000 04009nam a22005535i 4500
001 978-3-030-02101-6
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008 181031s2018 gw | s |||| 0|eng d
020 _a9783030021016
_9978-3-030-02101-6
050 4 _aQA75.5-76.95
072 7 _aUT
_2bicssc
072 7 _aCOM069000
_2bisacsh
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_2thema
082 0 4 _a005.7
_223
100 1 _aYao, Yuan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aMobile Data Mining
_h[electronic resource] /
_cby Yuan Yao, Xing Su, Hanghang Tong.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aIX, 58 p. 22 illus. in color.
_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
500 _aAcceso multiusuario
505 0 _a1 Introduction -- 2 Data Capturing and Processing -- 3 Feature Engineering -- 4 Hierarchical Model -- 5 Personalized Model -- 6 Online Model -- 7 Conclusions.
520 _aThis SpringerBrief presents a typical life-cycle of mobile data mining applications, including: data capturing and processing which determines what data to collect, how to collect these data, and how to reduce the noise in the data based on smartphone sensors feature engineering which extracts and selects features to serve as the input of algorithms based on the collected and processed data model and algorithm design In particular, this brief concentrates on the model and algorithm design aspect, and explains three challenging requirements of mobile data mining applications: energy-saving, personalization, and real-time Energy saving is a fundamental requirement of mobile applications, due to the limited battery capacity of smartphones. The authors explore the existing practices in the methodology level (e.g. by designing hierarchical models) for saving energy. Another fundamental requirement of mobile applications is personalization. Most of the existing methods tend to train generic models for all users, but the authors provide existing personalized treatments for mobile applications, as the behaviors may differ greatly from one user to another in many mobile applications. The third requirement is real-time. That is, the mobile application should return responses in a real-time manner, meanwhile balancing effectiveness and efficiency. This SpringerBrief targets data mining and machine learning researchers and practitioners working in these related fields. Advanced level students studying computer science and electrical engineering will also find this brief useful as a study guide. .
541 _fUABC ;
_cTemporal ;
_d01/01/2021-12/31/2023.
650 0 _aComputers.
650 0 _aComputer communication systems.
650 1 4 _aInformation Systems and Communication Service.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I18008
650 2 4 _aComputer Communication Networks.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I13022
700 1 _aSu, Xing.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aTong, Hanghang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030021009
776 0 8 _iPrinted edition:
_z9783030021023
830 0 _aSpringerBriefs in Computer Science,
_x2191-5768
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-3-030-02101-6
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cLIBRO_ELEC
999 _c242081
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