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.
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