Top Research Papers on Big 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.

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

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...

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 research discusses big data, the available big data analytical tools, the need to use big data analytics with its benefits and challenges, and the need to use big data analytics with its benefits and challenges in a small island state.

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.

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

This survey aims to provide an insightful overview of state-of-the-art development in the Lie group machine learning, to comprehensively understand the state of the state, identify the most appropriate tools for particular applications, and identify directions for future research.

This chapter contributes to a comparative study of big data analytics tools based on different aspects such as their functionality, pros, and cons based on characteristics that can be used to determine the best and most efficient among them.

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A novel automatic background subtraction algorithm for urban surveillance systems in which the computer can automatically renew an image as the new background image when no object is detected is proposed.

DHA-RS combines stacked denoising auto-encoders with neural collaborative filtering, which corresponds to the process of learning user and item features from auxiliary information to predict user preferences, and performs better than state-of-the-art methods.

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This article presents a comprehensive survey of the matrix completion methods used in recommendation systems, focusing on the mathematical models for matrix completion and the corresponding computational algorithms as well as their characteristics and potential issues.

A Hypertext-Induced Topic Search (HITS) based Topic-Decision method (TD-HITS), and a Latent Dirichlet Allocation (LDA) based Three-Step model (TS-LDA).

A survey of various state-of-the-art research works done to handle the inefficient storage problem of Big Data and proposes to split the Big Data into small chunks and provide each chunk to a different cluster for removing the redundant data and compressing it.

The major components of the Hadoop framework and it’s working process are presented and the limitations of traditional approaches to handle such data are focused on and emphasis is laid on the tools which can handle big data effectively.

Predictive analytics is a set of techniques and an advanced methodological and research approach that seeks to reach conclusions about the future, rather than explanations of specific issues or phenomena. The fast growth in popularity and application of data science to different businesses and activities, such as human services and nonprofit management, are related to the emergence and consolidation of big data. In terms of digital networking, the availability of data produced by individuals every day is enormous. Tools and techniques such as machine learning, deep learning, visualization, tim...

The efficiency of machine learning and deep learning in a sensitive environment was evaluated in this paper, which reviewed security problems in big data.

A large number of multi-view clustering algorithms are summarized, a taxonomy according to the mechanisms and principles involved is provided, and a few examples for how these techniques are used are given.

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.

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.

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.

The implementation stages of the educational big data platform on a cloud computing platform and the organization of educational data in a data lake architecture are highlighted and two analytical applications are performed to test the feasibility of the presented platform in discovering knowledge that potentially promotes the educational institutions.

The definitions and the challenges of big data systems are described, a systematic framework decomposes the architecture ofbig data systems into four stages like data generation, data acquisition, data storage and data analytics and some solutions are discussed.

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 chapter reviews the literature on the use of business analytics in higher education to examine how descriptive, predictive, prescriptive, decisive and basic analytics, and data collection influence the decision-making process at the executive leadership level of the state universities in terms of student retention and graduation rates.

The potential combined role of professional qualification examinations and master’s degrees in skills provision for future practitioners in accounting and data analysis is considered.

This paper focuses on models, paradigms, techniques and future challenges of temporal big data analytics, by reporting on state-of-the-art results as well as emerging trends, with also criticisms on future work that the community should expect from the community.

The study develops a theoretical structure to investigate if big data coupled with different production solutions can provide for a dispersed production system and discovers several uses of distributed manufacturing principles to evaluate the current production processes worked for larger customer product solutions by using analytics and industry analysis.

This course introduces the student to concepts of big data management, database management, data mining techniques and the underlying statistics that support big data analytics and uses the programming language R as the primary tool for analysis.

The Book give a description about Big Data Definitions, Characteristics, Applications and the Technology Hadoop which handles it and a detailed literature review is presented.

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.

Big data analytics have the potential to identify efficiencies that can be made in a wide range of sectors, and to lead to innovative new products and services, greater competitiveness and economic growth.

An extensive survey of big data analytics research is provided, while highlighting the specific concern in big data world.

A thorough review of the recent advancement of IoT with big data and analytics as well as the opportunities created by enabling analytics in an IoT system is presented.

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.

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.

Working with big data using data mining tools is rapidly becoming a trend in education industry. The combination of the current capacity to collect, store, manage and process data in a timely manner, and data from online educational platforms represents an unprecedented opportunity for educational institutes, learners, educators, and researchers. In this position paper, we consider some basic concepts as well as most popular tools, methods and techniques regarding Educational Data Mining and Learning Analytics, and discuss big data applications in language learning, in particular.

This paper aims to study many numbers of the various analytics ways and tools which might be practiced to Big Data, and debates in an elaborate manner about analyzing tools, processing tools and querying tools for Big data.

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

The concept of Big data is defined and the importance of Big Data Analytics is stressed in order to support decision for their strategies.

This paper presents a literature survey of the Big Data and Big Data analytics, a detailed survey of analytics platform like Apache Hadoop, and delineates the evaluation and research direction for the Big data System.

The applicability of offline engineering software solutions in operationalizing wide area measurement system(WAMS) solutions in the control room is detailed and the applications integrated in BigSync are demonstrated.

This review paper will present intriguing applications for data scientists, exploring the challenges and recent technological advancements in big data analytics concerning astronomy, and critically assess the strengths and weaknesses of various approaches, methodologies, or tools used within the context of astronomy.

Big Data analysis is a current research area that mainly explore the potential impact of big data challenges, various tools used for big data analysis and opens a new horizon for researchers.

This book extensively explores all facets of Big Data Analytics and comprehensively addresses the topic matter.

Upon considering the analytics in the Big Data sector, predictive analytics and social media analytics are widely used for determining the pattern or trend which is about to happen.