LEADER 04527nam 22005055 450 001 996418265703316 005 20231107223112.0 010 $a1-4614-8414-6 024 7 $a10.1007/978-1-4614-8414-1 035 $a(CKB)3710000001632623 035 $a(DE-He213)978-1-4614-8414-1 035 $a(PPN)242975399 035 $a(EXLCZ)993710000001632623 100 $a20190617d2020 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHandbook of Scan Statistics$b[electronic resource] /$fedited by Joseph Glaz, Markos V. Koutras 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2020. 215 $a1 online resource (L, 1400 p. 400 illus., 200 illus. in color.) 327 $aPreface -- I. History and Early Developments -- II. Methods and Techniques in Research and Scan Statistics -- III. One Dimensional Scan Statistics -- IV. Two and Three Dimensional Scan Statistics -- V. Biological Sciences -- VI. Biosurveillance and Reconnaissance -- VII. Engineering and Physical Sciences -- VIII. Ecology and Environmental Sciences -- IX. Information Sciences -- X. Medical Sciences -- XI. Public Health -- XII. Reliability and Quality Control -- XIII. Social Sciences -- XIV. Veterinary and Animal Science. 330 $aThe specialized field of scan statistics, fathered by Joseph Naus around 1999, burgeoned rapidly to prominence in the field of applied probability and statistics. In additional to challenging theoretical probelms, scan statistics has exciting applications in many areas of science and technologyu including archaeology, astronomy, physics, bioinformatics, and food sciences, just to name a few. In many fields, decision makers give a great deal of weight to clusters of events. Public Health investigators look for common cause factors to explain clusters of, for example, cancer. Molecular biologists look for palindrome clusters in DNA for clues as to the origin of replication viruses. Telecommunication engineers design capacity to accommodate clusters of calls being dialed simultaneously to a switchboard. Quality control experts investigate clusters of defects. The probabilities of different types of clusters  under various conditions are tools of the physical, natural, and social sciences. Scan statistics arise naturally in the scanning of time and space, seeking clusters of events. It is therefore no surprise that scan statistics is a major area of research in probability and statistics in the 21st century. In the last 5 years about 1600 hits appear on Google Scholar referencing the extensive activity in scan statistics and the breadth of the application. (Since 2010, about 482 hits are recorded in Google scholar.) The Handbook of Scan Statistics in two volumes is intended for researchers in probability and statistics and scientists in several areas including biology, engineering, health, medical, and social sciences. It will be of great value to graduate students in statistics and in all areas where scan statistics are used. 606 $aStatistics  606 $aBiostatistics 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 606 $aBiostatistics$3https://scigraph.springernature.com/ontologies/product-market-codes/L15020 606 $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030 606 $aStatistics for Social Sciences, Humanities, Law$3https://scigraph.springernature.com/ontologies/product-market-codes/S17040 606 $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17020 615 0$aStatistics . 615 0$aBiostatistics. 615 14$aStatistical Theory and Methods. 615 24$aBiostatistics. 615 24$aStatistics for Life Sciences, Medicine, Health Sciences. 615 24$aStatistics for Social Sciences, Humanities, Law. 615 24$aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 676 $a519.5 702 $aGlaz$b Joseph$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKoutras$b Markos V$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a996418265703316 996 $aHandbook of Scan Statistics$91734886 997 $aUNISA LEADER 03907nam 22006615 450 001 9910483686703321 005 20230810230933.0 010 $a3-662-62919-4 024 7 $a10.1007/978-3-662-62919-2 035 $a(CKB)4100000011716998 035 $a(DE-He213)978-3-662-62919-2 035 $a(MiAaPQ)EBC6458049 035 $a(PPN)253253004 035 $a(EXLCZ)994100000011716998 100 $a20210116d2021 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTransactions on Large-Scale Data- and Knowledge-Centered Systems XLVII $eSpecial Issue on Digital Ecosystems and Social Networks /$fedited by Abdelkader Hameurlain, A Min Tjoa, Richard Chbeir 205 $a1st ed. 2021. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2021. 215 $a1 online resource (XIII, 235 p. 124 illus., 64 illus. in color.) 225 1 $aTransactions on Large-Scale Data- and Knowledge-Centered Systems,$x2510-4942 ;$v12630 311 $a3-662-62918-6 327 $aMapping Experience Ecosystems as Emergent Actor-Created Spaces -- A Semantic-Based Strategy to Model Multimedia Social Networks -- Social Big Data: Concepts and Theory -- Social Big Data: Case Studies -- Data Analysis in Social Networks: A Case Study -- Smart Services Using Voice and Images -- Big Spatial and Spatio-Temporal Data Analytics Systems -- Cloud-Based e-Feedback Services Using Performance Analysis: A Linear Approach -- Semantic-Based Automatic Generation of Reconfigurable Distributed Mobile Applications in Pervasive Environments. 330 $aThe LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 47th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, constitutes a special issue focusing on Digital Ecosystems and Social Networks. The 9 revised selected papers cover topics that include Social Big Data, Data Analysis, Cloud-Based Feedback, Experience Ecosystems, Pervasive Environments, and Smart Systems. 410 0$aTransactions on Large-Scale Data- and Knowledge-Centered Systems,$x2510-4942 ;$v12630 606 $aArtificial intelligence$xData processing 606 $aApplication software 606 $aComputer networks 606 $aData structures (Computer science) 606 $aInformation theory 606 $aData Science 606 $aComputer and Information Systems Applications 606 $aComputer Communication Networks 606 $aData Structures and Information Theory 615 0$aArtificial intelligence$xData processing. 615 0$aApplication software. 615 0$aComputer networks. 615 0$aData structures (Computer science). 615 0$aInformation theory. 615 14$aData Science. 615 24$aComputer and Information Systems Applications. 615 24$aComputer Communication Networks. 615 24$aData Structures and Information Theory. 676 $a005.74 702 $aHameurlain$b Abdelkader 702 $aTjoa$b A Min 702 $aChbeir$b Richard 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a9910483686703321 996 $aTransactions on large-scale data- and knowledge-centered systems XLVII$92809753 997 $aUNINA