Stream Data Processing: A Quality of Service Perspective (Hardcover)

Sharma Chakravarthy

ISBN 10: 0387710027 / ISBN 13: 9780387710020

Editore: Springer-Verlag New York Inc., New York, NY, 2009

Riguardo questo articolo

Hardcover. 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. In recent years, a new class of applications has come to the forefront { p- marily due to the advancement in our ability to collect data from multitudes of devices, and process them e ciently. These - plications need to process data continuously (and as long as data is available) from one or more sources. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Codice articolo 9780387710020

Dati bibliografici

• Titolo: Stream Data Processing: A Quality of Service Perspective (Hardcover)
• Editore: Springer-Verlag New York Inc., New York, NY
• Data di pubblicazione: 2009
• Lingua: Inglese
• ISBN 10: 0387710027
• ISBN 13: 9780387710020
• Rilegatura: Hardcover
• Condizione: new

Descrizione articolo

Riassunto

In recent years, a new class of applications has come to the forefront { p- marily due to the advancement in our ability to collect data from multitudes of devices, and process them e ciently. These include homeland security - plications, sensor/pervasive computing applications, various kinds of mo- toring applications, and even traditional applications belonging to nancial, computer network management, and telecommunication domains. These - plications need to process data continuously (and as long as data is available) from one or more sources. The sequence of data items continuously gen- ated by sources is termed a data stream. Because of the possible never-ending nature of a data stream, the amount of data to be processed is likely to be unbounded. In addition, timely detection of interesting changes or patterns or aggregations over incoming data is critical for many of these applications. Furthermore, the data arrival rates may uctuate over a period of time and may be bursty at times. For most of these applications, Quality of Service (or QoS) requirements, such as response time, memory usage, and throughput are extremely important. These application requirements make it infeasible to simply load the incoming data streams into a persistent store and process them e ectively using currently available database management techniques.

Dalla quarta di copertina

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.