000			01775nam a2200217 a 4500
001			00025017
008			120425s2011 xxu eng d
020			_a3846509272 (paperback)
020			_a9783846509272 (paperback)
082	0	4	_a004 _222
100	1		_aElmongui, Hicham.
245	1	0	_aStreamlined mapreduce : _bmassively parallel processing of data streams / _cHicham Elmongui.
260			_a[S.l.] : _bLAP LAMBERT Academic Publishing, _c2011.
300			_a180 p. ; _c22 cm.
520			_aCritical applications affect human lives, their safety and their privacy. The navigation of emergency services or fire trucks would be efficient if traffic jams are avoided. Proactive disaster control would be possible with automated traffic surveillance. Several critical applications need an infrastructure that provides efficient processing of real-time data, which enables the provisioning of useful pieces of information in real-time. The first step into building such an infrastructure is to provide for the massively parallel processing of streamed data, which is the core of this book. In this book, we describe the design and implementation of a stream-based distributed processing system for continuous queries. Inspired by Google's MapReduce programming model running on Google File System, we build a distributed stream system and an in-memory MapReduce runtime environment to enable developers post their continuous queries on data streams to be processed in real time.
650		0	_aDatabase management.
650		0	_aCloud computing _xProgramming
999			_c12094 _d12094
952			_p3010025017 _40 _ekarim International _00 _bBRACUL _10 _o004 ELM _d2012-04-25 _t1 _70 _cGEN _2ddc _g7200.00 _yBK _aBRACUL