LEADER 04100nam 22007095 450 001 9910299226203321 005 20230810184412.0 010 $a3-319-17482-7 024 7 $a10.1007/978-3-319-17482-2 035 $a(CKB)3710000000416809 035 $a(SSID)ssj0001501022 035 $a(PQKBManifestationID)11848525 035 $a(PQKBTitleCode)TC0001501022 035 $a(PQKBWorkID)11521645 035 $a(PQKB)10753933 035 $a(DE-He213)978-3-319-17482-2 035 $a(MiAaPQ)EBC5579605 035 $a(MiAaPQ)EBC6284295 035 $a(Au-PeEL)EBL5579605 035 $a(OCoLC)909886944 035 $a(Au-PeEL)EBL6284295 035 $a(PPN)186031009 035 $a(EXLCZ)993710000000416809 100 $a20150506d2015 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aProcess Mining Techniques in Business Environments $eTheoretical Aspects, Algorithms, Techniques and Open Challenges in Process Mining /$fby Andrea Burattin 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (XII, 220 p. 101 illus.) 225 1 $aLecture Notes in Business Information Processing,$x1865-1356 ;$v207 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-319-17481-9 327 $a1 Introduction -- Part I: State of the Art: BPM, Data Mining and Process Mining -- 2 Introduction to Business Processes, BPM, and BPM Systems -- 3 Data Generated by Information Systems (and How to Get It) -- 4 Data Mining for Information System Data -- 5 Process Mining -- 6 Quality Criteria in Process Mining -- 7 Event Streams -- Part II: Obstacles to Process Mining in Practice -- 8 Obstacles to Applying Process Mining in Practice -- 9 Long-term View Scenario -- Part III: Process Mining as an Emerging Technology -- 10 Data Preparation -- 11 Heuristics Miner for Time Interval -- 12 Automatic Configuration of Mining Algorithm -- 13 User-Guided Discovery of Process Models -- 14 Extensions of Business Processes with Organizational Roles -- 15 Results Interpretation and Evaluation -- 16 Hands-On: Obtaining Test Data -- Part IV: A New Challenge in Process Mining -- 17 Process Mining for Stream Data Sources -- Part V: Conclusions and Future Work -- 18 Conclusions and Future Work. 330 $aAfter a brief presentation of the state of the art of process-mining techniques, Andrea Burratin proposes different scenarios for the deployment of process-mining projects, and in particular a characterization of companies in terms of their process awareness. The approaches proposed in this book belong to two different computational paradigms: first to classic "batch process mining," and second to more recent "online process mining." The book encompasses a revised version of the author's PhD thesis, which won the "Best Process Mining Dissertation Award" in 2014, awarded by the IEEE Task Force on Process Mining. 410 0$aLecture Notes in Business Information Processing,$x1865-1356 ;$v207 606 $aData mining 606 $aInformation technology$xManagement 606 $aPattern recognition systems 606 $aData Mining and Knowledge Discovery 606 $aBusiness Process Management 606 $aComputer Application in Administrative Data Processing 606 $aAutomated Pattern Recognition 615 0$aData mining. 615 0$aInformation technology$xManagement. 615 0$aPattern recognition systems. 615 14$aData Mining and Knowledge Discovery. 615 24$aBusiness Process Management. 615 24$aComputer Application in Administrative Data Processing. 615 24$aAutomated Pattern Recognition. 676 $a006.312 700 $aBurattin$b Andrea$4aut$4http://id.loc.gov/vocabulary/relators/aut$0849597 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299226203321 996 $aProcess Mining Techniques in Business Environments$91897187 997 $aUNINA