Big data is characterized by the seven V’s: volume (large amounts of data), velocity (continuously processed data in real time), variety (unstructured, semi–structured or structured data in different formats and from multiple and diverse sources), veracity (uncertainty and trustworthiness of data), validity (relevance of data to the problem to solve), volatility (constant change of input data), and value (how data and its analysis adds value). Big data systems are software applications that process and potentially generate big data. Such applications receive and process data from various diverse (usually distributed) sources, such as sensors, devices, whole networks, social networks, mobile devices or devices in an Internet–of–Things. They process high workloads of data and handle high requests for data. The idea is to use large amounts of data strategically and efficiently to provide additional intelligence.<br>This book will explore software engineering of big data systems, including topics related to requirements as well as architecture, detailed design, implementation, maintenance and operations. Software engineering is the application of a systematic approach to designing, operating and maintaining software systems and the study of all the activities involved in achieving the same. The software engineering discipline and research into software systems flourished with the advent of computers and the technological revolution ushered in by the World Wide Web and the Internet. Software systems have grown dramatically to the point of becoming ubiquitous. They have a significant impact on the global economy and on how we interact and communicate with each other and with computers using software in our daily lives. However, there have been major changes in the type of software systems developed over the years. In the past decade owing to breakthrough advancements in cloud and mobile computing technologies, unprecedented volumes of hitherto inaccessible data, referred to as big data, has become available to technology companies and business organizations farsighted and discerning enough to use it to create new products, and services generating astounding profits.<br>This book will focus on several research challenges of software engineering for developing big data systems, in particular by:<br>– surveying the existing software engineering literature on applying software engineering principles into developing and supporting big data systems<br>– identifying the fields of application for big data software systems<br>– investigating the software engineering knowledge areas that have seen research related to big data systems<br>– revealing the gaps in the knowledge areas that require more focus for big data systems development<br>– determining the open research challenges in each software engineering knowledge area that need to be met.<br>BACKGROUND TOPICS<br>All chapters should consider the practical application of the topic through case studies, experiments, empirical validation, or systematic comparisons with other data systems, as well as allow further approaches already in practice. The book intends to discuss systematic and disciplined approaches to building big data systems, dissemination of the state–of–the–art methods and techniques for representing and evaluating these systems.<br>
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SEBDS
City
Heidelberg
Country
Germany
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