Data Streams
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Data Streams
Author: Charu C. Aggarwal
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-04-03
This book primarily discusses issues related to the mining aspects of data streams and it is unique in its primary focus on the subject. This volume covers mining aspects of data streams comprehensively: each contributed chapter contains a survey on the topic, the key ideas in the field for that particular topic, and future research directions. The book is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for advanced-level students in computer science.
Data Stream Management
This volume focuses on the theory and practice of data stream management, and the novel challenges this emerging domain poses for data-management algorithms, systems, and applications. The collection of chapters, contributed by authorities in the field, offers a comprehensive introduction to both the algorithmic/theoretical foundations of data streams, as well as the streaming systems and applications built in different domains. A short introductory chapter provides a brief summary of some basic data streaming concepts and models, and discusses the key elements of a generic stream query processing architecture. Subsequently, Part I focuses on basic streaming algorithms for some key analytics functions (e.g., quantiles, norms, join aggregates, heavy hitters) over streaming data. Part II then examines important techniques for basic stream mining tasks (e.g., clustering, classification, frequent itemsets). Part III discusses a number of advanced topics on stream processing algorithms, and Part IV focuses on system and language aspects of data stream processing with surveys of influential system prototypes and language designs. Part V then presents some representative applications of streaming techniques in different domains (e.g., network management, financial analytics). Finally, the volume concludes with an overview of current data streaming products and new application domains (e.g. cloud computing, big data analytics, and complex event processing), and a discussion of future directions in this exciting field. The book provides a comprehensive overview of core concepts and technological foundations, as well as various systems and applications, and is of particular interest to students, lecturers and researchers in the area of data stream management.
Nomenclature of Datastreams, Mining Strategies, Concept Drift and Research Objectives
Author: Dr. Annaluri Sreenivasa Rao
language: en
Publisher: Shineeks Publishers
Release Date: 2022-03-16
Streaming data is one of the primary sources of what is known as big data. While data streams and big data have gotten a lot of attention in the recent decade, many research methodologies are often intended for well-behaved controlled problem settings, overlooking major obstacles given by real-world applications. The eight open difficulties for data stream mining are discussed in this book. Our goal is to discover gaps between present research and useful applications, to highlight unresolved issues, and to create new data stream mining research lines that are relevant to applications.
