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.

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.

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.

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.

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 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 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.

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.

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 systematically surveys 346 articles on MMLA published during the past three years and presents a conceptual model for reviewing these articles from three dimensions: data types, learning indicators, and data fusion.

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.

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.

In this paper, an automated driving system (ADS) data acquisition and analytics platform for vehicle trajectory extraction, reconstruction, and evaluation based on connected automated vehicle (CAV) cooperative perception are presented. This platform presents a holistic pipeline from the raw advanced sensory data collection to data processing, which is capable of processing the sensor data from multi-CAVs and extracting the objects' Identity (ID) number, position, speed, and orientation information in the map and Frenet coordinates. First, the ADS data acquisition and analytics platform are pre...

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.

Deep learning has been acknowledged as being robust in managing and controlling the performance of construction safety. However, there is an absence of state-of-the-art review that examines its developments and applications from the perspective of data utilization. Our review aims to fill this void and addresses the following research question: what developments in deep learning for data mining have been made to manage safety in construction? We systematically review the extant literature of deep-learning-based data analytics for construction safety management, including: (1) image/video-based...

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 study posits that the integration of AI and Data Analytics into BI is a fundamental, rather than a transient, shift in business operations, and recommends further exploration into the ethical implications of AI in BI, the development of user-friendly AI tools for non-technical users, and an examination of the long-term impacts of AI-driven BI across various industries.

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.

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...

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.

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.

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 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 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.

The paper proposed the prediction model of Apple disease in the apple orchards of Kashmir valley using data analytics and Machine learning in IoT system and discusses the challenges faced when incorporating these technologies in the traditional farming approaches.

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...

Abstract We introduce and make openly accessible a comprehensive, multivariate time series (MVTS) dataset extracted from solar photospheric vector magnetograms in Spaceweather HMI Active Region Patch (SHARP) series. Our dataset also includes a cross-checked NOAA solar flare catalog that immediately facilitates solar flare prediction efforts. We discuss methods used for data collection, cleaning and pre-processing of the solar active region and flare data, and we further describe a novel data integration and sampling methodology. Our dataset covers 4,098 MVTS data collections from active region...

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.

A positive relationship between the data analytics capabilities and supply chain performance in digital upstream supply chain procurement processes is confirmed and clarifies the impact and role of data Analytics capabilities in digital supply chain development and success.

An adaptive hyperparameter adjustment method is proposed, which allows the hyperparameters in the supervisory mechanism to be automatically adjusted along with the learning process in the hybrid parallel learning algorithm.