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Big Data: 10th CCF Conference, BigData 2022, Chengdu, China, November 18–20, 2022, Proceedings (Communications in Computer and Information Science #1709)
by Tianrui Li Rui Mao Guoyin Wang Fei Teng Lei Zhang Li Wang Laizhong Cui Jie HuThis book constitutes the refereed proceedings of the 10th CCF Conference on BigData 2022, which took place in Chengdu, China, in November 2022. The 8 full papers presented in this volume were carefully reviewed and selected from 28 submissions. The topics of accepted papers include theories and methods of data science, algorithms and applications of big data.
Big Data: 8th CCF Conference, BigData 2020, Chongqing, China, October 22–24, 2020, Revised Selected Papers (Communications in Computer and Information Science #1320)
by Hong Mei Weiguo Zhang Wenfei Fan Zili Zhang Yihua Huang Jiajun Bu Yang Gao Li WangThis book constitutes the proceedings of the 8th CCF Conference on Big Data, BigData 2020, held in Chongqing, China, in October 2020.The 16 full papers presented in this volume were carefully reviewed and selected from 65 submissions. They present recent research on theoretical and technical aspects on big data, as well as on digital economy demands in big data applications.
Big Data: A Primer (Studies in Big Data #11)
by Hrushikesha Mohanty Prachet Bhuyan Deepak ChenthatiThis book is a collection of chapters written by experts on various aspects of big data. The book aims to explain what big data is and how it is stored and used. The book starts from the fundamentals and builds up from there. It is intended to serve as a review of the state-of-the-practice in the field of big data handling. The traditional framework of relational databases can no longer provide appropriate solutions for handling big data and making it available and useful to users scattered around the globe. The study of big data covers a wide range of issues including management of heterogeneous data, big data frameworks, change management, finding patterns in data usage and evolution, data as a service, service-generated data, service management, privacy and security. All of these aspects are touched upon in this book. It also discusses big data applications in different domains. The book will prove useful to students, researchers, and practicing database and networking engineers.
Big Data: 29th British National Conference on databases, BNCOD 2013, Oxford, UK, July 8-10, 2013. Proceedings (Lecture Notes in Computer Science #7968)
by Dan Olteanu Georg Gottlob Christian SchallhartThis book constitutes the thoroughly refereed post-conference proceedings of the 29th British National Conference on Databases, BNCOD 2013, held in Oxford, UK, in July 2013. The 20 revised full papers, presented together with three keynote talks, two tutorials, and one panel session, were carefully reviewed and selected from 42 submissions. Special focus of the conference has been "Big Data" and so the papers cover a wide range of topics such as query and update processing; relational storage; benchmarking; XML query processing; big data; spatial data and indexing; data extraction and social networks.
Big Data: A Tutorial-Based Approach
by Nasir RaheemBig Data: A Tutorial-Based Approach explores the tools and techniques used to bring about the marriage of structured and unstructured data. It focuses on Hadoop Distributed Storage and MapReduce Processing by implementing (i) Tools and Techniques of Hadoop Eco System, (ii) Hadoop Distributed File System Infrastructure, and (iii) efficient MapReduce processing. The book includes Use Cases and Tutorials to provide an integrated approach that answers the ‘What’, ‘How’, and ‘Why’ of Big Data. Features Identifies the primary drivers of Big Data Walks readers through the theory, methods and technology of Big Data Explains how to handle the 4 V’s of Big Data in order to extract value for better business decision making Shows how and why data connectors are critical and necessary for Agile text analytics Includes in-depth tutorials to perform necessary set-ups, installation, configuration and execution of important tasks Explains the command line as well as GUI interface to a powerful data exchange tool between Hadoop and legacy r-dbms databases
Big Data: A Tutorial-Based Approach
by Nasir RaheemBig Data: A Tutorial-Based Approach explores the tools and techniques used to bring about the marriage of structured and unstructured data. It focuses on Hadoop Distributed Storage and MapReduce Processing by implementing (i) Tools and Techniques of Hadoop Eco System, (ii) Hadoop Distributed File System Infrastructure, and (iii) efficient MapReduce processing. The book includes Use Cases and Tutorials to provide an integrated approach that answers the ‘What’, ‘How’, and ‘Why’ of Big Data. Features Identifies the primary drivers of Big Data Walks readers through the theory, methods and technology of Big Data Explains how to handle the 4 V’s of Big Data in order to extract value for better business decision making Shows how and why data connectors are critical and necessary for Agile text analytics Includes in-depth tutorials to perform necessary set-ups, installation, configuration and execution of important tasks Explains the command line as well as GUI interface to a powerful data exchange tool between Hadoop and legacy r-dbms databases
Big Data: Concepts, Warehousing, and Analytics
by Maribel Yasmina Santos Carlos CostaBig Data is a concept of major relevance in today’s world, sometimes highlighted as a key asset for productivity growth, innovation, and customer relationship, whose popularity has increased considerably during the last years. Areas like smart cities, manufacturing, retail, finance, software development, environment, digital media, among others, can benefit from the collection, storage, processing, and analysis of Big Data, leveraging unprecedented data-driven workflows and considerably improved decision-making processes.The concept of a Big Data Warehouse (BDW) is emerging as either an augmentation or a replacement of the traditional Data Warehouse (DW), a concept that has a long history as one of the most valuable enterprise data assets. Nevertheless, research in Big Data Warehousing is still in its infancy, lacking an integrated and validated approach for designing and implementing both the logical layer (data models, data flows, and interoperability between components) and the physical layer (technological infrastructure) of these complex systems. This book addresses models and methods for designing and implementing Big Data Systems to support mixed and complex decision processes, giving special attention to BDWs as a way of efficiently storing and processing batch or streaming data for structured or semi-structured analytical problems.
Big Data: Concepts, Warehousing, and Analytics
by Maribel Yasmina Santos Carlos CostaBig Data is a concept of major relevance in today’s world, sometimes highlighted as a key asset for productivity growth, innovation, and customer relationship, whose popularity has increased considerably during the last years. Areas like smart cities, manufacturing, retail, finance, software development, environment, digital media, among others, can benefit from the collection, storage, processing, and analysis of Big Data, leveraging unprecedented data-driven workflows and considerably improved decision-making processes.The concept of a Big Data Warehouse (BDW) is emerging as either an augmentation or a replacement of the traditional Data Warehouse (DW), a concept that has a long history as one of the most valuable enterprise data assets. Nevertheless, research in Big Data Warehousing is still in its infancy, lacking an integrated and validated approach for designing and implementing both the logical layer (data models, data flows, and interoperability between components) and the physical layer (technological infrastructure) of these complex systems. This book addresses models and methods for designing and implementing Big Data Systems to support mixed and complex decision processes, giving special attention to BDWs as a way of efficiently storing and processing batch or streaming data for structured or semi-structured analytical problems.
Big Data: Understanding How Data Powers Big Business
by Bill SchmarzoLeverage big data to add value to your business Social media analytics, web-tracking, and other technologies help companies acquire and handle massive amounts of data to better understand their customers, products, competition, and markets. Armed with the insights from big data, companies can improve customer experience and products, add value, and increase return on investment. The tricky part for busy IT professionals and executives is how to get this done, and that's where this practical book comes in. Big Data: Understanding How Data Powers Big Business is a complete how-to guide to leveraging big data to drive business value. Full of practical techniques, real-world examples, and hands-on exercises, this book explores the technologies involved, as well as how to find areas of the organization that can take full advantage of big data. Shows how to decompose current business strategies in order to link big data initiatives to the organization’s value creation processes Explores different value creation processes and models Explains issues surrounding operationalizing big data, including organizational structures, education challenges, and new big data-related roles Provides methodology worksheets and exercises so readers can apply techniques Includes real-world examples from a variety of organizations leveraging big data Big Data: Understanding How Data Powers Big Business is written by one of Big Data's preeminent experts, William Schmarzo. Don't miss his invaluable insights and advice.
Big Data: Understanding How Data Powers Big Business
by Bill SchmarzoLeverage big data to add value to your business Social media analytics, web-tracking, and other technologies help companies acquire and handle massive amounts of data to better understand their customers, products, competition, and markets. Armed with the insights from big data, companies can improve customer experience and products, add value, and increase return on investment. The tricky part for busy IT professionals and executives is how to get this done, and that's where this practical book comes in. Big Data: Understanding How Data Powers Big Business is a complete how-to guide to leveraging big data to drive business value. Full of practical techniques, real-world examples, and hands-on exercises, this book explores the technologies involved, as well as how to find areas of the organization that can take full advantage of big data. Shows how to decompose current business strategies in order to link big data initiatives to the organization’s value creation processes Explores different value creation processes and models Explains issues surrounding operationalizing big data, including organizational structures, education challenges, and new big data-related roles Provides methodology worksheets and exercises so readers can apply techniques Includes real-world examples from a variety of organizations leveraging big data Big Data: Understanding How Data Powers Big Business is written by one of Big Data's preeminent experts, William Schmarzo. Don't miss his invaluable insights and advice.
Big Data: An Art of Decision Making
by Eglantine SchmittManipulating and processing masses of digital data is never a purely technical activity. It requires an interpretative and exploratory outlook – already well known in the social sciences and the humanities – to convey intelligible results from data analysis algorithms and create new knowledge. Big Data is based on an inquiry of several years within Proxem, a software publisher specializing in big data processing. The book examines how data scientists explore, interpret and visualize our digital traces to make sense of them, and to produce new knowledge. Grounded in epistemology and science and technology studies, Big Data offers a reflection on data in general, and on how they help us to better understand reality and decide on our daily actions.
Big Data: An Art of Decision Making
by Eglantine SchmittManipulating and processing masses of digital data is never a purely technical activity. It requires an interpretative and exploratory outlook – already well known in the social sciences and the humanities – to convey intelligible results from data analysis algorithms and create new knowledge. Big Data is based on an inquiry of several years within Proxem, a software publisher specializing in big data processing. The book examines how data scientists explore, interpret and visualize our digital traces to make sense of them, and to produce new knowledge. Grounded in epistemology and science and technology studies, Big Data offers a reflection on data in general, and on how they help us to better understand reality and decide on our daily actions.
Big Data: Opportunities and challenges
by The Chartered Institute for IT, BCSDespite the hype around big data, there is no denying that its potential to benefit organisations, businesses and customers is enormous. The articles in this ebook aim to give practical guidance for all those who want to understand big data better and learn how to make the most of it. Topics range from big data analysis, mobile big data and managing unstructured data to technologies, governance and intellectual property and security issues surrounding big data.
Big Data: 9th CCF Conference, BigData 2021, Guangzhou, China, January 8–10, 2022, Revised Selected Papers (Communications in Computer and Information Science #1496)
by Li Wang Yang Gao Wei Zhao Yinghuan Shi Nong Xiao Dan Huang Xiangke Liao Enhong Chen Changdong WangThis book constitutes the proceedings of the 9th CCF Conference on Big Data, BigData 2021, held in Guangzhou, China, in January 2022. Due to the COVID-19 pandemic BigData 2021 was postponed to 2022. The 21 full papers presented in this volume were carefully reviewed and selected from 66 submissions. They present recent research on theoretical and technical aspects on big data, as well as on digital economy demands in big data applications.
Big Data: Promise, Application and Pitfalls
by Adrian Wilkinson John S. PedersenSince the early 2000s, digital data has transformed the way we live and work. This timely book looks to big data analytics to understand this revolutionary change, unpacking the impact of big data analytics on the mobilization and allocation of individuals, organizations and societies’ resources. Contributions from leading experts on modern technological trends examine the promises, applications and pitfalls of big data. The contributors assess the ways in which contemporary trajectories of data processing have increased efficiency and had a transformative effect on all avenues of life, from energy, tourism and social media, to human resources, welfare systems and urban citizenship. At a time when our personal data is more valuable than ever, this book seeks to make sense of how big data analytics has transformed our lives and how it will continue to shape society in the future. Astute and comprehensive, this book is critical reading for business and management scholars with a focus on information systems and communications technologies. It will also prove to be vital information for students and researchers of big data and digital society, as well as politics and administration more widely.
Big Data: 6th CCF Conference, Big Data 2018, Xi'an, China, October 11-13, 2018, Proceedings (Communications in Computer and Information Science #945)
by Zongben Xu Xinbo Gao Qiguang Miao Yunquan Zhang Jiajun BuThis volume constitutes the proceedings of the 6th CCF Conference, Big Data 2018, held in Xi'an, China, in October 2018. The 32 revised full papers presented in this volume were carefully reviewed and selected from 880 submissions. The papers are organized in topical sections on natural language processing and text mining; big data analytics and smart computing; big data applications; the application of big data in machine learning; social networks and recommendation systems; parallel computing and storage of big data; data quality control and data governance; big data system and management.
Big Data: Techniques and Technologies in Geoinformatics
Over the past decade, since the publication of the first edition, there have been new advances in solving complex geoinformatics problems. Advancements in computing power, computing platforms, mathematical models, statistical models, geospatial algorithms, and the availability of data in various domains, among other things, have aided in the automation of complex real-world tasks and decision-making that inherently rely on geospatial data. Of the many fields benefiting from these latest advancements, machine learning, particularly deep learning, virtual reality, and game engine, have increasingly gained the interest of many researchers and practitioners. This revised new edition provides up-to-date knowledge on the latest developments related to these three fields for solving geoinformatics problems.FEATURES Contains a comprehensive collection of advanced big data approaches, techniques, and technologies for geoinformatics problems Provides seven new chapters on deep learning models, algorithms, and structures, including a new chapter on how spatial metaverse is used to build immersive realistic virtual experiences Presents information on how deep learning is used for solving real-world geoinformatics problems This book is intended for researchers, academics, professionals, and students in such fields as computing and information, civil and environmental engineering, environmental sciences, geosciences, geology, geography, and urban studies.
Big Data – BigData 2018: 7th International Congress, Held as Part of the Services Conference Federation, SCF 2018, Seattle, WA, USA, June 25–30, 2018, Proceedings (Lecture Notes in Computer Science #10968)
by Francis Y. L. Chin C. L. Philip Chen Latifur Khan Kisung Lee Liang-Jie ZhangThis volume constitutes the proceedings of the 7th International Conference on BIGDATA 2018, held as Part of SCF 2018 in Seattle, WA, USA in June 2018.The 22 full papers together with 10 short papers published in this volume were carefully reviewed and selected from 97 submissions. They are organized in topical sections such as Data analysis, data as a service, services computing, data conversion, data storage, data centers, dataflow architectures, data compression, data exchange, data modeling, databases, and data management.
Big Data – BigData 2019: 8th International Congress, Held as Part of the Services Conference Federation, SCF 2019, San Diego, CA, USA, June 25–30, 2019, Proceedings (Lecture Notes in Computer Science #11514)
by Keke Chen Sangeetha Seshadri Liang-Jie ZhangThis volume constitutes the proceedings of the 8th International Congress on BIGDATA 2019, held as Part of SCF 2019 in San Diego, CA, USA in June 2019. The 9 full papers presented in this volume were carefully reviewed and selected from 14 submissions. They cover topics such as: Big Data Models and Algorithms; Big Data Architectures; Big Data Management; Big Data Protection, Integrity and Privacy; Security Applications of Big Data; Big Data Search and Mining; Big Data for Enterprise, Government and Society.
Big Data – BigData 2020: 9th International Conference, Held as Part of the Services Conference Federation, SCF 2020, Honolulu, HI, USA, September 18-20, 2020, Proceedings (Lecture Notes in Computer Science #12402)
by Surya Nepal Wenqi Cao Aziz Nasridinov MD Zakirul Alam Bhuiyan Xuan Guo Liang-Jie ZhangThis book constitutes the proceedings of the 9th International Conference on Big Data, BigData 2020, held as part of SCF 2020, during September 18-20, 2020. The conference was planned to take place in Honolulu, HI, USA and was changed to a virtual format due to the COVID-19 pandemic. The 16 full and 3 short papers presented were carefully reviewed and selected from 52 submissions. The topics covered are Big Data Architecture, Big Data Modeling, Big Data As A Service, Big Data for Vertical Industries (Government, Healthcare, etc.), Big Data Analytics, Big Data Toolkits, Big Data Open Platforms, Economic Analysis, Big Data for Enterprise Transformation, Big Data in Business Performance Management, Big Data for Business Model Innovations and Analytics, Big Data in Enterprise Management Models and Practices, Big Data in Government Management Models and Practices, and Big Data in Smart Planet Solutions.
Big Data – BigData 2022: 11th International Conference, Held as Part of the Services Conference Federation, SCF 2022, Honolulu, HI, USA, December 10–14, 2022, Proceedings (Lecture Notes in Computer Science #13730)
by Bo Hu Yunni Xia Yiwen Zhang Liang-Jie ZhangThis book constitutes the proceedings of the 11th International Conference on Big Data, BigData 2022, held as part of the Services Conference Federation, SCF 2022, held in Honolulu, HI, USA, in December 2022. The 4 full papers and 5 short papers presented in this volume were carefully reviewed and selected from 16 submissions.The 2022 International Congress on Big Data (BigData 2022) aims to provide an international forum that formally explores various business insights of all kinds of value-added "services". Big Data is a key enabler of exploring business insights and economics of services.
Big Data – BigData 2023: 12th International Conference, Held as Part of the Services Conference Federation, SCF 2023, Honolulu, HI, USA, September 23–26, 2023, Proceedings (Lecture Notes in Computer Science #14203)
by Shunli Zhang Bo Hu Liang-Jie ZhangThis book constitutes the refereed proceedings of the 12th International Conference, BigData 2023, Held as Part of the Services Conference Federation, SCF 2023, Honolulu, HI, USA, during September 23–26, 2023. The 14 full papers presented together with 2 short papers were carefully reviewed and selected from 27 submissions. The conference focuses on research track and application track.
Big Data 2.0 Processing Systems: A Survey (SpringerBriefs in Computer Science)
by Sherif SakrThis book provides readers the “big picture” and a comprehensive survey of the domain of big data processing systems. For the past decade, the Hadoop framework has dominated the world of big data processing, yet recently academia and industry have started to recognize its limitations in several application domains and big data processing scenarios such as the large-scale processing of structured data, graph data and streaming data. Thus, it is now gradually being replaced by a collection of engines that are dedicated to specific verticals (e.g. structured data, graph data, and streaming data). The book explores this new wave of systems, which it refers to as Big Data 2.0 processing systems. After Chapter 1 presents the general background of the big data phenomena, Chapter 2 provides an overview of various general-purpose big data processing systems that allow their users to develop various big data processing jobs for different application domains. In turn, Chapter 3 examines various systems that have been introduced to support the SQL flavor on top of the Hadoop infrastructure and provide competing and scalable performance in the processing of large-scale structured data. Chapter 4 discusses several systems that have been designed to tackle the problem of large-scale graph processing, while the main focus of Chapter 5 is on several systems that have been designed to provide scalable solutions for processing big data streams, and on other sets of systems that have been introduced to support the development of data pipelines between various types of big data processing jobs and systems. Lastly, Chapter 6 shares conclusions and an outlook on future research challenges. Overall, the book offers a valuable reference guide for students, researchers and professionals in the domain of big data processing systems. Further, its comprehensive content will hopefully encourage readers to pursue further research on the subject.
Big Data 2.0 Processing Systems: A Systems Overview
by Sherif SakrThis book provides readers the “big picture” and a comprehensive survey of the domain of big data processing systems. For the past decade, the Hadoop framework has dominated the world of big data processing, yet recently academia and industry have started to recognize its limitations in several application domains and thus, it is now gradually being replaced by a collection of engines that are dedicated to specific verticals (e.g. structured data, graph data, and streaming data). The book explores this new wave of systems, which it refers to as Big Data 2.0 processing systems. After Chapter 1 presents the general background of the big data phenomena, Chapter 2 provides an overview of various general-purpose big data processing systems that allow their users to develop various big data processing jobs for different application domains. In turn, Chapter 3 examines various systems that have been introduced to support the SQL flavor on top of the Hadoop infrastructure and provide competing and scalable performance in the processing of large-scale structured data. Chapter 4 discusses several systems that have been designed to tackle the problem of large-scale graph processing, while the main focus of Chapter 5 is on several systems that have been designed to provide scalable solutions for processing big data streams, and on other sets of systems that have been introduced to support the development of data pipelines between various types of big data processing jobs and systems. Next, Chapter 6 focuses on covering the emerging frameworks and systems in the domain of scalable machine learning and deep learning processing. Lastly, Chapter 7 shares conclusions and an outlook on future research challenges. This new and considerably enlarged second edition not only contains the completely new chapter 6, but also offers a refreshed content for the state-of-the-art in all domains of big data processing over the last years. Overall, the book offers a valuable reference guide for professional, students, and researchers in the domain of big data processing systems. Further, its comprehensive content will hopefully encourage readers to pursue further research on the subject.
Big Data, Algorithms and Food Safety: A Legal and Ethical Approach to Data Ownership and Data Governance (Law, Governance and Technology Series #52)
by Salvatore SapienzaThis book identifies the principles that should be applied when processing Big Data in the context of food safety risk assessments. Food safety is a critical goal in the protection of individuals’ right to health and the flourishing of the food and feed market. Big Data is fostering new applications capable of enhancing the accuracy of food safety risk assessments. An extraordinary amount of information is analysed to detect the existence or predict the likelihood of future risks, also by means of machine learning algorithms. Big Data and novel analysis techniques are topics of growing interest for food safety agencies, including the European Food Safety Authority (EFSA). This wealth of information brings with it both opportunities and risks concerning the extraction of meaningful inferences from data. However, conflicting interests and tensions among the parties involved are hindering efforts to find shared methods for steering the processing of Big Data in a sound, transparent and trustworthy way. While consumers call for more transparency, food business operators tend to be reluctant to share informational assets. This has resulted in a considerable lack of trust in the EU food safety system. A recent legislative reform, supported by new legal cases, aims to restore confidence in the risk analysis system by reshaping the meaning of data ownership in this domain. While this regulatory approach is being established, breakthrough analytics techniques are encouraging thinking about the next steps in managing food safety data in the age of machine learning.The book focuses on two core topics – data ownership and data governance – by evaluating how the regulatory framework addresses the challenges raised by Big Data and its analysis in an applied, significant, and overlooked domain. To do so, it adopts an interdisciplinary approach that considers both the technological advances and the policy tools adopted in the European Union, while also assuming an ethical perspective when exploring potential solutions. The conclusion puts forward a proposal: an ethical blueprint for identifying the principles – Security, Accountability, Fairness, Explainability, Transparency and Privacy – to be observed when processing Big Data for food safety purposes, including by means of machine learning. Possible implementations are then discussed, also in connection with two recent legislative proposals, namely the Data Governance Act and the Artificial Intelligence Act.