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Advances in Database Systems #36: Stream Data Processing: A Quality of Service Perspective: Modeling, Scheduling, Load Shedding, and Complex Event Processingby Sharma Chakravarthy
Synopses & Reviews
Traditional database management systems, widely used today, are not well-suited for a class of emerging applications. These applications, such as network management, sensor computing, and so on, need to continuously process large amounts of data coming in the form of a stream and in addition, meet stringent response time requirements. Support for handling QoS metrics, such as response time, memory usage, and throughput, is central to any system proposed for the above applications. Stream Data Processing: A Quality of Service Perspective (Modeling,Scheduling, Load Shedding, and Complex Event Processing), presents a new paradigm suitable for stream and complex event processing. This book covers a broad range of topics in stream data processing and includes detailed technical discussions of a number of proposed techniques from QoS perspective. This volume is intended as a text book for graduate courses and as a reference book for researchers, advanced-level students in computer sciences, and IT practitioners.
The systems used to process data streams and provide for the needs of stream-based applications are Data Stream Management Systems (DSMSs). This book presents a new paradigm to meet the needs of these applications, including a detailed discussion of the techniques proposed. Ii includes important aspects of a QoS-driven DSMS (Data Stream Management System) and introduces applications where a DSMS can be used and discusses needs beyond the stream processing model. It also discusses in detail the design and implementation of MavStream. This volume is primarily intended as a reference book for researchers and advanced-level students in computer science. It is also appropriate for practitioners in industry who are interested in developing applications.
Traditional database management systems (DBMSs) are widely used in applications that require persistent storage and processing of ad hoc queries to manage and process a large volume of data. A large class of newer applications ??? in finance, computer network management, telecommunications, homeland security, sensor/pervasive computing, and environmental monitoring ??? produce data continuously and the data is typically presented in a data stream. The systems used to process data streams and provide for the needs of stream-based applications are Data Stream Management Systems (DSMSs). Stream Data Processing: Issues and Solutions presents a new paradigm to meet the needs of these applications, including a detailed discussion of the techniques proposed. This volume is intended as a reference book for researchers and advanced-level students in computer science. It is also appropriate for practitioners in industry who are interested in developing applications.
Table of Contents
Preface.- Introduction.- Overview of Data Stream Processing.- DSMS Challenges.- Literature Review.- Modeling Continuous Queries over Data Streams.- Scheduling Strategies for CQs.- Load Shedding in Data Stream Management Systems.- NFM-i: An Inter-Domain Fault Management System.- Architecture for Integrating Stream and Complex Event Processing.- MavStream: Design and Implementation of a DSMS Prototype.- MavEStream: Design and Integration of CEP with a DSMS.- Conclusions and Open Problems.- References.- Index.
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