Beyond Process Mining: Discovering Business Rules From Event Logs
•Descargar como PPTX, PDF•
4 recomendaciones•4,583 vistas
Keynote at the Brazilian Workshop on Business Process Management (WBPM) and Brazilian Symposium on Information Systems (SBSI), 23 May 2013
Recomendados
Recomendados
Más contenido relacionado
La actualidad más candente
La actualidad más candente (20)
Destacado
Destacado (19)
Similar a Beyond Process Mining: Discovering Business Rules From Event Logs
Similar a Beyond Process Mining: Discovering Business Rules From Event Logs (20)
Más de Marlon Dumas
Más de Marlon Dumas (20)
Último
Último (20)
Beyond Process Mining: Discovering Business Rules From Event Logs
- 1. Beyond Process Mining: Discovering Business Rules From Event Logs Marlon Dumas University of Tartu, Estonia With contributions from LucianoGarcía-Bañuelos, FabrizioMaggi&Massimiliano de Leoni Brazilian BPM Workshop (WBPM’ 2013)
- 2. Business Process Mining 2 Start Register order Prepare shipment Ship goods (Re)send bill Receive payment Contact customer Archive order End Performance Analysis Process Model Organizational Model Social Network Event Log Slide byAna Karla Alves de Medeiros Process mining tool (ProM, Disco, IBM BPI)
- 3. Automated Process Discovery 3 Enter Loan Application Retrieve Applicant Data Compute Installments Approve Simple Application Approve Complex Application Notify Rejection Notify Eligibility CID 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: Aggregation Bose, Veerbeck& van det Aalst: Discovering Hierarchical Process Models using ProM
- 7. ProM’s Fuzzy Miner Remove Infrequent Behavior & Aggregate
- 8. Approach 2: Trace Clustering G. Greco et al., Discovering Expressive Process Models by Clustering Log Traces, TKDE, 2006
- 9. Trace clustering in a nutshell Slide by Dirk Fahland
- 10. Bottom-Line Do we want models or do we want insights? www.interactiveinsightsgroup.com
- 11. Discovering Business Rules Decision rules • Why does something happen at a given point in time? Descriptive (temporal) rules • When and why does something happen? Discriminative rules • When and why does something wrong happen?
- 14. ProM’s Decision Miner 14 Enter Loan Application Retrieve Applicant Data Compute Installments Approve Simple Application Approve Complex Application Notify Rejection Notify Eligibility salaryage installment amount length CID Amount Len Salary Age Installm TaskCID Amount Len Salary Age Installm Task 13219 8500 1 NULL NULL NULL ELA Event Log CID Task Data Time Stamp … 13219 ELA Amount=8500 Len=1 2007-11-09 T 11:20:10 - 13219 RAP Salary=2000 Age=25 2007-11-09 T 11:22:15 - 13220 ELA Amount=25000 Len=1 2007-11-09 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 Task 13219 8500 1 NULL NULL NULL ELA 13219 8500 1 2000 25 NULL RAP 13219 8500 1 2000 25 750 RAP 13219 8500 1 2000 25 750 NE
- 15. (amount < 10000)(amount < 10000) ∨ (amount ≥ 10000 ∧ age < 35) amount Approve Simple Application (ASA) ≥10000 <10000 Approve Complex Application (ACA) Approve Simple Application (ASA) ≥ 35 age < 35 ProM’s Decision Miner / 2 CID Amount Installm Salary Age Len Task 13219 8500 750 2000 25 1 ASA 13220 12500 1200 3500 35 4 ACA 13221 9000 450 2500 27 2 ASA … … … … … … … 15 Approve Simple Application Approve Complex Application Decision tree learning amount ≥ 10000 ∧ age ≥35
- 16. ProM’s Decision Miner – Limitations • Decision tree learning cannot discover expressions of the form “v op v” 16 Approve Simple Application Approve Complex Application Notify Rejection Notify Eligibility installment > salary The 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 Rule Mining in Business Processes • Discover of decision rules composed of atoms of the form “v op c” and “v op v”, including linear equations or inequalities involving multiple variables • Approach: – Likely invariant discovery (Daikon) – Decision tree learning 17 De Leoni et al. FASE’2013
- 18. CID Amount Installm Salary Age Len Task 13210 20000 2000 2000 25 1 NR 13220 25000 1200 3500 35 2 NE 13221 9000 450 2500 27 2 NE 13219 8500 750 2000 25 1 ASA 13220 25000 1200 3500 35 2 ACA 13221 9000 450 2500 27 2 ASA … … … … … … … Daikon: Mining Likely Invariants 18 Approve Simple Application Approve Complex Application Notify Rejection Notify Eligibility Daikon installment > salary amount ≥ 5000 length < age … installment ≤ salary amount ≥ 5000 length < age … installment ≤ salary amount ≤ 9500 length < age … installment ≤ salary amount ≥ 10000 length < age …
- 19. • Information Gain (IG) quantifies the discriminating power of a predicate (with respect to two different outcomes) • Approach: – Use Daikon for discovering invariants – Combine invariants in a conjunction so as to maximize the overall IG 19 Approve Simple Application Approve Complex Application Notify Rejection Notify Eligibility a1: installment > salary a2: amount ≥ 5000 a3: length < age … IG(a1) = 0.8 IG(a2) = 0.2 IG(a3) = 0 … IG(a1∧a2) = 0.8 … Conjunctive Decision Rule Mining
- 20. Disjunctive Decision Rule Mining 20 … Partition 1 Partition2 Conjunctive Miner Conjunctive Miner CONJ1 CONJ2 Partitionn Conjunctive Miner CONJn Event Log
- 21. 21 … Partition 1 Partition2 Conjunctive BranchMiner Conjunctive BranchMiner CONJ1 CONJ2 Event Log Notify Rejection Notify Eligibility Notify Rejection Decision Tree … IG(CONJ1) = 0.4 IG(CONJ2) = 0.45 IG(CONJ3) = 0.5 … IG(CONJ1∨CONJ2) = 0.78 IG(CONJ1∨CONJ3) = 0.6 … Disjunctive Decision Rule Mining
- 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 leukocyte count is eventually followed by a platelet count • ☐(leukocyte_countplatelet_count) – Patients who undergo surgery X do not undergo surgery Y later • ☐(X ☐ not Y)
- 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 with condition X undergoes surgery Y • But not if the have previously been diagnosed with condition Z
- 27. Interesting Rules Something should have “normally” happened but did not happen, why? Something should normally not have happened but it 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% of cases • ☐(A B) has 90% fulfillment ratio – Discover a rule that describes the remaining 10% of cases, e.g. using data attributes • ☐(A [age < 70] B) has 100% fulfillment ratio
- 29. Now it’s better Bose et al. BPM’2013
- 30. And better (with data) Maggi et al. BPM’2013
- 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 that distinguish one class from the other, e.g. • Claims for house damage that end up in a complaint, are often those for which at two or more data entry errors are made by the customer when filing the claim
- 33. Mining Anomalous Software Development Issues (Sun et al. 2013) • Extract features from traces based on which events 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 Patterns Bose & van der Aalst 2013 K-nearest neighbor, one-class SVM kgrams, tandem repeats, … Decision trees, class association rules Cross validation
- 35. IBM Business Process Insight 1. Apply sequence mining to extract frequent patterns from event logs 2. Determine which patterns best discriminate between different outcomes – Uses Information Gain (IG) to rank patterns according to their discriminative power Lakshmanan et al. BPM’2013
- 36. Conclusion Business rules discovery goes beyond automated discovery 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 behavior Lots 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 from Business Process Execution Logs”. FASE’2013 • Mining rule-based process models – Maggi, Bose, van der Aalst: “Efficient Discovery of Understandable Declarative Process Models from Event Logs”. CAiSE'2012. – Di Ciccio, Mecella: “A Two-Step Fast Algorithm for the Automated Discovery of Declarative Workflows”. CIDM’2013. – Maggi, Dumas, García-Bañuelos, Montali: “Discovering Data-Aware Declarative Process Models from Event Logs”. BPM’2013 – Bose, Maggi, van der Aalst: “Enhancing Declare Maps Based on Event Correlations”. 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 With Outcomes”. BPM’2013
Notas del editor
- Discovering rules that describe not what happens but why it happens