblocking algorithms allocate memory dynamically, it is difficult to determine in a nonblocking context when it is safe to free memory. We present techniques to accomplish this. Furthermore, many nonblocking algorithms depend on the availability of two powerful synchronisation primitives, known as load-linked and store-conditional, which are not normally provided by hardware. We present implementations of these primitives that work on commonly available platforms.</p>

Orthogonal Range Search Data Structures

AbstractWe first review existing space-efficient data structures for the orthogonal range search problem. Then, we propose two improved data structures, the first of which has better query time complexity than the existing structures and the second of which has better space complexity that matches the information-theoretic lower bound.

Efficient Data Structures

Efficient data structures for data-driven mechanics, sat solving with gpu accelerated inprocessing.

AbstractSince 2013, the leading SAT solvers in the SAT competition all use inprocessing, which unlike preprocessing, interleaves search with simplifications. However, applying inprocessing frequently can still be a bottle neck, i.e., for hard or large formulas. In this work, we introduce the first attempt to parallelize inprocessing on GPU architectures. As memory is a scarce resource in GPUs, we present new space-efficient data structures and devise a data-parallel garbage collector. It runs in parallel on the GPU to reduce memory consumption and improves memory access locality. Our new parallel variable elimination algorithm is twice as fast as previous work. In experiments our new solver ParaFROST solves many benchmarks faster on the GPU than its sequential counterparts.

I/O-efficient data structures for non-overlapping indexing

Efficient data structures for range shortest unique substring queries.

Let T[1,n] be a string of length n and T[i,j] be the substring of T starting at position i and ending at position j. A substring T[i,j] of T is a repeat if it occurs more than once in T; otherwise, it is a unique substring of T. Repeats and unique substrings are of great interest in computational biology and information retrieval. Given string T as input, the Shortest Unique Substring problem is to find a shortest substring of T that does not occur elsewhere in T. In this paper, we introduce the range variant of this problem, which we call the Range Shortest Unique Substring problem. The task is to construct a data structure over T answering the following type of online queries efficiently. Given a range [α,β], return a shortest substring T[i,j] of T with exactly one occurrence in [α,β]. We present an O(nlogn)-word data structure with O(logwn) query time, where w=Ω(logn) is the word size. Our construction is based on a non-trivial reduction allowing for us to apply a recently introduced optimal geometric data structure [Chan et al., ICALP 2018]. Additionally, we present an O(n)-word data structure with O(nlogϵn) query time, where ϵ>0 is an arbitrarily small constant. The latter data structure relies heavily on another geometric data structure [Nekrich and Navarro, SWAT 2012].

Dynamic and Internal Longest Common Substring

Abstract Given two strings S and T, each of length at most n, the longest common substring (LCS) problem is to find a longest substring common to S and T. This is a classical problem in computer science with an $$\mathcal {O}(n)$$ O ( n ) -time solution. In the fully dynamic setting, edit operations are allowed in either of the two strings, and the problem is to find an LCS after each edit. We present the first solution to the fully dynamic LCS problem requiring sublinear time in n per edit operation. In particular, we show how to find an LCS after each edit operation in $$\tilde{\mathcal {O}}(n^{2/3})$$ O ~ ( n 2 / 3 ) time, after $$\tilde{\mathcal {O}}(n)$$ O ~ ( n ) -time and space preprocessing. This line of research has been recently initiated in a somewhat restricted dynamic variant by Amir et al. [SPIRE 2017]. More specifically, the authors presented an $$\tilde{\mathcal {O}}(n)$$ O ~ ( n ) -sized data structure that returns an LCS of the two strings after a single edit operation (that is reverted afterwards) in $$\tilde{\mathcal {O}}(1)$$ O ~ ( 1 ) time. At CPM 2018, three papers (Abedin et al., Funakoshi et al., and Urabe et al.) studied analogously restricted dynamic variants of problems on strings; specifically, computing the longest palindrome and the Lyndon factorization of a string after a single edit operation. We develop dynamic sublinear-time algorithms for both of these problems as well. We also consider internal LCS queries, that is, queries in which we are to return an LCS of a pair of substrings of S and T. We show that answering such queries is hard in general and propose efficient data structures for several restricted cases.

FSMI: Fast computation of Shannon mutual information for information-theoretic mapping

Exploration tasks are embedded in many robotics applications, such as search and rescue and space exploration. Information-based exploration algorithms aim to find the most informative trajectories by maximizing an information-theoretic metric, such as the mutual information between the map and potential future measurements. Unfortunately, most existing information-based exploration algorithms are plagued by the computational difficulty of evaluating the Shannon mutual information metric. In this article, we consider the fundamental problem of evaluating Shannon mutual information between the map and a range measurement. First, we consider 2D environments. We propose a novel algorithm, called the fast Shannon mutual information (FSMI). The key insight behind the algorithm is that a certain integral can be computed analytically, leading to substantial computational savings. Second, we consider 3D environments, represented by efficient data structures, e.g., an OctoMap, such that the measurements are compressed by run-length encoding (RLE). We propose a novel algorithm, called FSMI-RLE, that efficiently evaluates the Shannon mutual information when the measurements are compressed using RLE. For both the FSMI and the FSMI-RLE, we also propose variants that make different assumptions on the sensor noise distribution for the purpose of further computational savings. We evaluate the proposed algorithms in extensive experiments. In particular, we show that the proposed algorithms outperform existing algorithms that compute Shannon mutual information as well as other algorithms that compute the Cauchy–Schwarz quadratic mutual information (CSQMI). In addition, we demonstrate the computation of Shannon mutual information on a 3D map for the first time.

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Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

Research topics and ideas about data science and big data analytics

If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Research topic idea mega list

Data Science-Related Research Topics

  • Developing machine learning models for real-time fraud detection in online transactions.
  • The use of big data analytics in predicting and managing urban traffic flow.
  • Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
  • The application of predictive analytics in personalizing cancer treatment plans.
  • Analyzing consumer behavior through big data to enhance retail marketing strategies.
  • The role of data science in optimizing renewable energy generation from wind farms.
  • Developing natural language processing algorithms for real-time news aggregation and summarization.
  • The application of big data in monitoring and predicting epidemic outbreaks.
  • Investigating the use of machine learning in automating credit scoring for microfinance.
  • The role of data analytics in improving patient care in telemedicine.
  • Developing AI-driven models for predictive maintenance in the manufacturing industry.
  • The use of big data analytics in enhancing cybersecurity threat intelligence.
  • Investigating the impact of sentiment analysis on brand reputation management.
  • The application of data science in optimizing logistics and supply chain operations.
  • Developing deep learning techniques for image recognition in medical diagnostics.
  • The role of big data in analyzing climate change impacts on agricultural productivity.
  • Investigating the use of data analytics in optimizing energy consumption in smart buildings.
  • The application of machine learning in detecting plagiarism in academic works.
  • Analyzing social media data for trends in political opinion and electoral predictions.
  • The role of big data in enhancing sports performance analytics.
  • Developing data-driven strategies for effective water resource management.
  • The use of big data in improving customer experience in the banking sector.
  • Investigating the application of data science in fraud detection in insurance claims.
  • The role of predictive analytics in financial market risk assessment.
  • Developing AI models for early detection of network vulnerabilities.

Research topic evaluator

Data Science Research Ideas (Continued)

  • The application of big data in public transportation systems for route optimization.
  • Investigating the impact of big data analytics on e-commerce recommendation systems.
  • The use of data mining techniques in understanding consumer preferences in the entertainment industry.
  • Developing predictive models for real estate pricing and market trends.
  • The role of big data in tracking and managing environmental pollution.
  • Investigating the use of data analytics in improving airline operational efficiency.
  • The application of machine learning in optimizing pharmaceutical drug discovery.
  • Analyzing online customer reviews to inform product development in the tech industry.
  • The role of data science in crime prediction and prevention strategies.
  • Developing models for analyzing financial time series data for investment strategies.
  • The use of big data in assessing the impact of educational policies on student performance.
  • Investigating the effectiveness of data visualization techniques in business reporting.
  • The application of data analytics in human resource management and talent acquisition.
  • Developing algorithms for anomaly detection in network traffic data.
  • The role of machine learning in enhancing personalized online learning experiences.
  • Investigating the use of big data in urban planning and smart city development.
  • The application of predictive analytics in weather forecasting and disaster management.
  • Analyzing consumer data to drive innovations in the automotive industry.
  • The role of data science in optimizing content delivery networks for streaming services.
  • Developing machine learning models for automated text classification in legal documents.
  • The use of big data in tracking global supply chain disruptions.
  • Investigating the application of data analytics in personalized nutrition and fitness.
  • The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
  • Developing predictive models for customer churn in the telecommunications industry.
  • The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

  • Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
  • Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
  • Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
  • Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
  • An Essay on How Data Science Can Strengthen Business (Santos, 2023)
  • A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
  • You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
  • Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
  • Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
  • A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
  • Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
  • Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
  • Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
  • Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
  • TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
  • Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
  • Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

Research Topic Kickstarter - Need Help Finding A Research Topic?

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Data Structures Research Topics Areas

Data Structures Research Topics for MS and Ph.D Thesis

  • Data structures and algorithm analysis
  • On MATLAB experience in accelerating DIRECT-GLce algorithm for constrained global optimization through dynamic data structures and parallelization
  • New data structures for orthogonal range reporting and range minima queries
  • Peeling Close to the Orientability Threshold–Spatial Coupling in Hashing-Based Data Structures
  • Data Structures to Represent a Set of k-long DNA Sequences
  • Seaborn: statistical data visualization
  • Conflict-driven clause learning SAT solvers
  • Scalable Mesh Partitioning for Large-Scale 3D Finite Element-Multibody Structures
  • A Multimedia Synchronization Service and Implementation Architecture for Pre-orchestrated Data Communications
  • Evolution of standardization and dissemination of cryo-EM structures and data jointly by the community, PDB and EMDB
  • Combined detection and segmentation of archeological structures from LiDAR data using a deep learning approach
  • Subtropical Countercurrents: Band structures revealed from CSK data
  • RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental …
  • On the optimal layout of balanced complete multipartite graphs into grids and tree related structures
  • Further Unifying the Landscape of Cell Probe Lower Bounds
  • Linkage of deep lithospheric structures to intraplate earthquakes: A perspective from multi-source and multi-scale geophysical data in the South China Block
  • The impact of uneven temperature distribution on stability of concrete structures using data analysis and numerical approach
  • Palabos: parallel lattice Boltzmann solver
  • Reproduction of spatio-temporal structures of traffic flows using various ways of averaging data
  • A data-driven analysis on bridging techniques for heterogeneous materials and structures
  • A Corpus Based Approach to the Analysis of Structures in Prepositional Phrase
  • Minimizing the risk of deducing wrong natural product structures from NMR data
  • PIPS: Extension of the Internal Representation for C
  • PIPS: Memory effects of Statements
  • Designing an Intuitionistic Fuzzy Network Data Envelopment Analysis Model for Efficiency Evaluation of Decision-Making Units with Two-Stage Structures
  • VIPERdb v3. 0: a structure-based data analytics platform for viral capsids
  • Data-science based analysis of patient subgroup structures suggest effects of rhinitis on all chemosensory perceptions in the upper airways
  • Fast and Simple Modular Subset Sum*
  • Predicting dynamic cellular protein–RNA interactions by deep learning using in vivo RNA structures
  • Enhancing the Teaching and Learning of Computer Programming using Collaborative Method of Delivery
  • jl: Ambient Seismic Noise Cross Correlation on the CPU and GPU in Julia
  • dNEMO: a tool for quantification of mRNA and punctate structures in time-lapse images of single cells
  • Gauss-Legendre Features for Gaussian Process Regression
  • Local damage detection based on vibration data analysis in the presence of Gaussian and heavy-tailed impulsive noise
  • Machine-learning mathematical structures
  • Machine learning-assisted modeling of composite materials and structures: a review
  • Performance analysis of distributed storage clusters based on kernel and userspace traces
  • In defense of decentralized research data management
  • Technology Bundles in Maintenance: Ambivalences Between Virtuality and Assistance
  • Adapting to one-and two-way classified structures of hypotheses while controlling the false discovery rate
  • Legal Mobilization Against the Data Retention Directive—Opportunity Structures, Actors and Strategies
  • Interpretation of psychiatric genome-wide association studies with multispecies heterogeneous functional genomic data integration
  • Acoustic emission wireless monitoring of structures
  • Tight bounds for online graph partitioning
  • Review of the Hydra Conference on Concurrent and Distributed Computing Hydra 2020
  • TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Tracking
  • Space-time covariance structures and models
  • CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks
  • Isolation and electronic structures of derivatized manganocene, ferrocene and cobaltocene anions
  • Enumeration Algorithms for Conjunctive Queries with Projection
  • International Society of Nephrology Global Kidney Health Atlas: structures, organization and services for the management of kidney failure in North and East Asia
  • The Use of Time to Pregnancy for Estimating and Monitoring Human Fecundity From Demographic and Health Surveys
  • Improving Cross-Lingual Transfer for Event Argument Extraction with Language-Universal Sentence Structures
  • Complex structures arising from the self-assembly of a simple organic salt
  • Identification of Cameroon’s geological structures through a gravity separation and using seismic crustal models
  • NFDI-Neuro: Building a community for neuroscience research data management in Germany
  • Predicting Tie Strength with Ego Network Structures
  • Identifying latent group structures in nonlinear panels
  • Quantitative psychology under scrutiny: Measurement requires not result-dependent but traceable data generation
  • Complementary Libraries to TensorFlow 2. x
  • On modelling disordered crystal structures through restraints from molecule-in-cluster computations, and distinguishing static and dynamic disorder
  • Blastocyst-like structures generated from human pluripotent stem cells
  • Refinement and depth
  • Structures of the archaerhodopsin-3 transporter reveal that disordering of internal water networks underpins receptor sensitization
  • Multiscale characterisation of chimneys/pipes: Fluid escape structures within sedimentary basins
  • Accurate crystal structures and chemical properties from NoSpherA2
  • Atomically resolved interfacial water structures on crystalline hydrophilic and hydrophobic surfaces
  • An O(n5/4) Time ?-Approximation Algorithm for RMS Matching in a Plane
  • Social contagion on higher-order structures
  • An effective method for damage assessment based on limited measured locations in skeletal structures
  • Classifying oceanographic structures in the Amundsen Sea, Antarctica
  • A Procedure for 3D Modelling of Very Large Geotechnical Structures: Open Cast Coal Mine Case
  • RAFTS for basic blocks: A progress report on Forth native code compilation
  • Near-optimal randomized algorithms for selection in totally monotone matrices
  • Heat kernel analysis of syntactic structures
  • Soft Sequence Heaps
  • A note on a recent algorithm for minimum cut
  • NewGen: A Language-Independent Program Generator Report ENSMP/CRI/A-191
  • College of American Pathologists Cancer Protocols: From Optimizing Cancer Patient Care to Facilitating Interoperable Reporting and Downstream Data Use
  • CoV3D: a database of high resolution coronavirus protein structures
  • Strategies to Effectively Integrate Visualization with Active Learning in Computer Science Class
  • Investigating the impacts of three-dimensional spatial structures on CO2 emissions at the urban scale
  • Pure and efficient functional programming using the “unique” features of Clean
  • Structures of German emigration and remigration: Historical developments and demographic patterns
  • A comparative study of Bowman’s layer in some mammals: Relationships with other constituent corneal structures
  • A fast minimum degree algorithm and matching lower bound
  • Archiving in Evolutionary Multi-objective Optimization: A Short Overview
  • Piezoelectric adventures: from energy harvesting and bioinspired robotics to programmable metamaterials and wireless data/power transfer
  • Assessing failure probability of coastal structures based on probabilistic representation of sea conditions at the structures’ location
  • A comparative study of the Linda model with other alternative models of parallel programming
  • Investigating the dynamic compression response of elastomeric, additively manufactured fluid-filled structures via experimental and finite element analyses
  • International Society of Nephrology Global Kidney Health Atlas: structures, organization, and services for the management of kidney failure in Newly Independent …
  • For geographical imagination systems
  • Efficient Artificial neural networks based on a hybrid metaheuristic optimization algorithm for damage detection in laminated composite structures
  • Structures of the glucocorticoid-bound adhesion receptor GPR97–G o complex
  • Visualization Techniques with Data Cubes: Utilizing Concurrency for Complex Data
  • Discovering multiscale and self-similar structure with data-driven wavelets
  • Sample weights determination based on cosine similarity method as an extension to infrared action recognition
  • The torsional response of civil engineering structures during earthquake from an observational point of view
  • Modular Subset Sum, Dynamic Strings, and Zero-Sum Sets
  • Structures of active-state orexin receptor 2 rationalize peptide and small-molecule agonist recognition and receptor activation
  • Structures of HCMV Trimer reveal the basis for receptor recognition and cell entry
  • Circadian Regulation of GluA2 mRNA Processing in the Rat Suprachiasmatic Nucleus and Other Brain Structures
  • A smoothed iFEM approach for efficient shape-sensing applications: Numerical and experimental validation on composite structures
  • Biomechanical Contributions of Spinal Structures with Different Degrees of Disc Degeneration
  • BlastFrost: fast querying of 100,000 s of bacterial genomes in Bifrost graphs
  • For more information about USENIX Association contact
  • A two-step damage quantitative identification method for beam structures
  • On the Creation of Quadtrees by Using a Branching Process
  • A database of flavivirus RNA structures with a search algorithm for pseudoknots and triple base interactions
  • Intellectual changes after radiation for children with brain tumors: which brain structures are most important?
  • Community-led, integrated, reproducible multi-omics with anvi’o
  • Multivariate Series and Diagonals
  • Psychodynamic Structures in Vietnamese Personality
  • Tertiary lymphoid structures: diversity in their development, composition, and role
  • Privacy Preserving Anti-forensic Techniques
  • A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems
  • A comparative analysis of coherent structures around a pile over rigid-bed and scoured-bottom
  • Introduction to Fail-Safe Technologies for Bonded Unitized Composite Structures
  • A method of predicting visual detectability of low-velocity impact damage in composite structures based on logistic regression model
  • Persistency of debris accumulation in tidal estuaries using Lagrangian coherent structures
  • Towards a definitive measure of repetitiveness
  • Characterisation of material and connection behaviour in sheathed cold-formed steel wall systems–Part 1: Experimentation and data compilation
  • Subsurface structures along western Yucatan from integrated geophysical analysis
  • Thermal Technical Analysis of Lightweight Timber-Based External Wall Structures with Ventilated Air Gap
  • A regression-based damage detection method for structures subjected to changing environmental and operational conditions
  • Reconstruction of highly porous structures from FIB-SEM using a deep neural network trained on synthetic images
  • Small-scale isotropy and ramp-cliff structures in scalar turbulence
  • Decidable entailments in separation logic with inductive definitions: Beyond establishment
  • Innovative Approach to Evaluate the Mechanical Performance of Ti–6Al–4V Lattice Structures Produced by Electron Beam Melting Process
  • An Overview of the Parallel Asynchronous Recursion Model
  • Introduction to P-TRAP software for designing phosphorus removal structures
  • Covid-19. bioreproducibility. org: A web resource for SARS-CoV-2-related structural models
  • Deductive and inductive approaches to qualitative data analysis
  • FieldView-CREATE: A Unified Multi-disciplinary Post-processing Tool for CREATE Solvers
  • A CUDA Fast Multipole Method with highly efficient M2L far field evaluation
  • Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms (SODA)
  • Structures spread across our seas
  • RepeatsDB in 2021: improved data and extended classification for protein tandem repeat structures
  • Dissipation Effects of Coastal Vegetation on Nearshore Structures under Wave Runup Loading
  • CRAN task view: Machine learning & statistical learning
  • Uncertainty handling in structural damage detection via non-probabilistic meta-models and interval mathematics, a data-analytics approach
  • Benchmark for Ab Initio Prediction of Magnetic Structures Based on Cluster-Multipole Theory
  • A convex penalty for block-sparse signals with unknown structures
  • Approximate Distance Oracles Subject to Multiple Vertex Failures
  • Guided wavefield curvature imaging of invisible damage in composite structures
  • Semi-supervised deep learning based framework for assessing manufacturability of cellular structures in direct metal laser sintering process
  • Scalable FastMDP for Pre-departure Airspace Reservation and Strategic De-conflict
  • Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
  • Composing qualitative process research
  • Modeling of Avionics Applications and Performance Evaluation Techniques using the Synchronous Language SIGNAL
  • 3D Printed Multi-material Medical Phantoms for Needle-tissue Interaction Modelling of Heterogeneous Structures
  • Architectures for Flexible Collaborative Systems
  • Krigings over space and time based on latent low-dimensional structures
  • Interoperable Application Programming Interfaces for Computer Aided Engineering Applications
  • Magnetic properties and crystal structures of two copper coordination compounds with pentafluorobenzoate ligand
  • Nearest neighbour distributions: New statistical measures for cosmological clustering
  • How Jupiter’s unusual magnetospheric topology structures its aurora
  • Analysis of the influence of different bionic structures on the noise reduction performance of the centrifugal pump
  • Benchmarking coordination number prediction algorithms on inorganic crystal structures
  • Collective sensing of evolving urban structures: from activity-based to content-aware social monitoring
  • Multistable inflatable origami structures at the metre scale
  • First-Order Error-Adapted Eigen Perturbation for Real-Time Modal Identification of Vibrating Structures
  • A half-salamo-based pyridine-containing ligand and its novel NiII complexes including different auxiliary ligands: syntheses, structures, fluorescence properties, DFT …
  • Novel Parallel-Programming Idioms
  • Reticular exploration of uranium-based metal—organic frameworks with hexacarboxylate building units
  • PubChem in 2021: new data content and improved web interfaces
  • New design of composite structures used in automotive engineering
  • A survey of Blockchain consensus algorithms: mechanism, design and applications
  • Search on a line by byzantine robots
  • Flow Structures on a Planar Food and Drug Administration (FDA) Nozzle at Low and Intermediate Reynolds Number
  • Juice: A julia package for logic and probabilistic circuits
  • First-principles study of electronic structures and elasticity of Al2Fe3Si3
  • Margin of Safety Assessment for Composite StructureS using the Onset Method
  • Finite element modeling for the progressive collapse analysis of steel stiffened-plate structures in fires
  • Nanoscale hyperspectral imaging of amyloid secondary structures in liquid
  • Phylogenetics of Indo-European language families via an algebro-geometric analysis of their syntactic structures
  • Seismic interaction of adjacent structures on liquefiable soils: insight from centrifuge and numerical modeling
  • Review of Wind Effects on Structures: Modern Structural Design for Wind, 4th Edition by Emil Simiu and DongHun Yeo
  • Preparation and characterization of novel cellular/nonporous foam structures derived from tannin furanic resin
  • Quasi-static and dynamic compressive behaviour of sheet TPMS cellular structures
  • A survey on deep learning in medicine: Why, how and when?
  • A new model for the growth of normal faults developed above pre-existing structures
  • Facile tailoring of structures for controlled release of paracetamol from sustainable lignin derived platforms
  • Continuous-time system identification with neural networks: model structures and fitting criteria
  • Nonstationary panel models with latent group structures and cross-section dependence
  • Multi-objective optimum design of truss structures using differential evolution algorithms
  • Bat and pangolin coronavirus spike glycoprotein structures provide insights into SARS-CoV-2 evolution
  • Crowdsourced behavior-driven development
  • Learning low-rank latent mesoscale structures in networks
  • Oscillatory Entrainment of the Frequency-following Response in Auditory Cortical and Subcortical Structures
  • Determining target reliability index of structures based on cost optimization and acceptance criteria for fatality risk
  • Geographic data science
  • Preliminary Documented Recovery Patterns and Observations from Video Cataloged Data of the 2011 Joplin, Missouri, Tornado
  • Kirigami Engineering—Nanoscale Structures Exhibiting a Range of Controllable 3D Configurations
  • Application of Thermoelectric Technology in Sustainable Pavement Structures
  • Cryo-EM structures of engineered active bc 1-cbb 3 type CIII 2 CIV super-complexes and electronic communication between the complexes
  • RASP: an atlas of transcriptome-wide RNA secondary structure probing data
  • Massively parallel transport sweeps on meshes with cyclic dependencies
  • Planning brain tumor resection using a probabilistic atlas of cortical and subcortical structures critical for functional processing: a proof of concept
  • Tracking areas with increased likelihood of surface particle aggregation in the Gulf of Finland: A first look at persistent Lagrangian Coherent Structures (LCS)
  • Load balancing and neural dynamic model to optimize replicator dynamics controllers for vibration reduction of highway bridge structures
  • Ensuring transparency and traceability of food local products: A blockchain application to a Smart Tourism Region
  • Local HI filaments driven by a small-scale dynamo. Unraveling velocities and tangling of dusty magnetized structures
  • Corporate environmental disclosure and earnings management—The moderating role of corporate governance structures
  • Distinct structures and dynamics of chromatosomes with different human linker histone isoforms
  • On Locating Paths in Compressed Tries
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Must read research papers on Data Structures

Data structures.

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Data Structures are not seen to be as important as Algorithms but in reality, it is equally important to solve computational problems efficiently. The must read research papers on Data Structures are:

  • Ordered Hash Table (1973)
  • Randomized Search Trees (1989)
  • EERTREE: An Efficient Data Structure for Processing Palindromes in Strings (2015)
  • Making data structures persistent (1986)
  • Design and implementation of an efficient priority queue (1976)
  • Fractional cascading: A data structuring technique (1986)

Note that the above list has been prepared by OpenGenus and is very accuracy. We will now go through each in detail:

Ordered Hash Table

Basic details on the paper:

  • Author: O. Amble and D. E. Knuth
  • Affiliation: University of Oslo and Stanford University
  • Date published: 1973
  • Journal/ Conference: The Computer Journal
  • Read this paper here: by Oxford Academia
  • Read about Hash Tables

Hash table is a fundamental progress as it shows how a simple data structure like array can be used to improve common operations like searching to constant time. Today, hash map has a central place in algorithms but a lot of ideas goes into it for efficiency like:

  • collision avoidance
  • hash generation

This is a must read for anyone interested in Computer Science and specially, Algorithms and Data Structures.

Randomized Search Trees

  • Author: Raimund Seidel and Cecilia Aragon
  • Affiliation: UC Berkeley
  • Date published: November 1989
  • Journal/ Conference: 30th Annual Symposium on Foundations of Computer Science
  • Read this paper here: Research Gate (PDF)
  • Read about Randomized Search Trees

Balanced Search trees are a solution to a number of problems but when it comes to reality, trees keep changing and keeping it balanced is a difficult process. This paper presents how balanced trees can be modified using randomness and opens up a whole new category of algorithms.

For will need to understand Binary Search Tree to get the complete idea of this.

EERTREE: An Efficient Data Structure for Processing Palindromes in Strings

  • Author: Mikhail Rubinchik and Arseny M. Shur
  • Affiliation: Ural Federal University, Ekaterinburg, Russia
  • Date published: June 2015
  • Journal/ Conference: European Journal of Combinatorics
  • Read this paper here: ArXiv
  • Read about EerTree

This is a must read because this data structure shows that some problems like string related problems may seem to be pruely algorithmic problems but a clever use of a data structure can improve the performance significantly.

This paper bridges the gap between algorithms and data structures with respect to strings.

Making data structures persistent

  • Author: Driscoll, J.R., Sarnak, N., Sleator, D.D., Tarjan, R.E
  • Affiliation: Carnegie-Mellon University, IBM, AT&T Bell Laboratories, Princeton University
  • Date published: 5th August 1986
  • Journal/ Conference: Journal of Computer and System Sciences
  • Read this paper here: ACM
  • Read about Persistent Segment Tree

Usually, data structures need not remember previous structures but as we went on to solve complex problems in time domain, need of persistent data structures that is data structures maintaining its previous structures rose. This paper is a must read as it shows what it means for a data structure to be persistent and how we can do so.

This opens up the path to a whole new domain of persistent data structures.

Design and implementation of an efficient priority queue

  • Author: P. van Emde Boas, R. KaasE. Zijlstra
  • Affiliation: Mathematical Centre, Amsterdam, Netherlands
  • Date published: December 1976
  • Journal/ Conference: Mathematical systems theory
  • Read this paper here: Springer
  • Read about Priority Queue

This is an old paper but is a good read as it shows how a data structure should be implemented depending on the system architecture and programming language used to get the most performance out of it.

This will open up your mind to look at algorithms and data structures differently.

Fractional cascading: A data structuring technique

  • Author: Bernard Chazelle, Leonidas J. Guibas
  • Affiliation: Brown University, Ecole Normale Supérieure, DEC/SRC and Stanford University, USA
  • Date published: November 1986
  • Journal/ Conference: Algorithmica

This is an important paper as it shows how particular data structure can perform less for the first few tries and then, show superior performance as it gets warmed up. This is a must read to understand the true potential of data structures.

Learn more about Data Structures

With this, you will have a good understanding of the wide variety of data structures and what can be done using them.

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37 Research Topics In Data Science To Stay On Top Of

Stewart Kaplan

  • February 22, 2024

As a data scientist, staying on top of the latest research in your field is essential.

The data science landscape changes rapidly, and new techniques and tools are constantly being developed.

To keep up with the competition, you need to be aware of the latest trends and topics in data science research.

In this article, we will provide an overview of 37 hot research topics in data science.

We will discuss each topic in detail, including its significance and potential applications.

These topics could be an idea for a thesis or simply topics you can research independently.

Stay tuned – this is one blog post you don’t want to miss!

37 Research Topics in Data Science

1.) predictive modeling.

Predictive modeling is a significant portion of data science and a topic you must be aware of.

Simply put, it is the process of using historical data to build models that can predict future outcomes.

Predictive modeling has many applications, from marketing and sales to financial forecasting and risk management.

As businesses increasingly rely on data to make decisions, predictive modeling is becoming more and more important.

While it can be complex, predictive modeling is a powerful tool that gives businesses a competitive advantage.

predictive modeling

2.) Big Data Analytics

These days, it seems like everyone is talking about big data.

And with good reason – organizations of all sizes are sitting on mountains of data, and they’re increasingly turning to data scientists to help them make sense of it all.

But what exactly is big data? And what does it mean for data science?

Simply put, big data is a term used to describe datasets that are too large and complex for traditional data processing techniques.

Big data typically refers to datasets of a few terabytes or more.

But size isn’t the only defining characteristic – big data is also characterized by its high Velocity (the speed at which data is generated), Variety (the different types of data), and Volume (the amount of the information).

Given the enormity of big data, it’s not surprising that organizations are struggling to make sense of it all.

That’s where data science comes in.

Data scientists use various methods to wrangle big data, including distributed computing and other decentralized technologies.

With the help of data science, organizations are beginning to unlock the hidden value in their big data.

By harnessing the power of big data analytics, they can improve their decision-making, better understand their customers, and develop new products and services.

3.) Auto Machine Learning

Auto machine learning is a research topic in data science concerned with developing algorithms that can automatically learn from data without intervention.

This area of research is vital because it allows data scientists to automate the process of writing code for every dataset.

This allows us to focus on other tasks, such as model selection and validation.

Auto machine learning algorithms can learn from data in a hands-off way for the data scientist – while still providing incredible insights.

This makes them a valuable tool for data scientists who either don’t have the skills to do their own analysis or are struggling.

Auto Machine Learning

4.) Text Mining

Text mining is a research topic in data science that deals with text data extraction.

This area of research is important because it allows us to get as much information as possible from the vast amount of text data available today.

Text mining techniques can extract information from text data, such as keywords, sentiments, and relationships.

This information can be used for various purposes, such as model building and predictive analytics.

5.) Natural Language Processing

Natural language processing is a data science research topic that analyzes human language data.

This area of research is important because it allows us to understand and make sense of the vast amount of text data available today.

Natural language processing techniques can build predictive and interactive models from any language data.

Natural Language processing is pretty broad, and recent advances like GPT-3 have pushed this topic to the forefront.

natural language processing

6.) Recommender Systems

Recommender systems are an exciting topic in data science because they allow us to make better products, services, and content recommendations.

Businesses can better understand their customers and their needs by using recommender systems.

This, in turn, allows them to develop better products and services that meet the needs of their customers.

Recommender systems are also used to recommend content to users.

This can be done on an individual level or at a group level.

Think about Netflix, for example, always knowing what you want to watch!

Recommender systems are a valuable tool for businesses and users alike.

7.) Deep Learning

Deep learning is a research topic in data science that deals with artificial neural networks.

These networks are composed of multiple layers, and each layer is formed from various nodes.

Deep learning networks can learn from data similarly to how humans learn, irrespective of the data distribution.

This makes them a valuable tool for data scientists looking to build models that can learn from data independently.

The deep learning network has become very popular in recent years because of its ability to achieve state-of-the-art results on various tasks.

There seems to be a new SOTA deep learning algorithm research paper on  https://arxiv.org/  every single day!

deep learning

8.) Reinforcement Learning

Reinforcement learning is a research topic in data science that deals with algorithms that can learn on multiple levels from interactions with their environment.

This area of research is essential because it allows us to develop algorithms that can learn non-greedy approaches to decision-making, allowing businesses and companies to win in the long term compared to the short.

9.) Data Visualization

Data visualization is an excellent research topic in data science because it allows us to see our data in a way that is easy to understand.

Data visualization techniques can be used to create charts, graphs, and other visual representations of data.

This allows us to see the patterns and trends hidden in our data.

Data visualization is also used to communicate results to others.

This allows us to share our findings with others in a way that is easy to understand.

There are many ways to contribute to and learn about data visualization.

Some ways include attending conferences, reading papers, and contributing to open-source projects.

data visualization

10.) Predictive Maintenance

Predictive maintenance is a hot topic in data science because it allows us to prevent failures before they happen.

This is done using data analytics to predict when a failure will occur.

This allows us to take corrective action before the failure actually happens.

While this sounds simple, avoiding false positives while keeping recall is challenging and an area wide open for advancement.

11.) Financial Analysis

Financial analysis is an older topic that has been around for a while but is still a great field where contributions can be felt.

Current researchers are focused on analyzing macroeconomic data to make better financial decisions.

This is done by analyzing the data to identify trends and patterns.

Financial analysts can use this information to make informed decisions about where to invest their money.

Financial analysis is also used to predict future economic trends.

This allows businesses and individuals to prepare for potential financial hardships and enable companies to be cash-heavy during good economic conditions.

Overall, financial analysis is a valuable tool for anyone looking to make better financial decisions.

Financial Analysis

12.) Image Recognition

Image recognition is one of the hottest topics in data science because it allows us to identify objects in images.

This is done using artificial intelligence algorithms that can learn from data and understand what objects you’re looking for.

This allows us to build models that can accurately recognize objects in images and video.

This is a valuable tool for businesses and individuals who want to be able to identify objects in images.

Think about security, identification, routing, traffic, etc.

Image Recognition has gained a ton of momentum recently – for a good reason.

13.) Fraud Detection

Fraud detection is a great topic in data science because it allows us to identify fraudulent activity before it happens.

This is done by analyzing data to look for patterns and trends that may be associated with the fraud.

Once our machine learning model recognizes some of these patterns in real time, it immediately detects fraud.

This allows us to take corrective action before the fraud actually happens.

Fraud detection is a valuable tool for anyone who wants to protect themselves from potential fraudulent activity.

fraud detection

14.) Web Scraping

Web scraping is a controversial topic in data science because it allows us to collect data from the web, which is usually data you do not own.

This is done by extracting data from websites using scraping tools that are usually custom-programmed.

This allows us to collect data that would otherwise be inaccessible.

For obvious reasons, web scraping is a unique tool – giving you data your competitors would have no chance of getting.

I think there is an excellent opportunity to create new and innovative ways to make scraping accessible for everyone, not just those who understand Selenium and Beautiful Soup.

15.) Social Media Analysis

Social media analysis is not new; many people have already created exciting and innovative algorithms to study this.

However, it is still a great data science research topic because it allows us to understand how people interact on social media.

This is done by analyzing data from social media platforms to look for insights, bots, and recent societal trends.

Once we understand these practices, we can use this information to improve our marketing efforts.

For example, if we know that a particular demographic prefers a specific type of content, we can create more content that appeals to them.

Social media analysis is also used to understand how people interact with brands on social media.

This allows businesses to understand better what their customers want and need.

Overall, social media analysis is valuable for anyone who wants to improve their marketing efforts or understand how customers interact with brands.

social media

16.) GPU Computing

GPU computing is a fun new research topic in data science because it allows us to process data much faster than traditional CPUs .

Due to how GPUs are made, they’re incredibly proficient at intense matrix operations, outperforming traditional CPUs by very high margins.

While the computation is fast, the coding is still tricky.

There is an excellent research opportunity to bring these innovations to non-traditional modules, allowing data science to take advantage of GPU computing outside of deep learning.

17.) Quantum Computing

Quantum computing is a new research topic in data science and physics because it allows us to process data much faster than traditional computers.

It also opens the door to new types of data.

There are just some problems that can’t be solved utilizing outside of the classical computer.

For example, if you wanted to understand how a single atom moved around, a classical computer couldn’t handle this problem.

You’ll need to utilize a quantum computer to handle quantum mechanics problems.

This may be the “hottest” research topic on the planet right now, with some of the top researchers in computer science and physics worldwide working on it.

You could be too.

quantum computing

18.) Genomics

Genomics may be the only research topic that can compete with quantum computing regarding the “number of top researchers working on it.”

Genomics is a fantastic intersection of data science because it allows us to understand how genes work.

This is done by sequencing the DNA of different organisms to look for insights into our and other species.

Once we understand these patterns, we can use this information to improve our understanding of diseases and create new and innovative treatments for them.

Genomics is also used to study the evolution of different species.

Genomics is the future and a field begging for new and exciting research professionals to take it to the next step.

19.) Location-based services

Location-based services are an old and time-tested research topic in data science.

Since GPS and 4g cell phone reception became a thing, we’ve been trying to stay informed about how humans interact with their environment.

This is done by analyzing data from GPS tracking devices, cell phone towers, and Wi-Fi routers to look for insights into how humans interact.

Once we understand these practices, we can use this information to improve our geotargeting efforts, improve maps, find faster routes, and improve cohesion throughout a community.

Location-based services are used to understand the user, something every business could always use a little bit more of.

While a seemingly “stale” field, location-based services have seen a revival period with self-driving cars.


20.) Smart City Applications

Smart city applications are all the rage in data science research right now.

By harnessing the power of data, cities can become more efficient and sustainable.

But what exactly are smart city applications?

In short, they are systems that use data to improve city infrastructure and services.

This can include anything from traffic management and energy use to waste management and public safety.

Data is collected from various sources, including sensors, cameras, and social media.

It is then analyzed to identify tendencies and habits.

This information can make predictions about future needs and optimize city resources.

As more and more cities strive to become “smart,” the demand for data scientists with expertise in smart city applications is only growing.

21.) Internet Of Things (IoT)

The Internet of Things, or IoT, is exciting and new data science and sustainability research topic.

IoT is a network of physical objects embedded with sensors and connected to the internet.

These objects can include everything from alarm clocks to refrigerators; they’re all connected to the internet.

That means that they can share data with computers.

And that’s where data science comes in.

Data scientists are using IoT data to learn everything from how people use energy to how traffic flows through a city.

They’re also using IoT data to predict when an appliance will break down or when a road will be congested.

Really, the possibilities are endless.

With such a wide-open field, it’s easy to see why IoT is being researched by some of the top professionals in the world.

internet of things

22.) Cybersecurity

Cybersecurity is a relatively new research topic in data science and in general, but it’s already garnering a lot of attention from businesses and organizations.

After all, with the increasing number of cyber attacks in recent years, it’s clear that we need to find better ways to protect our data.

While most of cybersecurity focuses on infrastructure, data scientists can leverage historical events to find potential exploits to protect their companies.

Sometimes, looking at a problem from a different angle helps, and that’s what data science brings to cybersecurity.

Also, data science can help to develop new security technologies and protocols.

As a result, cybersecurity is a crucial data science research area and one that will only become more important in the years to come.

23.) Blockchain

Blockchain is an incredible new research topic in data science for several reasons.

First, it is a distributed database technology that enables secure, transparent, and tamper-proof transactions.

Did someone say transmitting data?

This makes it an ideal platform for tracking data and transactions in various industries.

Second, blockchain is powered by cryptography, which not only makes it highly secure – but is a familiar foe for data scientists.

Finally, blockchain is still in its early stages of development, so there is much room for research and innovation.

As a result, blockchain is a great new research topic in data science that vows to revolutionize how we store, transmit and manage data.


24.) Sustainability

Sustainability is a relatively new research topic in data science, but it is gaining traction quickly.

To keep up with this demand, The Wharton School of the University of Pennsylvania has  started to offer an MBA in Sustainability .

This demand isn’t shocking, and some of the reasons include the following:

Sustainability is an important issue that is relevant to everyone.

Datasets on sustainability are constantly growing and changing, making it an exciting challenge for data scientists.

There hasn’t been a “set way” to approach sustainability from a data perspective, making it an excellent opportunity for interdisciplinary research.

As data science grows, sustainability will likely become an increasingly important research topic.

25.) Educational Data

Education has always been a great topic for research, and with the advent of big data, educational data has become an even richer source of information.

By studying educational data, researchers can gain insights into how students learn, what motivates them, and what barriers these students may face.

Besides, data science can be used to develop educational interventions tailored to individual students’ needs.

Imagine being the researcher that helps that high schooler pass mathematics; what an incredible feeling.

With the increasing availability of educational data, data science has enormous potential to improve the quality of education.

online education

26.) Politics

As data science continues to evolve, so does the scope of its applications.

Originally used primarily for business intelligence and marketing, data science is now applied to various fields, including politics.

By analyzing large data sets, political scientists (data scientists with a cooler name) can gain valuable insights into voting patterns, campaign strategies, and more.

Further, data science can be used to forecast election results and understand the effects of political events on public opinion.

With the wealth of data available, there is no shortage of research opportunities in this field.

As data science evolves, so does our understanding of politics and its role in our world.

27.) Cloud Technologies

Cloud technologies are a great research topic.

It allows for the outsourcing and sharing of computer resources and applications all over the internet.

This lets organizations save money on hardware and maintenance costs while providing employees access to the latest and greatest software and applications.

I believe there is an argument that AWS could be the greatest and most technologically advanced business ever built (Yes, I know it’s only part of the company).

Besides, cloud technologies can help improve team members’ collaboration by allowing them to share files and work on projects together in real-time.

As more businesses adopt cloud technologies, data scientists must stay up-to-date on the latest trends in this area.

By researching cloud technologies, data scientists can help organizations to make the most of this new and exciting technology.

cloud technologies

28.) Robotics

Robotics has recently become a household name, and it’s for a good reason.

First, robotics deals with controlling and planning physical systems, an inherently complex problem.

Second, robotics requires various sensors and actuators to interact with the world, making it an ideal application for machine learning techniques.

Finally, robotics is an interdisciplinary field that draws on various disciplines, such as computer science, mechanical engineering, and electrical engineering.

As a result, robotics is a rich source of research problems for data scientists.

29.) HealthCare

Healthcare is an industry that is ripe for data-driven innovation.

Hospitals, clinics, and health insurance companies generate a tremendous amount of data daily.

This data can be used to improve the quality of care and outcomes for patients.

This is perfect timing, as the healthcare industry is undergoing a significant shift towards value-based care, which means there is a greater need than ever for data-driven decision-making.

As a result, healthcare is an exciting new research topic for data scientists.

There are many different ways in which data can be used to improve healthcare, and there is a ton of room for newcomers to make discoveries.


30.) Remote Work

There’s no doubt that remote work is on the rise.

In today’s global economy, more and more businesses are allowing their employees to work from home or anywhere else they can get a stable internet connection.

But what does this mean for data science? Well, for one thing, it opens up a whole new field of research.

For example, how does remote work impact employee productivity?

What are the best ways to manage and collaborate on data science projects when team members are spread across the globe?

And what are the cybersecurity risks associated with working remotely?

These are just a few of the questions that data scientists will be able to answer with further research.

So if you’re looking for a new topic to sink your teeth into, remote work in data science is a great option.

31.) Data-Driven Journalism

Data-driven journalism is an exciting new field of research that combines the best of both worlds: the rigor of data science with the creativity of journalism.

By applying data analytics to large datasets, journalists can uncover stories that would otherwise be hidden.

And telling these stories compellingly can help people better understand the world around them.

Data-driven journalism is still in its infancy, but it has already had a major impact on how news is reported.

In the future, it will only become more important as data becomes increasingly fluid among journalists.

It is an exciting new topic and research field for data scientists to explore.


32.) Data Engineering

Data engineering is a staple in data science, focusing on efficiently managing data.

Data engineers are responsible for developing and maintaining the systems that collect, process, and store data.

In recent years, there has been an increasing demand for data engineers as the volume of data generated by businesses and organizations has grown exponentially.

Data engineers must be able to design and implement efficient data-processing pipelines and have the skills to optimize and troubleshoot existing systems.

If you are looking for a challenging research topic that would immediately impact you worldwide, then improving or innovating a new approach in data engineering would be a good start.

33.) Data Curation

Data curation has been a hot topic in the data science community for some time now.

Curating data involves organizing, managing, and preserving data so researchers can use it.

Data curation can help to ensure that data is accurate, reliable, and accessible.

It can also help to prevent research duplication and to facilitate the sharing of data between researchers.

Data curation is a vital part of data science. In recent years, there has been an increasing focus on data curation, as it has become clear that it is essential for ensuring data quality.

As a result, data curation is now a major research topic in data science.

There are numerous books and articles on the subject, and many universities offer courses on data curation.

Data curation is an integral part of data science and will only become more important in the future.


34.) Meta-Learning

Meta-learning is gaining a ton of steam in data science. It’s learning how to learn.

So, if you can learn how to learn, you can learn anything much faster.

Meta-learning is mainly used in deep learning, as applications outside of this are generally pretty hard.

In deep learning, many parameters need to be tuned for a good model, and there’s usually a lot of data.

You can save time and effort if you can automatically and quickly do this tuning.

In machine learning, meta-learning can improve models’ performance by sharing knowledge between different models.

For example, if you have a bunch of different models that all solve the same problem, then you can use meta-learning to share the knowledge between them to improve the cluster (groups) overall performance.

I don’t know how anyone looking for a research topic could stay away from this field; it’s what the  Terminator  warned us about!

35.) Data Warehousing

A data warehouse is a system used for data analysis and reporting.

It is a central data repository created by combining data from multiple sources.

Data warehouses are often used to store historical data, such as sales data, financial data, and customer data.

This data type can be used to create reports and perform statistical analysis.

Data warehouses also store data that the organization is not currently using.

This type of data can be used for future research projects.

Data warehousing is an incredible research topic in data science because it offers a variety of benefits.

Data warehouses help organizations to save time and money by reducing the need for manual data entry.

They also help to improve the accuracy of reports and provide a complete picture of the organization’s performance.

Data warehousing feels like one of the weakest parts of the Data Science Technology Stack; if you want a research topic that could have a monumental impact – data warehousing is an excellent place to look.

data warehousing

36.) Business Intelligence

Business intelligence aims to collect, process, and analyze data to help businesses make better decisions.

Business intelligence can improve marketing, sales, customer service, and operations.

It can also be used to identify new business opportunities and track competition.

BI is business and another tool in your company’s toolbox to continue dominating your area.

Data science is the perfect tool for business intelligence because it combines statistics, computer science, and machine learning.

Data scientists can use business intelligence to answer questions like, “What are our customers buying?” or “What are our competitors doing?” or “How can we increase sales?”

Business intelligence is a great way to improve your business’s bottom line and an excellent opportunity to dive deep into a well-respected research topic.

37.) Crowdsourcing

One of the newest areas of research in data science is crowdsourcing.

Crowdsourcing is a process of sourcing tasks or projects to a large group of people, typically via the internet.

This can be done for various purposes, such as gathering data, developing new algorithms, or even just for fun (think: online quizzes and surveys).

But what makes crowdsourcing so powerful is that it allows businesses and organizations to tap into a vast pool of talent and resources they wouldn’t otherwise have access to.

And with the rise of social media, it’s easier than ever to connect with potential crowdsource workers worldwide.

Imagine if you could effect that, finding innovative ways to improve how people work together.

That would have a huge effect.

crowd sourcing

Final Thoughts, Are These Research Topics In Data Science For You?

Thirty-seven different research topics in data science are a lot to take in, but we hope you found a research topic that interests you.

If not, don’t worry – there are plenty of other great topics to explore.

The important thing is to get started with your research and find ways to apply what you learn to real-world problems.

We wish you the best of luck as you begin your data science journey!

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Home » Research Paper – Structure, Examples and Writing Guide

Research Paper – Structure, Examples and Writing Guide

Table of Contents

Research Paper

Research Paper


Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.


The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

  • Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
  • Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
  • Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
  • Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
  • Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
  • Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
  • Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
  • Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
  • Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

  • Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
  • Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
  • Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

  • How often do you use social media per day?
  • Less than 30 minutes
  • 30 minutes to 1 hour
  • 1 to 2 hours
  • 2 to 4 hours
  • More than 4 hours
  • Which social media platforms do you use?
  • Others (Please specify)
  • How often do you experience the following on social media?
  • Social comparison (comparing yourself to others)
  • Cyberbullying
  • Fear of Missing Out (FOMO)
  • Have you ever experienced any of the following mental health problems in the past month?
  • Do you think social media use has a positive or negative impact on your mental health?
  • Very positive
  • Somewhat positive
  • Somewhat negative
  • Very negative
  • In your opinion, which factors contribute to the negative impact of social media on mental health?
  • Social comparison
  • In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
  • Education on healthy social media use
  • Counseling for mental health problems caused by social media
  • Social media detox programs
  • Regulation of social media use

Thank you for your participation!

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Cross-correlation difference matrix based structural damage detection approach for building structures

  • Original Paper
  • Published: 12 April 2024

Cite this article

  • Soraj Kumar Panigrahi   ORCID: orcid.org/0000-0002-8927-8302 1 ,
  • Chandrabhan Patel 1 ,
  • Ajay Chourasia 1 &
  • Ravindra Singh Bisht 1  

Damages to various building structures often occur over their service life and can occasionally lead to severe structural failures, threatening the lives of its residents. In recent years, special attention has been paid to investigating various damages in buildings at the early stage to avoid failures and thereby minimize maintenance. Structural health monitoring can be used as a tool for damage quantification using vibration measurements. The application of various sensors for measuring accelerations, velocity and displacement in civil infrastructure monitoring has a long history in vibration-based approaches. These types of sensors reveal dynamic characteristics which are global in nature and ineffective in case of minor damage identification. In a practical application, the available damage detection approaches are not fully capable of quickly sensing and accurately identifying the realistic damage in structures. Research on damage identification from strain data is an interesting topic in recent days. Some work on the cross-correlation approach is now a centre of attraction and strictly confined to bridge or symmetric structures. The present paper uses strain data to validate the cross-correlation approach for detecting damage to building structures. The effectiveness of the methodology has been illustrated firstly on a simply supported beam, then on a 5-storey steel frame and a 6-storey scaled-down reinforced concrete shear building and lastly on a frame structure with moving load as a special case. The results show that this approach has the potential to identify damages in different kinds of civil infrastructure.

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Data availability

The authors confirm that the data supporting the findings of this study are available within the article. The raw data that support the findings of this study are available upon a reasonable request.

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This work was supported by the Council of Scientific & Industrial Research (CSIR), India. The authors would like to thank Director, CBRI Roorkee for giving permission for publishing the paper. The authors also thank Mr. Naman Garg, Mr. Sameer Yadav and Mr. Dinesh Kumar for their support in conducting the experiments.

This study was funded by the Council of Scientific & Industrial Research, India and Central Building Research Institute Roorkee, India.

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Panigrahi, S.K., Patel, C., Chourasia, A. et al. Cross-correlation difference matrix based structural damage detection approach for building structures. J Civil Struct Health Monit (2024). https://doi.org/10.1007/s13349-024-00781-1

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Regions & Countries

What public k-12 teachers want americans to know about teaching.

Illustrations by Hokyoung Kim

data structures research paper topics

At a time when most teachers are feeling stressed and overwhelmed in their jobs, we asked 2,531 public K-12 teachers this open-ended question:

If there’s one thing you’d want the public to know about teachers, what would it be?

We also asked Americans what they think about teachers to compare with teachers’ perceptions of how the public views them.

Related: What’s It Like To Be a Teacher in America Today?

A bar chart showing that about half of teachers want the public to know that teaching is a hard job.

Pew Research Center conducted this analysis to better understand what public K-12 teachers would like Americans to know about their profession. We also wanted to learn how the public thinks about teachers.

For the open-end question, we surveyed 2,531 U.S. public K-12 teachers from Oct. 17 to Nov. 14, 2023. The teachers surveyed are members of RAND’s American Teacher Panel, a nationally representative panel of public K-12 school teachers recruited through MDR Education. Survey data is weighted to state and national teacher characteristics to account for differences in sampling and response to ensure they are representative of the target population.

Overall, 96% of surveyed teachers provided an answer to the open-ended question. Center researchers developed a coding scheme categorizing the responses, coded all responses, and then grouped them into the six themes explored in the data essay.

For the questions for the general public, we surveyed 5,029 U.S. adults from Nov. 9 to Nov. 16, 2023. The adults surveyed are members of the Ipsos KnowledgePanel, a nationally representative online survey panel. Panel members are randomly recruited through probability-based sampling, and households are provided with access to the Internet and hardware if needed. To ensure that the results of this survey reflect a balanced cross section of the nation, the data is weighted to match the U.S. adult population by gender, age, education, race and ethnicity and other categories.

Here are the questions used for this analysis , along with responses, the teacher survey methodology and the general public survey methodology .

Most of the responses to the open-ended question fell into one of these six themes:

Teaching is a hard job

About half of teachers (51%) said they want the public to know that teaching is a difficult job and that teachers are hardworking. Within this share, many mentioned that they have roles and responsibilities in the classroom besides teaching, which makes the job stressful. Many also talked about working long hours, beyond those they’re contracted for.

“Teachers serve multiple roles other than being responsible for teaching curriculum. We are counselors, behavioral specialists and parents for students who need us to fill those roles. We sacrifice a lot to give all of ourselves to the role as teacher.”

– Elementary school teacher

“The amount of extra hours that teachers have to put in beyond the contractual time is ridiculous. Arriving 30 minutes before and leaving an hour after is just the tip of the iceberg. … And as far as ‘having summers off,’ most of August is taken up with preparing materials for the upcoming school year or attending three, four, seven days’ worth of unpaid development training.”

– High school teacher

Teachers care about their students

The next most common theme: 22% of teachers brought up how fulfilling teaching is and how much teachers care about their students. Many gave examples of the hardships of teaching but reaffirmed that they do their job because they love the kids and helping them succeed. 

data structures research paper topics

“We are passionate about what we do. Every child we teach is important to us and we look out for them like they are our own.”

– Middle school teacher

“We are in it for the kids, and the most incredible moments are when children make connections with learning.”

Teachers are undervalued and disrespected

Some 17% of teachers want the public to know that they feel undervalued and disrespected, and that they need more public support. Some mentioned that they are well-educated professionals but are not treated as such. And many teachers in this category responded with a general plea for support from the public, which they don’t feel they’re getting now.

“We feel undervalued. The public and many parents of my students treat me and my peers as if we do not know as much as they do, as if we are uneducated.”

“The public attitudes toward teachers have been degrading, and it is making it impossible for well-qualified teachers to be found. People are simply not wanting to go into the profession because of public sentiments.”

Teachers are underpaid

A similar share of teachers (15%) want the public to know that teachers are underpaid. Many teachers said their salary doesn’t account for the effort and care they put into their students’ education and believe that their pay should reflect this.

data structures research paper topics

“We are sorely underpaid for the amount of hours we work and the education level we have attained.”

Teachers need support and resources from government and administrators

About one-in-ten teachers (9%) said they need more support from the government, their administrators and other key stakeholders. Many mentioned working in understaffed schools, not having enough funding and paying for supplies out of pocket. Some teachers also expressed that they have little control over the curriculum that they teach.

“The world-class education we used to be proud of does not exist because of all the red tape we are constantly navigating. If you want to see real change in the classroom, advocate for smaller class sizes for your child, push your district to cap class sizes at a reasonable level and have real, authentic conversations with your child’s teacher about what is going on in the classroom if you’re curious.”

Teachers need more support from parents

Roughly the same share of teachers (8%) want the public to know that teachers need more support from parents, emphasizing that the parent-teacher relationship is strained. Many view parents as partners in their child’s education and believe that a strong relationship improves kids’ overall social and emotional development.

data structures research paper topics

“Teachers help students to reach their potential. However, that job is near impossible if parents/guardians do not take an active part in their student’s education.”

How the U.S. public views teachers

While the top response from teachers in the open-ended question is that they want the public to know that teaching is a hard job, most Americans already see it that way. Two-thirds of U.S. adults say being a public K-12 teacher is harder than most other jobs, with 33% saying it’s a lot harder.

And about three-quarters of Americans (74%) say teachers should be paid more than they are now, including 39% who say teachers should be paid a lot more.

data structures research paper topics

Americans are about evenly divided on whether the public generally looks up to (32%) or down on (30%) public K-12 teachers. Some 37% say Americans neither look up to or down on public K-12 teachers.

A bar chart showing that teachers’ perceptions of how much Americans trust public K-12 teachers to do their job well is more negative than the general public’s response.

In addition to the open-ended question about what they want the public to know about them, we asked teachers how much they think most Americans trust public K-12 teachers to do their job well. We also asked the public how much they trust teachers. Answers differ considerably.

Nearly half of public K-12 teachers (47%) say most Americans don’t trust teachers much or at all. A third say most Americans trust teachers some, and 18% say the public trusts teachers a great deal or a fair amount.

In contrast, a majority of Americans (57%) say they do trust public K-12 teachers to do their job well a great deal or a fair amount. About a quarter (26%) say they trust teachers some, and 17% say they don’t trust teachers much or at all.

Related: About half of Americans say public K-12 education is going in the wrong direction

How the public’s views differ by party

There are sizable party differences in Americans’ views of teachers. In particular, Democrats and Democratic-leaning independents are more likely than Republicans and Republican leaners to say:

  • They trust teachers to do their job well a great deal or a fair amount (70% vs. 44%)
  • Teaching is a lot or somewhat harder when compared with most other jobs (77% vs. 59%)
  • Teachers should be paid a lot or somewhat more than they are now (86% vs. 63%)

data structures research paper topics

In their own words

Below, we have a selection of quotes that describe what teachers want the public to know about them and their profession.

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About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .


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  1. Data Structures and Algorithms authors/titles recent submissions

    Title: Efficient Distributed Data Structures for Future Many-core Architectures Authors: Panagiota Fatourou , Nikolaos D. Kallimanis , Eleni Kanellou , Odysseas Makridakis , Christi Symeonidou

  2. data structure Latest Research Papers

    Our compressed structure allows for directed and undirected graphs, faster arc and neighborhood queries, as well as the ability for arcs and frames to be added and removed directly from the compressed structure (streaming operations). We use publicly available network data sets such as Flickr, Yahoo!, and Wikipedia in our experiments and show ...

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    Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

  5. (PDF) Data Structure: Theoretical Approach

    Data structures are generally based on the abilit y of a. computer to fetch and store data at any pla ce in its. memory, specified by a pointer — a bit string, representing a memory address ...

  6. Algorithms

    Special Issue Information. Dear Colleagues, Machine learning is the study of computer algorithms that allow computer programs to improve automatically through experience. Machine learning algorithms build a model based on training data to make predictions or decisions without being explicitly programmed to do so.

  7. Algorithms and Data Structures for New Models of Computation

    In the early days of computer science, the community settled on a simple standard model of computing and a basic canon of general purpose algorithms and data structures suited to that model. With isochronous computing, heterogeneous multiprocessors, flash memory, energy-aware computing, cache and other anisotropic memory, distributed computing, streaming environments, functional languages ...


    PDF | This book contains the following chapters: Chapter 1: Introduction Chapter 2: Data Structures And Algorithms Chapter 3: Data Structures And Its... | Find, read and cite all the research you ...

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    The task is to construct a data structure over T answering the following type of online queries efficiently. Given a range [α,β], return a shortest substring T [i,j] of T with exactly one occurrence in [α,β]. We present an O (nlogn)-word data structure with O (logwn) query time, where w=Ω (logn) is the word size.

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    The tree data structure allows an efficient mapping of a huge amount of data and small changes made to the data can be easily identified. ... (Punjab), India. He has published more than 400 technical research papers in top-cited journals such as IEEE TKDE, IEEE TIE, IEEE TDSC, IEEE TITS, IEEE TCE, IEEE TII, IEEE TVT, IEEE ITS, IEEE SG, IEEE ...

  11. Research Topics & Ideas: Data Science

    Data Science-Related Research Topics. Developing machine learning models for real-time fraud detection in online transactions. The use of big data analytics in predicting and managing urban traffic flow. Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.

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    Data Structures Research Topics for MS and Ph.D Thesis. Data structures and algorithm analysis. On MATLAB experience in accelerating DIRECT-GLce algorithm for constrained global optimization through dynamic data structures and parallelization. New data structures for orthogonal range reporting and range minima queries.

  13. Must read research papers on Data Structures

    Data Structures are not seen to be as important as Algorithms but in reality, it is equally important to solve computational problems efficiently. The must read research papers on Data Structures are: Ordered Hash Table (1973) Randomized Search Trees (1989) EERTREE: An Efficient Data Structure for Processing Palindromes in Strings (2015)

  14. Recent Studies About Teaching Algorithms (CS1) and Data Structures (CS2

    This Research Full Paper presents a review of recent studies on SIGCSE about teaching programming (CS1) and data structures (CS2) for university students in computer science courses. Our main contribution is the identification of three categories and their respective subcategories for teaching programming: (i) characterization of contents, (ii) identification of pedagogical strategies and (iii ...

  15. PDF A Survey Paper on Data Structure and Algorithm Visualization

    and teachers visualize data structures and algorithms with their real-life implementation. Keywords: Data Structures, Algorithms, Visualization, Real-Life Implementation. I. INTRODUCTION Data structures and algorithms play a major role in Computer Science and also help people to get hired. Data structure and Algorithms are the foundation.

  16. (PDF) Data Structures and Algorithms (DSA)

    Data Structures and Algorithms (DSA) July 2019. ISBN: 978-9957-67-315-5. Authors: Nada M. Al Hakkak. Baghdad College of Economic Sciences University. Content uploaded by Nada M. Al Hakkak. Author ...

  17. 37 Research Topics In Data Science To Stay On Top Of » EML

    9.) Data Visualization. Data visualization is an excellent research topic in data science because it allows us to see our data in a way that is easy to understand. Data visualization techniques can be used to create charts, graphs, and other visual representations of data.

  18. 55 Research Paper Topics to Jump-Start Your Paper

    55 Research Paper Topics to Jump-Start Your Paper. Matt Ellis. Updated on October 9, 2023 Students. Coming up with research paper topics is the first step in writing most papers. While it may seem easy compared to the actual writing, choosing the right research paper topic is nonetheless one of the most important steps.

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  20. Cross-correlation difference matrix based structural damage ...

    Research on damage identification from strain data is an interesting topic in recent days. Some work on the cross-correlation approach is now a centre of attraction and strictly confined to bridge or symmetric structures. The present paper uses strain data to validate the cross-correlation approach for detecting damage to building structures.

  21. 6 facts about Americans and TikTok

    Here are six key facts about Americans and TikTok, drawn from Pew Research Center surveys. A third of U.S. adults - including a majority of adults under 30 - use TikTok. Around six-in-ten U.S. adults under 30 (62%) say they use TikTok, compared with 39% of those ages 30 to 49, 24% of those 50 to 64, and 10% of those 65 and older. In a 2023 ...

  22. Families & Living Arrangements Working Papers

    Working papers are intended to make results of Census Bureau research available to others and to encourage discussion on a variety of topics. They have not undergone a review and editorial process generally accorded official Census Bureau publications. View the list of working paper topics. View the list of working papers by year. All. 2024. 2023.

  23. What Public K-12 Teachers Want Americans To Know About Teaching

    Overall, 96% of surveyed teachers provided an answer to the open-ended question. Center researchers developed a coding scheme categorizing the responses, coded all responses, and then grouped them into the six themes explored in the data essay. For the questions for the general public, we surveyed 5,029 U.S. adults from Nov. 9 to Nov. 16, 2023.