Constructs Competition Miner: Process Control-flow Discovery of BP-domain Constructs David Redlich, Thomas Molka, Wasif Gilani, Awais Rashid, Gordon Blair BPM2014 – Process Discovery Track external PhD student enrolled since August 2011, academic supervisors: Gordon and Awais employed at SAP Research Belfast Before: working on decision support for business processes Idea: do it at real-time Contacted Lancaster University as they are already involved in M@RT outline

Outline Introduction Constructs Competition Miner Evaluation Overview – Divide & Conquer Trace/Log Footprint Calculation Constructs Evaluation Constructs Competition BP Construction Example Evaluation Methodology/Logs Results Current/Future Work + Demo Conclusion

Introduction Process Discovery is great Many approaches/solutions exist Assumptions: Logs are incomplete and/or noisy There is always a “well enough”-fitting block-structured BP representation without duplicated activities for a given log (BPs are strongly nested) Goals: Direct discovery of human-readable block-structured BPs (like Process Tree) Robust: Deal with noisy/incomplete logs/inefficient representation General Idea: Utilize a Top-Down approach: Divide and Conquer  on every stage different BP Constructs compete with each other Utilize global rather than local relations between activity occurrences

Overview – Divide & Conquer Find Best Construct: All Activities Log Activity Set Find Best Construct Winning Construct L1 L1 Winning Construct Footprint Calculation Activity Subset 1 Activity Subset 2 L2 Winning Construct 2 : Log Footprint Find Best Construct Construct Suitability Evaluation : Find Best Construct L2 Winning Construct 1 : Construct Suitability Matrices : Constructs Competition BP Model Winning Construct, Activity Subsets 1 and 2

Trace/Log Footprint Log Activity Set Focus on global relationships between Activity Occurrences: Appears Before First: x y Appears Before: x y Occurrence Once Oon, Occurrence Overall Oov, First Element Fel Footprint Calculation Approach: Calculation of a Trace Footprint Calculation of a Log Footprint ∆ Footprint Calculation ∆ Log Footprint Construct Suitability Evaluation Construct Suitability Matrices Constructs Competition Winning Construct, Activity Subsets 1 and 2

Constructs Suitability Evaluation Log Activity Set Footprint Calculation Log Footprint Construct Suitability Evaluation Construct Suitability Matrices Constructs Competition Winning Construct, Activity Subsets 1 and 2 …

Constructs Competition Algorithm Log Activity Set Footprint Calculation  Utilizing the fact that we only have penalties between the subsets a c Log Footprint d b e f Construct Suitability Evaluation Exemplary Execution for one Construct (Choice): Construct Suitability Matrices Constructs Competition Winning Construct, Activity Subsets 1 and 2

Example: Footprint Interpretation B C D E F G ⃝ V⃝ +  ⃝ ⃝ -  Sign BP-Construct  Sequence V Choice + Split ⃝ Loop ⃝ Loop-Sequ. V⃝ Loop-Choice +⃝ Loop-Split x x A,B,C A x x G B B,C C x F,G x F + A,B,C,D D + x E,F,G E x

Evaluation Methodology Process Discovery Tools: HeuristicsMiner Inductive Miner (SotA) CCM Flower Miner (all with default settings) Test Logs 1 L1 EX5 REP BE1 BE2 BE3 BE4 BE5 DF FLA |A| 8 14 20 18 10 |T| 34 100 1104 8204 8206 8194 8153 8190 13T |E| 204 1498 7733 189T 132T 240T 254T 152T 3354 61T 3 3 4 5 2 Methodology: 6 6 6 PNetReplayer Plugin - Van Der Aalst et al.: Replaying history on process models for conformance checking and performance analysis. WIREs Data Mining and Knowledge Discovery, 2(2), 182-192, 2012 1 Weijters et al.: Process Mining with the Heuristics Miner-algorithm. BETA Working Paper Series, WP 166, Eindhoven University of Technology, 2006 2 Leemans et al.: Discovering Block-Structured Process Models from Event Logs Containing Infrequent Behaviour, In: BPM Workshops 2013, LNBIP, pp. 66-78, Springer, 2013 3 Logs from ProM website 4 Galushka et al.: DrugFusion - Retrieval Knowledge Management for Prediction of Adverse Drug Events. In: BIS2014, pp. 13-24, LNBIP, Springer, 2014 5 Partial Financial Log from BPI Challenge 2012

Evaluation Results Trace Fitness Precision Generalization Simplicity Log HM IM CCM L1 0.679 0.863 1.000 0.532 0.529 0.550 0.638 0.422 0.654 86 91 81 EX5 0.985 0.935 0.495 0.560 0.931 0.996 0.998 155 102 80 REP 0.905 0.955 0.999 72 46 49 BE1 0.991 0.838 0.814 0.818 192 132 122 BE2 0.924 0.981 0.737 0.594 0.621 196 156 146 BE3 0.822 0.983 0.891 0.443 0.525 178 149 139 BE4 0.876 0.707 0.406 0.608 193 173 BE5 0.942 0.590 0.668 0.711 206 181 167 DF 0.911 0.970 0.563 0.559 0.588 0.914 0.906 0.832 177 136 121 FLA 0.974 0.920 0.695 0.727 0.925 0.825 98 62 65 ALL 0.919 0.966 0.979 0.718 0.622 0.663 0.941 0.915 0.930 155.3 122.8 111.9

Current Work/Future Work Static Process Discovery: Static Constructs Competition Miner Log  BP Model (e1, e2, … , en-1, en)  BPn Dynamic Process Discovery: Dynamic Constructs Competition Algorithm Additional Scalability Constraints  work at run-time (event-based) (BPn-1, en)  BPn

Demo

Conclusion Investigation how far global relations can be utilized to discover a model Application of run-time efficient divide-and-conquer approach (due to global relationships) CCM with few adaptations usable on event-streams Advantages: Works very well on strongly nested BPs Promising approach for improving the discovery of parallel constructs Tuning possible: Trace Fitness vs. Precision Robust: incomplete or noisy logs Discovered Disadvantages: “Wrong” early decision leads to compensation at a later stage (no backtracking) Certain behaviour cannot be determined only through global relations, e.g. loop that is always executed a fixed amount of times > 1

Thank you. mr.redlich@gmail.com