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020 _a9783319174822
_z978-3-319-17482-2
024 7 _a10.1007/978-3-319-17482-2
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aCOM021030
_2bisacsh
072 7 _aUNF
_2thema
072 7 _aUYQE
_2thema006.312
_223
100 1 _aBurattin, Andrea.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aProcess Mining Techniques in Business Environments :
_bTheoretical Aspects, Algorithms, Techniques and Open Challenges in Process Mining /
_cby Andrea Burattin.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 0 _aLecture Notes in Business Information Processing,
_x1865-1348 ;
_v207
505 0 _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.
520 _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.
650 0 _aData mining.
650 0 _aManagement information systems.
650 0 _aInformation systems.
650 0 _aOptical pattern recognition.
650 1 4 _aData Mining and Knowledge Discovery.
_0http://scigraph.springernature.com/things/product-market-codes/I18030
650 2 4 _aBusiness Process Management.
_0http://scigraph.springernature.com/things/product-market-codes/522020
650 2 4 _aComputer Appl. in Administrative Data Processing.
_0http://scigraph.springernature.com/things/product-market-codes/I2301X
650 2 4 _aPattern Recognition.
_0http://scigraph.springernature.com/things/product-market-codes/I2203X
710 2 _aSpringerLink (Online service)
856 4 0 _uhttps://doi.org/10.1007/978-3-319-17482-2
_3Springer eBooks
_zOnline access link to the resource
912 _aZDB-2-SCS
912 _aZDB-2-LNB
999 _c200433868
_d52080
942 _2lcc
_cEBK
041 _aeng