- Full Description
Traditional database management systems, widely used today, are not well-suited for a class of emerging applications, such as computer network management, homeland security, sensor computing, and environmental monitoring. These applications need to continuously process large amounts of data coming in the form of a stream, and 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. This volume is intended as a textbook for graduate courses and as a reference book for researchers, advanced-level students in CS, and IT practitioners.
- Table of Contents
Table of Contents
- 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.
- 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.
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