LEADER 00756nam0-22002891i-450- 001 990001323290403321 010 $a0-937175-75-7 035 $a000132329 035 $aFED01000132329 035 $a(Aleph)000132329FED01 035 $a000132329 100 $a20000920d1991----km-y0itay50------ba 101 0 $aeng 200 1 $aManaging NFS and NIS$fHal Stern. 210 $aSebastopol (CA)$cO'Reilly & Assoc.$dc1991. 215 $aXXIV, 410 p.$d23 cm 610 0 $aReti di computers 676 $a004.6 700 1$aStern,$bHal$059997 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990001323290403321 952 $a124-C-8$b11134$fMA1 959 $aMA1 996 $aManaging NFS and NIS$9382909 997 $aUNINA DB $aING01 LEADER 03717nam 22006135 450 001 9910299162103321 005 20200701030910.0 010 $a3-319-96116-0 024 7 $a10.1007/978-3-319-96116-3 035 $a(CKB)4100000005958352 035 $a(MiAaPQ)EBC5497844 035 $a(DE-He213)978-3-319-96116-3 035 $a(PPN)229917917 035 $a(EXLCZ)994100000005958352 100 $a20180823d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMobile Big Data /$fby Xiang Cheng, Luoyang Fang, Liuqing Yang, Shuguang Cui 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (132 pages) 225 1 $aWireless Networks,$x2366-1186 311 $a3-319-96115-2 327 $a1 Mobile Big Data -- 2 Source and Collection -- 3 Transmission -- 4 Computing -- 5 Applications -- 6 Case Study: Demand Forecasting for Predictive Network Managements -- 7 Case Study: User Identification for Mobile Privacy. 330 $aThis book provides a comprehensive picture of mobile big data starting from data sources to mobile data driven applications. Mobile Big Data comprises two main components: an overview of mobile big data, and the case studies based on real-world data recently collected by one of the largest mobile network carriers in China. In the first component, four areas of mobile big data life cycle are surveyed: data source and collection, transmission, computing platform and applications. In the second component, two case studies are provided, based on the signaling data collected in the cellular core network in terms of subscriber privacy evaluation and demand forecasting for network management. These cases respectively give a vivid demonstration of what mobile big data looks like, and how it can be analyzed and mined to generate useful and meaningful information and knowledge. This book targets researchers, practitioners and professors relevant to this field. Advanced-level students studying computer science and electrical engineering will also be interested in this book as supplemental reading. . 410 0$aWireless Networks,$x2366-1186 606 $aComputer networks 606 $aData mining 606 $aElectrical engineering 606 $aArtificial intelligence 606 $aComputer Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13022 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputer networks. 615 0$aData mining. 615 0$aElectrical engineering. 615 0$aArtificial intelligence. 615 14$aComputer Communication Networks. 615 24$aData Mining and Knowledge Discovery. 615 24$aCommunications Engineering, Networks. 615 24$aArtificial Intelligence. 676 $a005.7 700 $aCheng$b Xiang$4aut$4http://id.loc.gov/vocabulary/relators/aut$0788052 702 $aFang$b Luoyang$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aYang$b Liuqing$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aCui$b Shuguang$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910299162103321 996 $aMobile Big Data$92262044 997 $aUNINA