This book constitutes the thoroughly refereed joint proceedings of the Third and Fourth Workshop on Big Data Benchmarking. The third WBDB was held in Xi'an, China, in July 2013 and the Fourth WBDB was held in San José, CA, USA, in October, 2013. The 15 papers presented in this book were carefully reviewed and selected from 33 presentations. They focus on big data benchmarks; applications and scenarios; tools, systems and surveys.

This book constitutes the thoroughly revised selected papers of the 4th and 5th workshops on Big Data Benchmarks, Performance Optimization, and Emerging Hardware, BPOE 4 and BPOE 5, held respectively in Salt Lake City, in March 2014, and in Hangzhou, in September 2014. The 16 papers presented were carefully reviewed and selected from 30 submissions. Both workshops focus on architecture and system support for big data systems, such as benchmarking; workload characterization; performance optimization and evaluation; emerging hardware.

This book constitutes the refereed post-conference proceedings of the 12th TPC Technology Conference on Performance Evaluation and Benchmarking, TPCTC 2020, held in August 2020.The 8 papers presented were carefully reviewed and cover the following topics: testing ACID compliance in the LDBC social network benchmark; experimental performance evaluation of stream processing engines made easy; revisiting issues in benchmarking metric selection; performance evaluation for digital transformation; experimental comparison of relational and NoSQL document systems; a framework for supporting repetition and evaluation in the process of cloud-based DBMS performance benchmarking; benchmarking AI inference; a domain independent benchmark evolution model for the transaction processing performance council.

An in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning. Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions. The book covers basic concepts and necessary background knowledge, including data processing frameworks, storage systems, and hardware capabilities; offers a detailed discussion of technical issues in accelerating big data computing in terms of computation, communication, memory and storage, codesign, workload characterization and benchmarking, and system deployment and management; and surveys benchmarks and workloads for evaluating big data middleware systems. It presents a detailed discussion of big data computing systems and applications with high-performance networking, computing, and storage technologies, including state-of-the-art designs for data processing and storage systems. Finally, the book considers some advanced research topics in high-performance big data computing, including designing high-performance deep learning over big data (DLoBD) stacks and HPC cloud technologies.

BOOK SUMMARY The main topics in this book are; • Machine Learning Algorithms for Predictive Analysis • Natural Language Processing in Text Analytics • Graph Analytics and Network Analysis • Time Series Analysis and Forecasting • Deep Learning in Image and Video Analytics • Streaming Data Analytics • Spatial Data Analysis and Geospatial Analytics • Big Data Ethics and Privacy Considerations “Advanced Big Data Analytics” offers a comprehensive exploration of cutting-edge techniques and methodologies in the realm of big data analysis. Through a blend of theoretical insights, practical examples and real-world case studies, the book guides readers in harnessing the power of vast datasets to uncover valuable insights, make informed decisions and address contemporary data-driven challenges.

This book constitutes the thoroughly refereed post-conference proceedings of the 7th TPC Technology Conference on Performance Evaluation and Benchmarking, TPSTC 2015, held in conjunction with the 40th International Conference on Very Large Databases (VLDB 2015) in Kohala Coast, Hawaii, USA, in August/September 2015. The 8 papers presented together with 1 keynote, and 1 vision paper were carefully reviewed and selected from 24 submissions. Many buyers use TPC benchmark results as points of comparison when purchasing new computing systems. The information technology landscape is evolving at a rapid pace, challenging industry experts and researchers to develop innovative techniques for evaluation, measurement and characterization of complex systems. The TPC remains committed to developing new benchmark standards to keep pace, and one vehicle for achieving this objective is the sponsorship of the Technology Conference on Performance Evaluation and Benchmarking (TPCTC).

In recent years, there has been steady increase in the interest shown in both big data analytics and the use of information technology (IT) solutions to improve healthcare services. Despite the growing interest, there are limited materials, to addressing the needs and challenges posed by the activities and processes including the use of big data. From IT solutions’ perspectives, this book aims to advance the deployment and use of big data analytics to increase patients’ big data usefulness and improve healthcare service delivery. The book provides significant insights and useful guide on how to access and manage big data, in improving healthcare service delivery. The book contributes a fresh perspective, which primarily comes from the complementary use of analytics approach with actor-network theory (ANT), and other techniques, in advancing healthcare service delivery. Accessing and managing healthcare big data have always been a challenging exercise. Due to the sensitivity of the health sector, the focus on patients’ big data is from either technical or social perspective. Thus, the book employs sociotechnical theories, ANT and structuration theory (ST) as lenses to examine and explain the factors that enable and constrain the use of patients’ big data for health services. By doing so, the book brings a different dimension and advance health service delivery. Providing a timely and important contribution to this critical area, this book is a valuable, international resource for academics, postgraduate students and researchers in the areas of IT, big data analytics, data management and health informatics.

These two volumes set LNCS 8421 and LNCS 8422 constitutes the refereed proceedings of the 19th International Conference on Database Systems for Advanced Applications, DASFAA 2014, held in Bali, Indonesia, in April 2014. The 62 revised full papers presented together with 1 extended abstract paper, 4 industrial papers, 6 demo presentations, 3 tutorials and 1 panel paper were carefully reviewed and selected from a total of 257 submissions. The papers cover the following topics: big data management, indexing and query processing, graph data management, spatio-temporal data management, database for emerging hardware, data mining, probabilistic and uncertain data management, web and social data management, security, privacy and trust, keyword search, data stream management and data quality.

This book provides an overview of the resources and research projects that are bringing Big Data and High Performance Computing (HPC) on converging tracks. It demystifies Big Data and HPC for the reader by covering the primary resources, middleware, applications, and tools that enable the usage of HPC platforms for Big Data management and processing.Through interesting use-cases from traditional and non-traditional HPC domains, the book highlights the most critical challenges related to Big Data processing and management, and shows ways to mitigate them using HPC resources. Unlike most books on Big Data, it covers a variety of alternatives to Hadoop, and explains the differences between HPC platforms and Hadoop.Written by professionals and researchers in a range of departments and fields, this book is designed for anyone studying Big Data and its future directions. Those studying HPC will also find the content valuable.

From cloud computing to data analytics, society stores vast supplies of information through wireless networks and mobile computing. As organizations are becoming increasingly more wireless, ensuring the security and seamless function of electronic gadgets while creating a strong network is imperative. Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics highlights the challenges associated with creating a strong network architecture in a perpetually online society. Readers will learn various methods in building a seamless mobile computing option and the most effective means of analyzing big data. This book is an important resource for information technology professionals, software developers, data analysts, graduate-level students, researchers, computer engineers, and IT specialists seeking modern information on emerging methods in data mining, information technology, and wireless networks.