Traditional data-management systems software is built on the concept of persistent data sets that are stored reliably in stable storage and queried\/updated several times throughout their lifetime. For several emerging application domains, however, data arrives and needs to be processed on a continuous basis, without the benefit of several passes over a static, persistent data image. Such continuous data streams arise naturally, for instance telecom and IP network monitoring. This volume focuses on the theory and practice of data stream management, and the difficult, novel challenges this emerging domain introduces for data-management systems. The collection of chapters (contributed by authorities in the field) offers a comprehensive introduction to both the algorithmic\/theoretical foundations of data streams and the streaming systems\/applications built in different domains. In the remainder of this introductory chapter, we provide a brief summary of some basic data streaming concepts and models, and discuss the key elements of a generic stream query processing architecture. We then give a short overview of the contents of this volume.<\/p>\n","protected":false},"excerpt":{"rendered":"
Traditional data-management systems software is built on the concept of persistent data sets that are stored reliably in stable storage and queried\/updated several times throughout their lifetime. For several emerging application domains, however, data arrives and needs to be processed on a continuous basis, without the benefit of several passes over a static, persistent data 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