Applicability of Process Mining Techniques in Business Environments
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1 Applicability of Process Mining Techniques in Business Environments Annual Meeting IEEE Task Force on Process Mining Andrea Burattin andreaburattin September 8, 2014
2 Brief Curriculum Vitæ 2009, M.Sc. Computer Science (A.I. program) University of Padova , Ph.D. Supervisor: Prof. Alessandro Sperduti Joint school University of Bologna Padova Thesis defended on April , Postdoc Prompt project (prompt.processmining.it) University of Padova Specola, Padova. 2 of 17
3 Ph.D. Inception Ph.D background Inception during M.Sc. thesis ˆ Companies: study on process mining A company (Siav S.p.A.) funded my PhD ˆ Aim: investigate applicability of process mining techniques in business scenarios ˆ Interaction with companies: interesting! (but sometimes... ) Outcome ˆ Applicability of Process Mining Techniques in Business Environments 3 of 17
4 Quick Recap of Process Mining Imagination Incarnation / Environment Operational Model implement Operational Incarnation control Information S ystem (re-)design describe basis Process Mining support protocol / audit Extension analyze Analytical Model augment compare Conformance compare Event Logs create Discovery mine Observation Source: C. Günther, Process mining in Flexible Environments. PhD thesis, TU/e, Eindhoven, of 17
5 Quick Recap of Process Mining Imagination Incarnation / Environment Operational Model implement Operational Incarnation control Information S ystem (re-)design describe basis Process Mining support protocol / audit Extension analyze Analytical Model augment compare Conformance compare Event Logs create Discovery mine Observation Source: C. Günther, Process mining in Flexible Environments. PhD thesis, TU/e, Eindhoven, of 17
6 Quick Recap of Process Mining Imagination Incarnation / Environment Operational Model implement Operational Incarnation control Information S ystem (re-)design describe basis Process Mining support protocol / audit Extension analyze Analytical Model augment compare Conformance compare Event Logs create Discovery mine Observation Source: C. Günther, Process mining in Flexible Environments. PhD thesis, TU/e, Eindhoven, of 17
7 Quick Recap of Process Mining Imagination Incarnation / Environment Operational Model implement Operational Incarnation control Information S ystem (re-)design describe basis Process Mining support protocol / audit Extension analyze Analytical Model augment compare Conformance compare Event Logs create Discovery mine Observation Source: C. Günther, Process mining in Flexible Environments. PhD thesis, TU/e, Eindhoven, of 17
8 Theoretical vs. Industrial-related Open Problems Some literature open problems Duplicate tasks Exploiting all data available Holistic mining Dierent perspectives from dierent sources Noise and incompleteness 5 of 17
9 Theoretical vs. Industrial-related Open Problems Some literature open problems Duplicate tasks Exploiting all data available Holistic mining Dierent perspectives from dierent sources Noise and incompleteness Case studies open problems Using process mining tools and conguring algorithms Results interpretation Readable results Computational power and storage capacity required 5 of 17
10 Theoretical vs. Industrial-related Open Problems Some literature open problems Duplicate tasks Exploiting all data available Holistic mining Dierent perspectives from dierent sources Noise and incompleteness Case studies open problems Using process mining tools and conguring algorithms Results interpretation Readable results Computational power and storage capacity required Not overlapping sets 5 of 17
11 Possible Industry Scenarios Four possible industry scenarios Process aware vs. Process unaware Process aware software vs. Process unaware software Process Aware Companies Company 4 Company 3 Process Unaware Companies Company 1 Company 2 Process Unaware Information Systems Process Aware Information Systems 6 of 17
12 Thesis Structure and Organization Data Prepara on Process Mining Capable Event Logs Process Mining Capable Event Stream Control flow Mining Stream Control flow Mining Process Extension Process Representa on Results Evalua on Model Evalua on 6 of 17
13 Overview Data Preparation Data Prepara on Process Mining Capable Event Logs Process Mining Capable Event Stream Control flow Mining Stream Control flow Mining Process Extension Process Representa on Results Evalua on Model Evalua on 6 of 17
14 Problems with Data Preparation Problems at dierent complexity and abstraction levels. Examples: Adaptation of existing data (Syntax problem, easy) Introduction of new information (Dicult) 7 of 17
15 Problems with Data Preparation Problems at dierent complexity and abstraction levels. Examples: Adaptation of existing data (Syntax problem, easy) Introduction of new information (Dicult) Typical set of required elds (case-id; activity; timestamp; [process-name]; [originator]) 7 of 17
16 Problems with Data Preparation Problems at dierent complexity and abstraction levels. Examples: Adaptation of existing data (Syntax problem, easy) Introduction of new information (Dicult) Typical set of required elds (case-id; activity; timestamp; [process-name]; [originator]) Our context: Company process aware; IS process unaware Structure of available log (activity; timestamp; originator; info 1 ;... ; info n ) 7 of 17
17 Problems with Data Preparation (cont.) Case-id from info i elds Candidate case-id elds A-priori knowledge Events chains Strings similarity functions Selection of maximal chain Most activities or simplest chain Process name is not a problem All events belonging to the same process 8 of 17
18 Problems with Data Preparation (cont.) Case-id from info i elds Candidate case-id elds A-priori knowledge Events chains Strings similarity functions Selection of maximal chain Most activities or simplest chain Process name is not a problem All events belonging to the same process Act. info 1 info 2 a 1 AB-01 BB-01 a 2 AA-02 AB-01 a 3 AB-01 BB-02 a 4 AB-01 BB-03 a 1 AA-03 BB-04 a 5 AA-03 BB-05 8 of 17
19 Overview Control-ow Mining Data Prepara on Process Mining Capable Event Logs Process Mining Capable Event Stream Control flow Mining Stream Control flow Mining Process Extension Process Representa on Results Evalua on Model Evalua on 8 of 17
20 Exploiting Data Available Events with duration instead of instantaneous event Generalization of Heuristics Miner to exploit this new information Time Sub ac vity 1 Sub ac vity 2 Sub ac vity n 1 Sub ac vity n Start End Main ac vity 9 of 17
21 Exploiting Data Available Events with duration instead of instantaneous event Generalization of Heuristics Miner to exploit this new information Time Sub ac vity 1 Sub ac vity 2 Sub ac vity n 1 Sub ac vity n Start End Main ac vity A B Process with events as me intervals B A D C D C A B C D Time A B C D Process with instantaneous events 9 of 17
22 Not-expert Users Our users: not-expert in process mining, with notions of BPM 10 of 17
23 Not-expert Users Our users: not-expert in process mining, with notions of BPM Observations Process mining algorithms require congurations Typically, algorithm congurations are threshold on measures The mining log is nite Only a nite amount of congurations possible 10 of 17
24 Not-expert Users Our users: not-expert in process mining, with notions of BPM Observations Process mining algorithms require congurations Typically, algorithm congurations are threshold on measures The mining log is nite Only a nite amount of congurations possible We are able to discretize the parameter values τ 1 =? τ 2 =? τ 3 =? τ 4 =? E A B C D B A C F D? E A A C B B C D D 10 of 17
25 Model Selection Approaches User-guided Approach Hierarchical clustering of models Average linkage Any model-to-model metric Process 1 Process 10 Process 9 Process 8 Process 5 Process 6 Process 4 Process 7 Process 2 Process Navigation of the dendrogram 11 of 17
26 Model Selection Approaches User-guided Approach Hierarchical clustering of models Average linkage Any model-to-model metric Automatic Approach Hill climbing with Maximum plateau steps Random restarts (Local optimum) h MDL = arg min h H L(h) + L(D h) Process 1 Process 10 Process 9 Process 8 Process 5 Process 6 Process 4 Process 7 Process 2 Process Navigation of the dendrogram MDL encodings MDL by Calders et al. Simplied heuristics 11 of 17
27 Overview Results Evaluation Data Prepara on Process Mining Capable Event Logs Process Mining Capable Event Stream Control flow Mining Stream Control flow Mining Process Extension Process Representa on Results Evalua on Model Evalua on 11 of 17
28 Evaluation Metrics Model-to-model Metric Complex process into Permitted relations Forbidden relations Generation rules (based on Alpha alg.) A B A > B, B A A B A > B, B > A A # B A B, B A Comparison as Jaccard similarity on two sets (> and ) 12 of 17
29 Evaluation Metrics Model-to-model Metric Complex process into Permitted relations Forbidden relations Generation rules (based on Alpha alg.) A B A > B, B A A B A > B, B > A A # B A B, B A Comparison as Jaccard similarity on two sets (> and ) Model-to-log Metric Declare constraint π and a trace σ healthiness measures Activation sparsity: 1 n a(σ,π) n(σ) Violation ratio: n v (σ,π) n a(σ,π) Fulllment ratio: Conict ratio: n f (σ,π) n a(σ,π) n c (σ,π) n a(σ,π) 12 of 17
30 Overview Process Extension Data Prepara on Process Mining Capable Event Logs Process Mining Capable Event Stream Control flow Mining Stream Control flow Mining Process Extension Process Representa on Results Evalua on Model Evalua on 12 of 17
31 Multiperspective Mining Given Log with information on originators Process model Assumption Roles are characterized by consistent set of originators We add roles to the model 13 of 17
32 Multiperspective Mining Given Log with information on originators Process model Assumption Roles are characterized by consistent set of originators We add roles to the model 1 Dependencies as handover of roles 2 Remove dependencies below threshold Connected components are candidate roles 3 Merge candidate roles if users sets similarities above threshold Entropy-based metric to tune thresholds 13 of 17
33 Overview Stream Control-ow Mining Data Prepara on Process Mining Capable Event Logs Process Mining Capable Event Stream Control flow Mining Stream Control flow Mining Process Extension Process Representa on Results Evalua on Model Evalua on 13 of 17
34 Stream Context Stream Mining Peculiarities Cannot store the entire stream Approximation Backtracking not feasible One pass over data Variable system condition Ex. uctuating stream rates Adapt the model to new data Concept drifts Completely new problems! 14 of 17
35 Stream Context Stream Mining Peculiarities Cannot store the entire stream Approximation Backtracking not feasible One pass over data Variable system condition Ex. uctuating stream rates Adapt the model to new data Concept drifts Principle Recent observations are more important than older ones Completely new problems! 14 of 17
36 Stream Context Stream Mining Peculiarities Cannot store the entire stream Approximation Backtracking not feasible One pass over data Variable system condition Ex. uctuating stream rates Adapt the model to new data Concept drifts Completely new problems! Principle Recent observations are more important than older ones 3 version of Heuristics Miner Based on Sliding Window Based on Lossy Counting Based on Budget Lossy Counting 14 of 17
37 Overview Data Prepara on Process Mining Capable Event Logs Process Mining Capable Event Stream Control flow Mining Stream Control flow Mining Process Extension Process Representa on Results Evalua on Model Evalua on 14 of 17
38 Extra: Processes and Logs Generator Companies are reluctant to share their data Researchers need to do tests (No BPI challenges at that time) 15 of 17
39 Extra: Processes and Logs Generator Companies are reluctant to share their data Researchers need to do tests (No BPI challenges at that time) Processes and Logs Generator Stochastic context free grammar generates random processes Rules to simulate a process and produce an event log Reference model used for evaluation control-ow mining algorithms A a P astart G (G; G) a end (G G) (G; G) A; (G G); A A b A A e f c d A g 15 of 17
40 Detailed Map of Performed Activities Legacy, Process unaware Informa on Systems Data Prepara on Process Mining Capable Event Logs Process Mining Capable Event Stream Control flow Mining Algorithm Exploi ng More Data User guided Discovery Algorithm Configura on Automa c Algorithm Configura on Event Logs Generator Stream Control flow Mining Framework Process Representa on (e.g. Dependency Graph, Petri Net) Extension of Process Models with Organiza onal Roles Random Process Generator Model to model Metric Model to log Metric Model Evalua on (wrt Log / Original Model) 16 of 17
41 Thanks! Doing the Ph.D. has been amazing! A huge Thank you! to My supervisor, Alessandro Sperduti Siav S.p.A. and Roberto Pinelli My internal examiners: Tullio Vardanega, Paolo Baldan My external examiners: Barbara Weber, Diogo Ferreira All the process mining community! 17 of 17
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