000 03537nam a22004935i 4500
999 _c200434257
_d52469
003 DE-He213
005 20231104114336.0
007 cr nn 008mamaa
008 150707s2015 gw | s |||| 0|eng d
020 _a9783319212579
_z978-3-319-21257-9
024 7 _a10.1007/978-3-319-21257-9
_2doi
040 _aTR-AnTOB
_beng
_cTR-AnTOB
_erda
050 4 _aQA75.5-76.95
072 7 _aUNH
_2bicssc
072 7 _aCOM030000
_2bisacsh
072 7 _aUNH
_2thema
072 7 _aUND
_2thema025.04
_223
100 1 _aP, Deepak.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aOperators for Similarity Search :
_bSemantics, Techniques and Usage Scenarios /
_cby Deepak P, Prasad M. Deshpande.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 0 _aSpringerBriefs in Computer Science,
_x2191-5768
505 0 _a1 Introduction -- 2 Fundamentals of Similarity Search -- 3 Common Similarity Search Operators -- 4 Categorizing Operators -- 5 Advanced Operators for Similarity Search -- 6 Indexing for Similarity Search Operators -- 7 The Road Ahead.
520 _aThis book provides a comprehensive tutorial on similarity operators. The authors systematically survey the set of similarity operators, primarily focusing on their semantics, while also touching upon mechanisms for processing them effectively. The book starts off by providing introductory material on similarity search systems, highlighting the central role of similarity operators in such systems. This is followed by a systematic categorized overview of the variety of similarity operators that have been proposed in literature over the last two decades, including advanced operators such as RkNN, Reverse k-Ranks, Skyline k-Groups and K-N-Match. Since indexing is a core technology in the practical implementation of similarity operators, various indexing mechanisms are summarized. Finally, current research challenges are outlined, so as to enable interested readers to identify potential directions for future investigations. In summary, this book offers a comprehensive overview of the field of similarity search operators, allowing readers to understand the area of similarity operators as it stands today, and in addition providing them with the background needed to understand recent novel approaches.
650 0 _aInformation storage and retrieva.
650 0 _aComputational complexity.
650 0 _aArtificial intelligence.
650 0 _aData mining.
650 1 4 _aInformation Storage and Retrieval.
_0http://scigraph.springernature.com/things/product-market-codes/I18032
650 2 4 _aDiscrete Mathematics in Computer Science.
_0http://scigraph.springernature.com/things/product-market-codes/I17028
650 2 4 _aArtificial Intelligence.
_0http://scigraph.springernature.com/things/product-market-codes/I21000
650 2 4 _aData Mining and Knowledge Discovery.
_0http://scigraph.springernature.com/things/product-market-codes/I18030
700 1 _aDeshpande, Prasad M.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
856 4 0 _uhttps://doi.org/10.1007/978-3-319-21257-9
_3Springer eBooks
_zOnline access link to the resource
942 _2lcc
_cEBK
041 _aeng