| Stream Data Processing: A Quality of Service Perspective: Modeling, Scheduling, Load Shedding, and Complex Event Processing 2009 Edition Contributor(s): Chakravarthy, Sharma (Author), Jiang, Qingchun (Author) |
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ISBN: 0387710027 ISBN-13: 9780387710020 Publisher: Springer OUR PRICE: $161.49 Product Type: Hardcover - Other Formats Published: May 2009 Annotation: 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. |
| Additional Information |
| BISAC Categories: - Computers | Databases - General - Computers | Networking - Hardware - Computers | System Administration - Storage & Retrieval |
| Dewey: 005.74 |
| LCCN: 2009920957 |
| Series: Advances in Database Systems |
| Physical Information: 0.8" H x 6.2" W x 9.3" (1.35 lbs) 324 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. |