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Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
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Beyond Process Mining: Discovering Business Rules From Event Logs

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Keynote at the Brazilian Workshop on Business Process Management (WBPM) and Brazilian Symposium on Information Systems (SBSI), 23 May 2013

Keynote at the Brazilian Workshop on Business Process Management (WBPM) and Brazilian Symposium on Information Systems (SBSI), 23 May 2013

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  • Discovering rules that describe not what happens but why it happens
  • Transcript

    • 1. Beyond Process Mining:Discovering Business RulesFrom Event LogsMarlon DumasUniversity of Tartu, EstoniaWith contributions from LucianoGarcía-Bañuelos,FabrizioMaggi&Massimiliano de LeoniBrazilian BPM Workshop (WBPM’ 2013)
    • 2. Business Process Mining2StartRegister orderPrepareshipmentShip goods(Re)send billReceive paymentContactcustomerArchive orderEndPerformance AnalysisProcess ModelOrganizational ModelSocial NetworkEventLogSlide byAna Karla Alves de MedeirosProcess mining tool(ProM, Disco, IBMBPI)
    • 3. Automated Process Discovery3Enter LoanApplicationRetrieveApplicantDataComputeInstallmentsApproveSimpleApplicationApproveComplexApplicationNotifyRejectionNotifyEligibilityCID Task Time Stamp …13219 Enter Loan Application 2007-11-09 T 11:20:10 -13219 Retrieve Applicant Data 2007-11-09 T 11:22:15 -13220 Enter Loan Application 2007-11-09 T 11:22:40 -13219 Compute Installments 2007-11-09 T 11:22:45 -13219 Notify Eligibility 2007-11-09 T 11:23:00 -13219 Approve Simple Application 2007-11-09 T 11:24:30 -13220 ComputeInstallements 2007-11-09 T 11:24:35 -… … … …
    • 4. The Problem of Process Mining
    • 5. Dealing with Complexity• Question: How to cope with complexity in(information) system specifications?• Aggregate-Decompose (“part-of”)• Generalize-Specialize (“is a”)• Special cases• Summarize by aggregating and ignoring“uninteresting” parts• Summarize by specializing and ignoring“uninteresting” specialized classes
    • 6. Approach 1: AggregationBose, Veerbeck& van det Aalst: Discovering Hierarchical Process Models using ProM
    • 7. ProM’s Fuzzy MinerRemove Infrequent Behavior & Aggregate
    • 8. Approach 2: Trace ClusteringG. Greco et al., Discovering Expressive Process Models by Clustering Log Traces, TKDE, 2006
    • 9. Trace clustering in a nutshellSlide by Dirk Fahland
    • 10. Bottom-LineDo we want modelsor do we want insights?www.interactiveinsightsgroup.com
    • 11. Discovering Business RulesDecision rules• Why does something happen at a given point intime?Descriptive (temporal) rules• When and why does something happen?Discriminative rules• When and why does something wrong happen?
    • 12. Mining Decision Rules
    • 13. What’s missing?13Enter LoanApplicationRetrieveApplicantDataComputeInstallmentsApproveSimpleApplicationApproveComplexApplicationNotifyRejectionNotifyEligibilitysalaryageinstallmentamountlengthDecisionpoints
    • 14. ProM’s Decision Miner14Enter LoanApplicationRetrieveApplicantDataComputeInstallmentsApproveSimpleApplicationApproveComplexApplicationNotifyRejectionNotifyEligibilitysalaryageinstallmentamountlengthCID Amount Len Salary Age Installm TaskCID Amount Len Salary Age Installm Task13219 8500 1 NULL NULL NULL ELAEventLogCID Task Data Time Stamp …13219 ELAAmount=8500Len=12007-11-09 T 11:20:10 -13219 RAPSalary=2000Age=252007-11-09 T 11:22:15 -13220 ELAAmount=25000Len=12007-11-09 T 11:22:40 -13219 CI Installm=750 2007-11-09 T 11:22:45 -13219 NE 2007-11-09 T 11:23:00 -13219 ASA 2007-11-09 T 11:24:30 -13220 CI Installm=1200 2007-11-09 T 11:24:35 -… … … … …CID Amount Len Salary Age Installm Task13219 8500 1 NULL NULL NULL ELA13219 8500 1 2000 25 NULL RAP13219 8500 1 2000 25 750 RAP13219 8500 1 2000 25 750 NE
    • 15. (amount < 10000)(amount < 10000) ∨(amount ≥ 10000 ∧ age < 35)amountApprove SimpleApplication (ASA)≥10000 <10000Approve ComplexApplication (ACA)Approve SimpleApplication (ASA)≥ 35age< 35ProM’s Decision Miner / 2CID Amount Installm Salary Age Len Task13219 8500 750 2000 25 1 ASA13220 12500 1200 3500 35 4 ACA13221 9000 450 2500 27 2 ASA… … … … … … …15ApproveSimpleApplicationApproveComplexApplicationDecision treelearningamount ≥ 10000∧ age ≥35
    • 16. ProM’s Decision Miner – Limitations• Decision tree learning cannot discover expressionsof the form “v op v”16ApproveSimpleApplicationApproveComplexApplicationNotifyRejectionNotifyEligibilityinstallment > salaryThe decision miner would return:installment ≤ 1760 ∧ salary ≤ 1750 ∨installment ≤ 1810 ∧salary ≤ 1800 ∨installment ≤ 1875 ∧ salary ≤ 1850 ∨installment ≤ 1960 ∧ salary ≤ 1950 ∨installment ≤ 1975 ∧ salary ≤ 1970 ∨installment ≤ 2000 ∧ salary ≤ 1990 ∨ …
    • 17. Generalized Decision RuleMining in Business Processes• Discover of decision rules composed of atoms of theform “v op c” and “v op v”, including linear equationsor inequalities involving multiple variables• Approach:– Likely invariant discovery (Daikon)– Decision tree learning17De Leoni et al. FASE’2013
    • 18. CID Amount Installm Salary Age Len Task13210 20000 2000 2000 25 1 NR13220 25000 1200 3500 35 2 NE13221 9000 450 2500 27 2 NE13219 8500 750 2000 25 1 ASA13220 25000 1200 3500 35 2 ACA13221 9000 450 2500 27 2 ASA… … … … … … …Daikon: Mining Likely Invariants18ApproveSimpleApplicationApproveComplexApplicationNotifyRejectionNotifyEligibilityDaikoninstallment > salaryamount ≥ 5000length < age…installment ≤ salaryamount ≥ 5000length < age…installment ≤ salaryamount ≤ 9500length < age…installment ≤ salaryamount ≥ 10000length < age…
    • 19. • Information Gain (IG) quantifies the discriminating power of apredicate (with respect to two different outcomes)• Approach:– Use Daikon for discovering invariants– Combine invariants in a conjunction so as to maximize the overall IG19ApproveSimpleApplicationApproveComplexApplicationNotifyRejectionNotifyEligibilitya1: installment >salarya2: amount ≥ 5000a3: length < age…IG(a1) = 0.8IG(a2) = 0.2IG(a3) = 0…IG(a1∧a2) = 0.8…Conjunctive Decision Rule Mining
    • 20. Disjunctive Decision Rule Mining20…Partition1Partition2ConjunctiveMinerConjunctiveMinerCONJ1 CONJ2PartitionnConjunctiveMinerCONJnEventLog
    • 21. 21…Partition1Partition2ConjunctiveBranchMinerConjunctiveBranchMinerCONJ1 CONJ2EventLogNotifyRejectionNotifyEligibilityNotifyRejectionDecision Tree…IG(CONJ1) = 0.4IG(CONJ2) = 0.45IG(CONJ3) = 0.5…IG(CONJ1∨CONJ2) = 0.78IG(CONJ1∨CONJ3) = 0.6…Disjunctive Decision Rule Mining
    • 22. Mining Descriptive TemporalRules
    • 23. Problem Statement• Given a log, discover a set of temporal rules(LTL) that describe the underlying process,e.g.– In a lab analysis process, every leukocytecount is eventually followed by a platelet count• ☐(leukocyte_countplatelet_count)– Patients who undergo surgery X do notundergo surgery Y later• ☐(X ☐ not Y)
    • 24. DeclareMiner(Maggi et al.)
    • 25. Oh no! Not again!
    • 26. What went wrong?• Not all rules are interesting• What is “interesting”?– Not necessarily what is frequent (expected)– But what deviates from the expected• Example:– Every patient who is diagnosed withcondition X undergoes surgery Y• But not if the have previously been diagnosedwith condition Z
    • 27. Interesting RulesSomething should have “normally” happened butdid not happen, why?Something should normally not have happened butit happened, why?Something happens only when things go “well”Something happens only when things go “wrong”
    • 28. Discovering Refined Temporal Rules• Discover temporal rules that are frequently“activated” but not always “fulfilled”, e.g.– When A occurs, eventually B occurs in 90% ofcases• ☐(A  B) has 90% fulfillment ratio– Discover a rule that describes the remaining10% of cases, e.g. using data attributes• ☐(A [age < 70]  B) has 100% fulfillment ratio
    • 29. Now it’s betterBose et al. BPM’2013
    • 30. And better (with data)Maggi et al. BPM’2013
    • 31. Discriminative Rules Mining
    • 32. Problem Statement• Given a log partitioned into classes– e.g. good vs bad cases, on-time vs late cases• Discover a set of temporal rules thatdistinguish one class from the other, e.g.• Claims for house damage that end up in acomplaint, are often those for which at two or moredata entry errors are made by the customer whenfiling the claim
    • 33. Mining Anomalous SoftwareDevelopment Issues (Sun et al. 2013)• Extract features from traces based on whichevents occur in the trace• Apply a contrasting itemset mining technique features in one class and not in the other• Decision tree to construct readable rules
    • 34. Discovering Signature PatternsBose & van der Aalst 2013K-nearest neighbor, one-class SVMkgrams, tandem repeats, …Decision trees, class association rulesCross validation
    • 35. IBM Business Process Insight1. Apply sequence mining to extractfrequent patterns from event logs2. Determine which patterns bestdiscriminate between different outcomes– Uses Information Gain (IG) to rank patternsaccording to their discriminative powerLakshmanan et al. BPM’2013
    • 36. ConclusionBusiness rules discovery goes beyond automateddiscovery of process models• Enhances mined process models with decision rules• Describes an event log in terms of frequent patterns (descriptive rules)• Provides insights into:• Unexpected behavior• Undesirable behaviorLots of open challenges• Define & validate metrics of rule interestingness• Design scalable algorithms• Case studies with user feedback• Interactive business rule mining
    • 37. References• Mining decision rules– Rozinat, van der Aalst: “Decision Mining in ProM”. BPM’2006– De Leoni, Dumas, García-Bañuelos: “Discovering Branching Conditions fromBusiness Process Execution Logs”. FASE’2013• Mining rule-based process models– Maggi, Bose, van der Aalst: “Efficient Discovery of Understandable DeclarativeProcess Models from Event Logs”. CAiSE2012.– Di Ciccio, Mecella: “A Two-Step Fast Algorithm for the Automated Discovery ofDeclarative Workflows”. CIDM’2013.– Maggi, Dumas, García-Bañuelos, Montali: “Discovering Data-AwareDeclarative Process Models from Event Logs”. BPM’2013– Bose, Maggi, van der Aalst: “Enhancing Declare Maps Based on EventCorrelations”. BPM’2013.• Discriminative rules mining– Sun et al. Mining “Explicit Rules for Software Process Evaluation”. ICSSP’2013– Bose and van der Aalst: “Discovering Signature Patterns from Event Logs”.CIDM’2013.– Lakshmanan et al. “Investigating Clinical Care Pathways Correlated WithOutcomes”. BPM’2013

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