LEADER 03586oam 2200493 450 001 9910298979403321 005 20190911103511.0 010 $a1-4471-5454-1 024 7 $a10.1007/978-1-4471-5454-9 035 $a(OCoLC)869771702 035 $a(MiFhGG)GVRL6YPK 035 $a(EXLCZ)992670000000427457 100 $a20130709d2014 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aData mining techniques in sensor networks $esummarization, interpolation and surveillance /$fAnnalisa Appice [and four others] 205 $a1st ed. 2014. 210 1$aLondon :$cSpringer,$d2014. 215 $a1 online resource (xiii, 105 pages) $cillustrations (some color) 225 1 $aSpringerBriefs in Computer Science,$x2191-5768 300 $a"ISSN: 2191-5768." 311 $a1-4471-5453-3 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Sensor Networks and Data Streams: Basics -- Geodata Stream Summarization -- Missing Sensor Data Interpolation -- Sensor Data Surveillance -- Sensor Data Analysis Applications. 330 $aEmerging real life applications, such as environmental compliance, ecological studies and meteorology, are characterized by real-time data acquisition through a number of (wireless) remote sensors. Operatively, remote sensors are installed across a spatially distributed network; they gather information along a number of attribute dimensions and periodically feed a central server with the measured data. The server is required to monitor these data, issue possible alarms or compute fast aggregates. As data analysis requests, which are submitted to a server, may concern both present and past data, the server is forced to store the entire stream. But, in the case of massive streams (large networks and/or frequent transmissions), the limited storage capacity of a server may impose to reduce the amount of data stored on the disk.  One solution to address the storage limits is to compute summaries of the data as they arrive and use these summaries to interpolate the real data which are discarded instead.  On any future demands of further analysis of the discarded data, the server pieces together the data from the summaries stored in database and processes them according to the requests. This work introduces the multiple possibilities and facets of a recently defined spatio-temporal pattern, called trend cluster, and its applications to summarize, interpolate and identify anomalies in a sensor network.   As an example application, the authors illustrate the application of trend cluster discovery to monitor the efficiency of photovoltaic power plants. The work closes with remarks on new possibilities for surveillance gained by recent developments of sensing technology, and with an outline of future challenges. 410 0$aSpringerBriefs in computer science. 606 $aData mining 606 $aSensor networks 615 0$aData mining. 615 0$aSensor networks. 676 $a004.6 700 $aAppice$b Annalisa$4aut$4http://id.loc.gov/vocabulary/relators/aut$0994068 702 $aCiampi$b Anna$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aFumarola$b Fabio$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aMalerba$b Donato$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiFhGG 801 1$bMiFhGG 906 $aBOOK 912 $a9910298979403321 996 $aData Mining Techniques in Sensor Networks$92276653 997 $aUNINA