03469nam a22005175i 4500001001800000003000900018005001700027007001500044008004100059020001800100020001900118024002500137040000900162082001500171100003300186245011600219264003800335300003400373336002600407337002600433338003900459347002400498490005100522505041200573520125300985650002202238650003702260650002502297650004702322650002502369650002402394650002202418650002502440650003902465650003602504650003702540650002702577650005402604700002602658700003302684710003402717773002002751776003602771830005102807856009302858978-0-387-25229-2DE-He21320260521091834.0cr nn 008mamaa100301s2005    xxu|    s    |||| 0|eng d  a9780387252292  a997803872522927 a10.1007/b1069682doi  cCICY04a005.742231 aChaudhry, Nauman A.eeditor.10aStream Data Managementh[recurso electrónico] /cedited by Nauman A. Chaudhry, Kevin Shaw, Mahdi Abdelguerfi. 1aBoston, MA :bSpringer US,c2005.  aXIV, 170 p.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  arecurso en líneabcr2rdacarrier  atext filebPDF2rda1 aAdvances in Database Systems,x1386-2944 ;v300 ato Stream Data Management -- Query Execution and Optimization -- Filtering, Punctuation, Windows and Synopses -- XML & Data Streams -- CAPE: A Constraint-Aware Adaptive Stream Processing Engine -- Efficient Support for Time Series Queries in Data Stream Management Systems -- Managing Distributed Geographical Data Streams with the GIDB Portal System -- Streaming Data Dissemination Using Peer-Peer Systems.  aResearchers in data management have recently recognized the importance of a new class of data-intensive applications that requires managing data streams, i.e., data composed of continuous, real-time sequence of items. Streaming applications pose new and interesting challenges for data management systems. Such application domains require queries to be evaluated continuously as opposed to the one time evaluation of a query for traditional applications. Streaming data sets grow continuously and queries must be evaluated on such unbounded data sets. These, as well as other challenges, require a major rethink of almost all aspects of traditional database management systems to support streaming applications. Stream Data Management comprises eight invited chapters by researchers active in stream data management. The collected chapters provide exposition of algorithms, languages, as well as systems proposed and implemented for managing streaming data. Stream Data Management is designed to appeal to researchers or practitioners already involved in stream data management, as well as to those starting out in this area. This book is also suitable for graduate students in computer science interested in learning about stream data management.  0aCOMPUTER SCIENCE. 0aCOMPUTER COMMUNICATION NETWORKS. 0aDATABASE MANAGEMENT. 0aINFORMATION STORAGE AND RETRIEVAL SYSTEMS. 0aINFORMATION SYSTEMS. 0aMULTIMEDIA SYSTEMS.14aCOMPUTER SCIENCE.24aDATABASE MANAGEMENT.24aINFORMATION STORAGE AND RETRIEVAL.24aMULTIMEDIA INFORMATION SYSTEMS.24aCOMPUTER COMMUNICATION NETWORKS.24aMODELS AND PRINCIPLES.24aINFORMATION SYSTEMS APPLICATIONS (INCL.INTERNET).1 aShaw, Kevin.eeditor.1 aAbdelguerfi, Mahdi.eeditor.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9780387243931 0aAdvances in Database Systems,x1386-2944 ;v3040uhttp://dx.doi.org/10.1007/b106968zVer el texto completo en las instalaciones del CICY