Top Research Papers on Data Analytics

A lot of work is being done to find ways to minimize the dimensionality of big data in order to provide analytics reports that are even more accurate and data visualizations that are more interesting.

The Social Data Analytics dual-title degree program enables students from diverse graduate programs to attain and be identified with an interdisciplinary array of tools, techniques, and methodologies for social data analytics, while maintaining a close association with a home discipline.

This article presents a detailed review of healthcare data analytics and the respective data sources, and discusses advanced data analytics for the healthcare sector, and its practical systems as well as applications of healthcare data analytics.

The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software.

A theory of credible skepticism in organizations is developed to explain the main tradeoffs in organizing data generation, analysis, and reporting and shows that motivating informative experimentation while discouraging misreporting are often conflicting organizational goals.

The human brain works in such a complex way that the authors have not yet managed to decipher its functional mysteries, so it is important to understand how it works in a more systematic way.

This paper discusses how the revolution of data analytics based on artificial intelligence algorithms might provide improvements for many applications and presents several innovative methods that use data analytics techniques to improve the analysis process and data management.

Data analytics projects can be like throwing darts in the dark. Problem‐centric thinking is vital, argue Vincent Charles, Ali Emrouznejad, Tatiana Gherman, and James Cochran

Mobile, ubiquitous, and immersive computing appear poised to transform visualization, data science, and data-driven decision making.

This article presents a detailed review of healthcare data analytics and the respective data sources, and discusses advanced data analytics for the healthcare sector, and its practical systems as well as applications of Healthcare data analytics.

A theoretical analysis of the MD market is presented, addressing challenges such as data fragmentation, ambiguous property rights, and the public-good nature of MD, and identifying significant inefficiencies and market failures, highlighting the need for nuanced policy interventions.

This paper builds temporal and demographic hierarchies, which capture characteristics of COVID-19 patients, to discover valuable knowledge and information about temporal-demographic characteristics of these patients, and evaluates the practicality of the solution in conducting smart data analytics on CO VID-19 data.

This experimental study analyses to create a novel ensemble technique for balancing highly imbalanced data, and results showed that the SMOTE ENN technique produces higher classification with accuracy than the SMOTE and SMOTE Tomek techniques.

This study has addressed certain aspects of such a large topic and can be improved by presenting information about the tools and techniques used in data analytics, the problems encountered in data Analytics, and the trends that can be observed in the future in future studies.

This book takes a data oriented approach to studying cyber threats, showing in depth how traditional methods such as anomaly detection can be extended using data analytics and also applies data analytics to non-traditional views of cybersecurity, such as multi domain analysis, time series and spatial data analysis, and human-centered cybersecurity.

This is the first study to measure the forecast accuracy of the multiple regression and neural network models performed using a Micro Data set and no generalized conclusions on predictors included in the models could be drawn.

This work discusses the motivations for distributing analytics, the different approaches that have been taken, and ways in which analytics can be distributed to the very edges of the network, before presenting the Databox, a platform for supporting distributed analytics.

CommonGraph is proposed, an approach for efficient processing of queries on evolving graphs that implements optimizations that enable efficient handling of edge additions without resorting to expensive in place graph mutations, significantly reducing the streaming overhead, and enabling direct reuse of shared edges among different snapshots.

A process theory is developed that explains the transformations data undergo on their way to becoming commodities and shows how these transformations are related to organizational practices and to the editable, portable, and recontextualizable attributes of data.

The project deals with studying and implementing selected tools in order to provide the right framework to achieve an interactive dashboard capable of predictive analytics which can also be integrated into the existing products of the company.

The paper answers the call for more empirical studies on the utilization of BDA in financial auditing by analyzing the application of such techniques in an unexplored operational setting in which auditees are mainly medium-sized enterprises and family-run businesses.

Titel Big Data Management and Analytics Typ Vorlesung mit Übung Credits 6 ECTS Lehrform/SWS 3V + 2Ü Sprache Englisch Modulniveau Master Arbeitsaufwand Präsenzstunden 75 Stunden Eigenstudium 105 Stunden Gesamtaufwand 180 Stunden Angestrebte Lernergebnisse Die Studierenden sind in der Lage die Anwendungsgebiete für Big Data Systeme von Anwendungen herkömmlicher Datenbanksysteme abzugrenzen und die Vorteile der verschiedene Big Data Systeme wiederzugeben. Die Studierenden verstehen die Funktionsweise von parallelen Batch-Systemen und parallelen Streaming Systemen und können diese installieren und...

Challenges for information management, and in turn law and society, include: AI‐powered predictive policing; big data for legal and adversarial decisions; bias using big data and analytics in profiling and predicting criminality; forecasting crime risk and crime rates; and, regulating AI systems.

Global stakeholders have expressed interest in increasing the use of data analytics throughout the audit process. While data analytics offer great promise in identifying audit-relevant information, auditors may not uniformly incorporate this information into their decision making. This study examines whether conclusions from two data analytic inputs, the type of data analytical model (anomaly vs. predictive) and type of data analyzed (financial vs. nonfinancial), result in different auditors’ decisions. Findings suggest that conclusions from data analytical models and data analyzed jointly imp...

This paper focuses on a cross-region comparison of demand and employer requirements in job ads posted on LinkedIn for entry-level data scientist, data analytics, business analytics, marketing analytics and digital analytics professionals.

Bigdata analytics and Artificial Intelligence technologies or platforms such as ChatGPT may be utilized to mitigate cybersecurity risks and the barriers to such adoptions are examined, proposing potential approaches to address them.

This paper discusses agricultural difficulties as well as the strategies utilized to apply the offered remedies, and presents feasible solutions and minor improvements to the publications previously reviewed.

This paper presents Sieve, a learning-enhanced index designed to efficiently filter out irrelevant blocks by capturing data patterns, which utilizes piece-wise linear functions to capture block distribution trends over the key space.

In this world of pharmaceutical analysis, the dedicated analysts are not just observers of the future, they are the architects of progress and innovation, shaping a future characterized by ground breaking discoveries and enhanced pharmaceutical solutions.

This thesis develops differentially private algorithms to analyze distributed and streaming data and offers one of the strongest privacy guarantees for the streaming model, user-level pan-privacy, which ensures that the privacy of any user is protected, even against an adversary that observes the internal state of the algorithm.

A systematic analysis of how data analytics affects Blockchain performance is presented, investigating the current state of Blockchain-based data analytics techniques in research fields and exploring how specific features of this new technology may transform traditional business methods.

It emerges that big data analysis (BDA), traditional marketing analysis (TMA) and big data system quality (BDSQ) are significant determinants of new product development (NPD) success and the BDA and TMA significantly affect the BDSQ.

A significant impact of data analytics on the accounting industry which has enabled accounting professionals to draw appropriate decisions based on real-time data is suggested.

This paper demonstrates that there is a third option to provide the functionality needed to run off-the-shelf distributed data processing systems on top of existing serverless platforms (e.g., AWS Lambda) in a transparent manner and presents initial experimental results of the TPC-H benchmark.

Six articles in this special issue each play a role in advancing sport analytics theory, producing new knowledge, and developing new inquiries and provide a foundation for future research on this evolving area within the field of sport management.

This paper presents the application of federated learning techniques to a well-known learning analytics problem: student dropout prediction and concludes that the proposed solutions achieve comparable results from the performance point of view with the centralized versions, avoiding the concentration of all the data in a single place for training the models.

This Special Issue aims to present cutting-edge research on the application of advanced data analysis and machine learning techniques in education with the aim of extracting knowledge that can be highly useful for improving educational processes.

The significances of utilizing big data technologies in the creation of intelligence logistics are elaborated, analyzing its critical role not only in advancing the development of cross-borders e-commerce but also throughout the process of the overall transformations and expansion of the national economies.

This paper aims to structure the thoughts around data analytics in investment banks, and puts forward a classification of relevant data analytics use cases, and discusses some of the challenges that investment banks might encounter when engaging in this transformation process.

The immense measure of organized, unstructured, and semi-organized information are produced each second around the cyber world, which should be managed efficiently.

Various topics are covered in this special issue, including Bayesian learning of unconfined compressive strength of rock, machine learning of geological details from borehole logs for the development of the high-resolution subsurface geological profile, determination of optimal sampling locations using Gaussian Process Regression (GPR), and Gaussian mixture model for estimating debris-flow exceedance probability.

A framework that enables privacy infusion in all aspects of big data analytics is discussed and TIPPERS: a testbed for privacy enhancing technologies is presented.

This chapter presents a system called seabed, which facilitates efficient analytics on huge encrypted datasets, and outlines research directions for security intelligence by applying security analytics.

This chapter described technology that is used to handle the big data like, NoSQL, Hadoop, MangoDB, MapReduce, etc.

This article presents HC processing techniques, Big Data Analytics (BDA), large dataset instruments, and some classic ML and DM methodologies, and the use of deep learning to Data Analytics is investigated.

A Data Analytics/Big Data framework applied to AMI data as a tool to leverage the potential of this data within the applications in a Smart City to face the challenges arriving in energy markets.

A brief introduction of data analytics, architecture of big data, knowledge discovery and big data algorithms is presented.

This chapter discusses both the concepts and the tools being used in the zone of technology-education, where data science and data analytics have become the buzz words.

This paper proposes models of machine learning that can successfully detect fake news and applies three machine learning models; random forest classifier, logistic regression, and term frequency-inverse document frequency (TF-IDF) vectorizer.

The study concludes that for Nigerian enterprises to fully leverage IT innovations, a robust governance framework, strategic alignment of IT practices, and investment in technology and skills development are imperative.