Top Research Papers on Data Analytics

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

This study presents an innovative fusion structure that encompasses the theoretical foundation and technological innovation of data analytics and optimization, as well as their application to smart industrial engineering, to provide a four-level framework for smart industry.

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.

This paper critically review how IoT-generated data are processed for machine learning analysis and highlights the current challenges in furthering intelligent solutions in the IoT environment and proposes a framework to enable IoT applications to adaptively learn from other IoT applications.

To better understand current research efforts, hot topics, and tending topics on this critical intersection, the basic concepts in Industry 4.0 and data analytics are introduced first and the merge between them is decomposed into three components: industry sectors, cyber-physical systems, and analytic methods.

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.

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.

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 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.

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.

This survey comprehensively review recent research trends in trajectory data management, ranging from trajectory pre-processing, storage, common trajectory analytic tools, such as querying spatial-only and spatial-textual trajectory data, and trajectory clustering, and explores four closely related analytical tasks commonly used with trajectory data in interactive or real-time processing.

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...

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.

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.

The spread and use of social networks provide a rich data source that can be used to answer a wide range of research questions from various disciplines. However, the nature of social media data poses a challenge to the analysis. The aim of this study is to provide an in-depth overview of the research that analyzes social media data since 2017. An extensive literature review based on 94 papers led to the findings that clear definitions are neither established nor commonly applied. Predominant research domains include marketing, hospitality and tourism, disaster management, and disruptive techno...

Lambada is presented, a serverless distributed data processing framework designed to explore how to perform data analytics on serverless computing, and which scenarios serverless makes sense from an economic and performance perspective.

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.

The current state of the art in data mining in education is provided by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, themain objectives, and the future trends in this research area.

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.

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.