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.

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.