LEADER 03652nam 22005175 450 001 9910299163703321 005 20200705212625.0 010 $a3-030-02101-7 024 7 $a10.1007/978-3-030-02101-6 035 $a(CKB)4100000007110871 035 $a(DE-He213)978-3-030-02101-6 035 $a(MiAaPQ)EBC5628041 035 $a(PPN)231462395 035 $a(EXLCZ)994100000007110871 100 $a20181031d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMobile Data Mining /$fby Yuan Yao, Xing Su, Hanghang Tong 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (IX, 58 p. 22 illus. in color.) 225 1 $aSpringerBriefs in Computer Science,$x2191-5768 311 $a3-030-02100-9 327 $a1 Introduction -- 2 Data Capturing and Processing -- 3 Feature Engineering -- 4 Hierarchical Model -- 5 Personalized Model -- 6 Online Model -- 7 Conclusions. 330 $aThis SpringerBrief presents a typical life-cycle of mobile data mining applications, including: data capturing and processing which determines what data to collect, how to collect these data, and how to reduce the noise in the data based on smartphone sensors feature engineering which extracts and selects features to serve as the input of algorithms based on the collected and processed data model and algorithm design In particular, this brief concentrates on the model and algorithm design aspect, and explains three challenging requirements of mobile data mining applications: energy-saving, personalization, and real-time Energy saving is a fundamental requirement of mobile applications, due to the limited battery capacity of smartphones. The authors explore the existing practices in the methodology level (e.g. by designing hierarchical models) for saving energy. Another fundamental requirement of mobile applications is personalization. Most of the existing methods tend to train generic models for all users, but the authors provide existing personalized treatments for mobile applications, as the behaviors may differ greatly from one user to another in many mobile applications. The third requirement is real-time. That is, the mobile application should return responses in a real-time manner, meanwhile balancing effectiveness and efficiency. This SpringerBrief targets data mining and machine learning researchers and practitioners working in these related fields. Advanced level students studying computer science and electrical engineering will also find this brief useful as a study guide. . 410 0$aSpringerBriefs in Computer Science,$x2191-5768 606 $aComputers 606 $aComputer networks 606 $aInformation Systems and Communication Service$3https://scigraph.springernature.com/ontologies/product-market-codes/I18008 606 $aComputer Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13022 615 0$aComputers. 615 0$aComputer networks. 615 14$aInformation Systems and Communication Service. 615 24$aComputer Communication Networks. 676 $a005.7 676 $a006.312 700 $aYao$b Yuan$4aut$4http://id.loc.gov/vocabulary/relators/aut$0999985 702 $aSu$b Xing$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aTong$b Hanghang$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910299163703321 996 $aMobile Data Mining$92295502 997 $aUNINA