000			03552nam a22005535i 4500
001			978-3-319-02408-0
003			DE-He213
005			20200421112226.0
007			cr nn 008mamaa
008			131015s2013 gw | s |||| 0|eng d
020			_a9783319024080 _9978-3-319-02408-0
024	7		_a10.1007/978-3-319-02408-0 _2doi
050		4	_aQA76.9.D3
072		7	_aUN _2bicssc
072		7	_aUMT _2bicssc
072		7	_aCOM021000 _2bisacsh
082	0	4	_a005.74 _223
100	1		_aVieira, Marcos R. _eauthor.
245	1	0	_aSpatio-Temporal Databases _h[electronic resource] : _bComplex Motion Pattern Queries / _cby Marcos R. Vieira, Vassilis J. Tsotras.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2013.
300			_aXIII, 114 p. 46 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Computer Science, _x2191-5768
505	0		_aIntroduction -- Flexible Pattern Queries -- Pattern Queries for Mobile Phone-Call Databases -- Flock Pattern Queries -- Diversified Pattern Queries -- Conclusion.
520			_aThis brief presents several new query processing techniques, called complex motion pattern queries, specifically designed for very large spatio-temporal databases of moving objects. The brief begins with the definition of flexible pattern queries, which are powerful because of the integration of variables and motion patterns. This is followed by a summary of the expressive power of patterns and flexibility of pattern queries. The brief then present the Spatio-Temporal Pattern System (STPS) and density-based pattern queries. STPS databases contain millions of records with information about mobile phone calls and are designed around cellular towers and places of interest. Density-based pattern queries capture the aggregate behavior of trajectories as groups. Several evaluation algorithms are presented for finding groups of trajectories that move together in space and time, i.e. within a predefined distance to each other. Finally, the brief describes a generic framework, called DivDB, for diversifying query results. Two new evaluation methods, as well as several existing ones, are described and tested in the proposed DivDB framework. The efficiency and effectiveness of all the proposed complex motion pattern queries are demonstrated through an extensive experimental evaluation using real and synthetic spatio-temporal databases. This clear evaluation of new query processing techniques makes Spatio-Temporal Database a valuable resource for professionals and researchers studying databases, data mining, and pattern recognition.
650		0	_aComputer science.
650		0	_aDatabase management.
650		0	_aData mining.
650		0	_aPattern recognition.
650		0	_aRegional economics.
650		0	_aSpatial economics.
650	1	4	_aComputer Science.
650	2	4	_aDatabase Management.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aPattern Recognition.
650	2	4	_aRegional/Spatial Science.
700	1		_aTsotras, Vassilis J. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319024073
830		0	_aSpringerBriefs in Computer Science, _x2191-5768
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-319-02408-0
912			_aZDB-2-SCS
942			_cEBK
999			_c57664 _d57664