Top Research Papers on Big Data Analytics

This book covers big data analytics techniques, e.g. optimization, SVM, feature selection, and their use in business, engineering and healthcare.

The state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, are investigated.

The research positively confirmed that medical facilities in Poland are moving towards data-based healthcare, together with its benefits, and clearly shows that the decisions made in medical facilities are highly data-driven.

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.

Continued advancement of sensors has led to an ever-increasing amount of data of various physical nature to be acquired from production lines. As rich information relevant to the machines and processes are embedded within these "big data", how to effectively and efficiently discover patterns in the big data to enhance productivity and economy has become both a challenge and an opportunity. This paper discusses essential elements of and promising solutions enabled by data science that are critical to processing data of high volume, velocity, variety, and low veracity, towards the creation of ad...

An overview of Big Data Analytics as a crucial process in many fields and sectors is presented and issues such as analytics cycle, analytics benefits and the movement from ETL to ELT paradigm as a result of Big data analytics in Cloud are discussed.

A comprehensive review of associated topics such as the concept of big data, model driven and data driven methodologies and the challenges and opportunities for future research are highlighted.

This paper proposes a review of related works to IoT, Big Data Analytics in smart buildings, revealing new trends of understanding information technologies to integrate in this sector.

A new binary variant of the wrapper feature selection grey wolf optimization and particle swarm optimization is proposed, and the K-nearest neighbor classifier with Euclidean matrices is used to find the optimal solutions.

This paper tenders a systematic literature review method for big data analytic approaches in weather forecasting (published between 2014 and August 2020) and presents a comparison of the aforementioned categories regarding accuracy, scalability, execution time, and other Quality of Service factors.

The findings from this study suggest that applications of BDA in healthcare can be observed from five perspectives, namely, health awareness among the general public, interactions among stakeholders in the healthcare ecosystem, hospital management practices, treatment of specific medical conditions, and technology in healthcare service delivery.

Big Data applications in CI processes within organizations within organizations are examined by exploring how organizations deal with Big Data analytics, and this study provides a context for developing Big Data frameworks and process models for CI in organizations.

It is posits that to successfully use data for competitive advantage, tourism and hospitality organisations need to solely expand compliance-based data governance frameworks to frameworks that include more effective privacy and ethics data solutions.

A neural network-based model is designed to diagnose and predict the pandemic, which can facilitate medical staff and predict pandemic using neural networks and also compare the results with other machine learning algorithms.

A comprehensive bibliometric analysis of applications of big data analytics in enterprises with a total of 1727 articles from the Scopus database identified four major thematic areas in the extant literature.

A modern data-driven decision theory, DECAS, is proposed, which extends upon classical decision theory by proposing three main claims: (1) (big) data and analytics (machine) should be considered as separate elements; (2) collaboration between the (human) decision maker and the analytics ( machine) can result in a collaborative rationality, extending beyond the classically defined bounded rationality.

It is proposed that data envelopment analysis (DEA) should be viewed as a method (or tool) for data-oriented analytics in performance evaluation and benchmarking and shown that network DEA is related to the value dimension of big data.

The 6G-based big data analysis technology can significantly improve large-scale IoT devices’ access success rate and enable the system to meet the requirements of low energy consumption and high accesssuccess rate, significant for research on more devices‘ access data analysis.

A comprehensive literature review of the current and emerging paradigms for Big educational data, including predictive analytics, learning analytics including collaborative, behavior, personal learnings and assessment, and technological challenges for Big data in education is presented.

ABSTRACT Operational risk management (ORM) is critical for any organization, and in the big data era, analytical tools for operational risk management are evolving faster than ever. This paper examines recent developments in academic ORM literature from the data analytics perspective. We focus on identifying present trends in ORM related to various types of natural and man‐made disasters that have been challenging all aspects of life. Although we examine the broader operations management (OM) literature, we keep the focus on the articles published in the well‐regarded OM journals, including bo...

A literature review is conducted to highlight the contributions of several studies in the domain of COVID-19-based big data analysis, and presents as a taxonomy several applications used to manage and control the pandemic.

It is argued that a firm’s BDAC helps enhance two distinct types of innovative capabilities, incremental and radical capabilities, and that information governance positively moderates this relationship.

The body of research revolving around big data (BD) and analytics in hospitality and tourism is surveyed by detecting macro topical areas, research streams and gaps and to develop an agenda for future research, based on a systematic literature review of academic papers published up to 31 December 2020.

A people analytics approach to predict employee attrition that shifts from a big data to a deep data context by focusing on data quality instead of its quantity and indicates that ‘business travel’ is the leading motivator for employees and must be considered within HR policies to retention.

This study explores the interdependence of big data analytics capabilities and the impact of these capabilities on firm performance using an integrated multicriteria decision-making (MCDM) methodology and shows that BDA capabilities are interdependent.

The paper provides deep insights into various big data technologies and discusses big data analytics in the context of the smart grid and presents the challenges and opportunities brought by the advent of machine learning and big data from smart grids.

A bibliometric study to analyze the contributions of major authors, universities/organizations, and countries in terms of productivity, citations, and bibliographic coupling and indicates saturation in the information systems publication landscape.

A strategic framework for CXM based on CX insights resulting from BDA is developed and a step-by-step guide on how to kick-start or implement this strategic framework is provided.

The proposed research work organizes and summarizes the existing published research work based on the research questions defined and keywords identified for the search process, which ultimately will help to use the study as evidence for treating patients and suggest medicines accordingly.

This survey denotes the strengths and weaknesses of the selected AI-driven big data analytics techniques and discusses the related parameters, comparing them in terms of scalability, efficiency, precision, and privacy.

A novel taxonomy is proposed, which serves as a tool for systematic naming and describing marketing attribution methods and allows to reflect on the contemporary attribution methods’ capabilities to account for the specifics of the customer journey, thereby creating currently lacking theoretical backbone for advancing the accuracy of value attribution.

Business intelligence systems give important and competitive information to business planners and decision-makers by combining operational and historical data with analytical tools. Business intelligence (BI) aims to increase the timeliness and quality of data, allowing managers to better comprehend their company's position with rivals. For example, changes in market share, consumer behavior and spending patterns, customer preferences, corporate capabilities, and market circumstances may be analyzed using business intelligence tools and technology. In addition, analysts and managers may utiliz...

This paper attempts to evaluate technical and nontechnical factors affecting the adoption of BD technologies, which includes BD development platforms, network architecture, data privacy implications, cybersecurity, and the opportunities and challenges of adopting BD technologies in the O&G industry.

Business intelligence helps gather essential information from a wide variety of unstructured data and convert them into actionable information that allows firms to make informed policy decisions and improve business efficiency and productivity. The challenges facing any organization in business intelligence and decision-making include plan failure, lack of preparation, resource failure, and risk-taking capability.This paper proposes a framework of Optimized Data Management using Big Data Analytics (ODM-BDA) to increase the intelligent organizational effectiveness and decision-making analysis i...

The IoUT, BMD, and their synthesis are comprehensively surveyed to inspire researchers, engineers, data scientists, and governmental bodies to further progress the field, to develop new tools and techniques, as well as to make informed decisions and set regulations related to the maritime and underwater environments around the world.

It is demonstrated that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling and the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector.

Big data initiatives are critical for transforming traditional organizational decision making into data-driven decision making. However, prior information systems research has not paid enough attention to the impact of big data analytics usage on decision-making quality. Drawing on the dynamic capability theory, this study investigated the impact of big data analytics usage on decision-making quality and tested the mediating effect of data analytics capabilities. We collected data from 240 agricultural firms in China. The empirical results showed that big data analytics usage had a positive im...

This study provides an insightful recommendation that could assist researchers, industrial practitioners, big data providers, and governments in the area of BDA on the challenges of the current BDA methods, and solutions that would alleviate these challenges.

The availability of digital technology in the hands of every citizenry worldwide makes an available unprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the high number of free BDA tools, platforms, and data mining tools makes it challenging to select the appropriate one for the right task. This paper presents a comprehensive mini-literature review of ML in BDA, using a keyword search; a total of 1512 published articles was identified. ...

Findings indicate that different combinations of organizational inertia including economic, political, socio-cognitive, negative psychology, and socio-technical hamper the formation of each type of capability.