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Automated Discovery of Declarative Process Models

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Claudio Di Ciccio
Claudio Di Ciccio

The document discusses the automated discovery of declarative process models, focusing on understanding the constraints that define allowed behaviors in knowledge-intensive and flexible processes. It outlines the usage of various constraints and templates, such as responded existence and response, to model the relationships between activities in a process instance. The text also addresses challenges in mining declarative processes and improving the intelligibility of mined models through pruned constraints and inconsistency detection.

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The Automated Discovery of Declarative Process Models Claudio Di Ciccio claudio.di.ciccio@wu.ac.at Humboldt-Universität zu Berlin, 7 December 2016
www.wu.ac.at www.wu.ac.at/infobiz www.wu.ac.at/infobiz/team/diciccio
Control-flow discovery ? Objective: understanding the temporal structure that best describes the process behind the event log SEITE 3
Knowledge-intensive Processes SEITE 4
Knowledge-intensive Processes SEITE 5
Mining flexible processes SEITE 6
Declarative modelling of processes  Usage of constraints  “Open model”  Declare  state-of-the-art language If A is performed, B must be performed, no matter if before or afterwards (responded existence) Whenever B is performed, C must be performed afterwards and B can not be repeated until C is done (alternate response) SEITE 7
Workflow Nets as process models SEITE 8
Imperative v declarative SEITE 9 SEITE 9
Declarative processes DECLARE
Declarative process modelling  “Open model”  Specify constraints for permitted behaviour  Every execution that complies with them is acceptable  Works best with flexible processes  The set of DECLARE templates is extendible SEITE 11
A fragment of declarative process model  If an abstract is submitted, a new paper had been written or will be written  After the paper submission, a confirmation email is sent  After the paper submission, the paper will be reviewed; there can be no review without a preceding submission  A paper can be accepted only after it has been reviewed  After the rejection, no further submission follows  Paper cannot be both accepted and rejected SEITE 12 Submit abstract Write new paper Submit paper Send confirmation email Submit paper Review paper Review paper Accept paper Reject paper Submit paper Accept paper Reject paper = activation task Responded existence(Submit abstract, Write new paper) Response(Submit paper, Send confirmation email) Succession(Submit paper, Review paper) Precedence(Review paper, Accept paper) Not succession(Reject paper, Submit paper) Not co-existence(Accept paper, Reject paper) Template Tasks
A fragment of declarative process model SEITE 13 Submit abstract Write new paper Submit paper Send confirmation email Review paper Accept paper Reject paper
Declare: Existence Constraint Templates Existence(n, A) Activity A occurs at least n times in the process instance BCAAC ✓ BCAAAC ✓ BCAC ✗ (for n = 2) Absence(A) Activity A does not occur in the process instance BCC ✓ BCAC ✗ Absence(n+1, A) Activity A occurs at most n-1 times in the process instance BCAAC ✗ BCAC ✓ BCC ✓ (for n = 2) Exactly(n, A) Activity A occurs exactly n times in the process instance BCAAC ✓ BCAAAC ✗ BCAC ✗ (for n = 2) Init(A) Activity A is the first to occur in each process instance BCAAC ✗ ACAAAC ✓ BCC ✗ Absence(2, A) ≡ AtMostOne(A) Existence(1, A) ≡ Participation(A) SEITE 14
Declare: Relation Constraint Templates RespondedExistence(A, B) If A occurs in the process instance, then B occurs as well CAC ✗ CAACB ✓ BCAC ✓ BCC ✓ SEITE 15
Declare: Relation Constraint Templates RespondedExistence(A, B) If A occurs in the process instance, then B occurs as well CAC ✗ CAACB ✓ BCAC ✓ BCC ✓ Response(A, B) If A occurs in the process instance, then B occurs after A BCAAC ✗ CAACB ✓ CAC ✗ BCC ✓ SEITE 16
Declare: Relation Constraint Templates RespondedExistence(A, B) If A occurs in the process instance, then B occurs as well CAC ✗ CAACB ✓ BCAC ✓ BCC ✓ Response(A, B) If A occurs in the process instance, then B occurs after A BCAAC ✗ CAACB ✓ CAC ✗ BCC ✓ AlternateResponse(A, B) Each time A occurs in the process instance, then B occurs afterwards, before A recurs BCAAC ✗ CAACB ✗ CACB ✓ CABCA ✗ BCC ✓ CACBBAB ✓ SEITE 17
Declare: Relation Constraint Templates RespondedExistence(A, B) If A occurs in the process instance, then B occurs as well CAC ✗ CAACB ✓ BCAC ✓ BCC ✓ Response(A, B) If A occurs in the process instance, then B occurs after A BCAAC ✗ CAACB ✓ CAC ✗ BCC ✓ AlternateResponse(A, B) Each time A occurs in the process instance, then B occurs afterwards, before A recurs BCAAC ✗ CAACB ✗ CACB ✓ CABCA ✗ BCC ✓ CACBBAB ✓ ChainResponse(A, B) Each time A occurs in the process instance, then B occurs immediately afterwards BCAAC ✗ BCAABC ✗ BCABABC ✓ Activation Target
Declare: Relation Constraint Templates RespondedExistence(B, A) If B occurs in the process instance, then A occurs as well CAC ✓ CAACB ✓ BCAC ✓ BCC ✗ Precedence(A, B) B occurs in the process instance only if preceded by A BCAAC ✗ CAACB ✓ CAC ✓ BCC ✓ AlternatePrecedence(A, B) Each time B occurs in the process instance, it is preceded by A and no other B can recur in between BCAAC ✗ CAACB ✓ CACB ✓ CABCA ✓ BCC ✗ CACBAB ✓ ChainPrecedence(A, B) Each time B occurs in the process instance, then B occurs immediately beforehand BCAAC ✗ BCAABC ✗ CABABCA ✓ Target Activation
Declare: Relation Constraint Templates CoExistence(A, B) ≡ Resp’dEx.(A, B) ∧ Resp’dEx.(B, A) If B occurs in the process instance, then A occurs, and viceversa CAC ✗ CAACB ✓ BCAC ✓ BCC ✗ Succession(A, B) ≡ Response(A, B) ∧ Precedence(A, B) A occurs if and only if it is followed by B in the process instance BCAAC ✗ CAACB ✓ CAC ✗ BCC ✗ AlternateSuccession(A, B) ≡ Alt.Resp.(A, B) ∧ Alt.Prec.(A, B) A and B occur in the process instance if and only if the latter follows the former, and they alternate each other in the trace BCAAC ✗ CAACB ✗ CACB ✓ CABCA ✗ BCC ✗ CACBAB ✓ ChainSuccession(A, B) ≡ ChainResp.(A, B) ∧ ChainPrec.(A, B) A and B occur in the process instance if and only if the latter immediately follows the former BCAAC ✗ BCAABC ✗ CABABC ✓ Activation Target Target Activation
Declare: Negative Relation Constraint Templates NotCoExistence(A, B) A and B never occur together in the process instance CAC ✓ CAACB ✗ BCAC ✗ BCC ✓ NotSuccession(A, B) A can never occur before B in the process instance BCAAC ✓ CAACB ✗ CAC ✓ BCC ✓ NotChainSuccession(A, B) A and B occur in the process instance if and only if the latter does not immediately follows the former BCAAC ✓ BCAABC ✗ CBACBA ✓ Activation Target Target Activation
Subsumption hierarchy of constraint templates SEITE 22
Declarative processes Discovery of DECLARE models
Constraints mining ? Objective: understanding the constraints that best define the allowed behaviour of the process behind the event log SEITE 24
Mining declarative processes: ingredients “Submit abstract”, “Submit paper”, “Accept paper”, … SEITE 25 A, B, C, … Activities Process alphabet
Mining declarative processes RespondedExistence(A, B) ? RespondedExistence(A, C) ? … Response(A, B) ? Response(A, C) ? … SEITE 26 • Support: fraction of cases fulfilling the constraint • Confidence: support scaled by fraction of traces in which the activation occurs • Interest factor: confidence scaled by fraction of traces in which the target occurs Support Conf. I.F.
Mining declarative processes RespondedExistence(A, B) ? RespondedExistence(A, C) ? … Response(A, B) ? Response(A, C) ? … SEITE 27 Support Conf. I.F. • Support: fraction of cases fulfilling the constraint • Confidence: support scaled by fraction of traces in which the activation occurs • Interest factor: confidence scaled by fraction of traces in which the target occurs
Mining declarative processes RespondedExistence(A, B)  RespondedExistence(A, C)  … Response(A, B) ? Response(A, C)  … SEITE 28 Support Conf. I.F. • Support: fraction of cases fulfilling the constraint • Confidence: support scaled by fraction of traces in which the activation occurs • Interest factor: confidence scaled by fraction of traces in which the target occurs
Mining declarative processes RespondedExistence(A, B)  RespondedExistence(A, C)  … Response(A, B)  Response(A, C)  … SEITE 29 Support Conf. I.F. • Support: fraction of cases fulfilling the constraint • Confidence: support scaled by fraction of traces in which the activation occurs • Interest factor: confidence scaled by fraction of traces in which the target occurs
Constraints mining An example SEITE 30 A A B C A B C A C B C D A C C A B B C B C A C B B D C C C C C A A B C A A B A A B A B B B D C B A B D A B B D A A A C A C B D A B C D C A B A A C C B B D B C C D C A A C C C A A B C B C C B D
The role of Support (event-based) A A B C A B C A C B C D A C C A B B C B C A C B B D C C C C C A A B C A A B A A B A B B B D C B A B D A B B D A A A C A C B D A B C D C A B A A C C B B D B C C D C A A C C C A A B C B C C B D  Support for  Response(A, B)  1.0  Precedence(A, B)  0.926 (25/27)  Pruning on the basis of a support threshold  E.g., 0.95  A threshold equal to 1.0 for a constraint means “always valid in the log” Activation Target Target Activation SEITE 31 http://github.com/cdc08x/minerful Prom Nightly Builds > “Declare MINERful” plug-in
The role of Support (trace-based) A A B C A B C A C B C D A C C A B B C B C A C B B D C C C C C A A B C A A B A A B A B B B D C B A B D A B B D A A A C A C B D A B C D C A B A A C C B B D B C C D C A A C C C A A B C B C C B D  Support for  Response(A, B)  1.0  Precedence(A, B)  0.818 (9/11)  Pruning on the basis of a support threshold  E.g., 0.95  A threshold equal to 1.0 for a constraint means “always valid in the log” SEITE 32 http://sourceforge.net/projects/prom/files/ProM/6.3
Declarative processes Challenges
SEITE 34
Objective SEITE 35
Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Increase the expressiveness of discovered models  Specify templates SEITE 36
Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Increase the expressiveness of discovered models  Specify templates SEITE 37 © Granger, NYC, source: https://www.granger.com/
Redundancies in discovered constraints  E.g.,  A trace like A B A B C A B C C satisfies (w.r.t. A and B):  RespondedExistence(A, B), RespondedExistence(B, A), Response(A, B), AlternateResponse(A, B), ChainResponse(A, B), Precedence(A, B), AlternatePrecedence(A, B), ChainPrecedence(A, B), CoExistence(A, B), CoExistence(B, A), Succession(A, B), AlternateSuccession(A, B), ChainSuccession(A, B)  Among them, the mining algorithm should return only the most restricting constraint – i.e., ChainSuccession(A, B)  In order to face this issue, returned constraints are pruned on the basis of the subsumption hierarchy of constraints SEITE 38
Pruning redundant constraints 1.0 1.0 1.0 1.01.0 1.0 1.0 1.0 1.0 1.0 1.0 ✖ ✖ ✖ ✖ ✖ ✖✖ ✖✖ ✖ SEITE 39
Pruning redundant constraints 0.8 0.8 0.9 0.90.9 0.9 0.7 0.7 0.9 0.9 0.9 ✖ ✖ ? ? ✖ ✖ ✖ ? ? S u p p o r t ✖ ✖ ✖ ✖ SEITE 40
Mining declarative processes RespondedExistence(A, B)  RespondedExistence(A, C) ? … Response(A, B)  Response(A, C)  … SEITE 41 Support Conf. I.F.
Mining declarative processes RespondedExistence(A, B)  RespondedExistence(A, C) ? … Response(A, B)  Response(A, C)  … SEITE 42 Support Conf. I.F.
Mining declarative processes RespondedExistence(A, B)  RespondedExistence(A, C)  … Response(A, B)  Response(A, C)  … SEITE 43 Support Conf. I.F. http://github.com/cdc08x/minerful Prom Nightly Builds > “Declare MINERful” plug-in http://sourceforge.net/projects/prom/files/ProM/6.3
Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Increase the expressiveness of discovered models  Specify templates SEITE 44
Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Increase the expressiveness of discovered models  Specify templates Interlude DECLARE & Regular Expressions SEITE 45
Mining declarative processes RespondedExistence(A, B) RespondedExistence(A, C) and Response(A, B) Response(A, C) and … SEITE 46
Semantics of Declare: Regular Expressions SEITE 47
From constraints-based model to FSA SEITE 48 [^a]*((a.*b.*)|(b.*a.*))*[^a]* [^a]*(a.*c)*[^a]*  Regular Expression Deterministic Finite State Automaton RespondedExistence(A, B) RespondedExistence(A, C) and Response(A, B) Response(A, C) and …
Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Increase the expressiveness of discovered models Reprise SEITE 49
While mining a real-life log… SEITE 50
Time to challenge the X SEITE 51 
The result SEITE 52
The problem  When support threshold is lower than 100%, constraints can be valid through most of the log, though being in conflict  Example: an event log consists of two traces: 1. <A, B, A, B, A, B, C> 2. <A, B, A, B, A, C>  Support threshold: 0.7 • a is always the first  Init(A) • c is always the last  End(C) • In 6 cases over 8 (75%), a and c do not directly follow each other  NotChainSuccession(A, C) • In 5 cases over 7 (71.143%), b and c do not directly follow each other  NotChainSuccession(B, C) SEITE 53
The problem  When support threshold is lower than 100%, constraints can be valid through most of the log, though being in conflict  Example: an event log consists of two traces: 1. <A, B, A, B, A, B, C> 2. <A, B, A, B, A, C>  Support threshold: 0.7 • a is always the first  Init(A) • c is always the last  End(C) • In 6 cases over 8 (75%), a and c do not directly follow each other  NotChainSuccession(A, C) • In 5 cases over 7 (71.143%), a and b do not directly follow each other  NotChainSuccession(B, C)  Question: what can be done right before C?  inconsistency! SEITE 54
The algorithm to detect inconsistencies SEITE 55  Init(a) Participation(b) AtMostOne(c) End(c) ChainPrecedence(a, b) … ChainSuccession(a, b) Response(a, b) ChainResponse(b, a) NotChainSuccession(a, c) ChainSuccession(b, a) NotChainSuccession(b, c) … … … 1
The algorithm to detect inconsistencies  Init(a) Participation(b) AtMostOne(c) End(c) ChainPrecedence(a, b) … ChainSuccession(a, b) Response(a, b) ChainResponse(b, a) NotChainSuccession(a, c)  ChainSuccession(b, a) NotChainSuccession(b, c) … … … 1 2 SEITE 56 http://github.com/cdc08x/minerful
SEITE 57
Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Increase the expressiveness of discovered models SEITE 58 © Granger, NYC, source: https://www.granger.com/
The application of the method to minimise the model  Rationale: 1. How to find redundancies among constraints?  Use the automaton-model correspondence  Same language recognised after the product?  Main difference with the inconsistency- checking algorithm  Constraints having support 100% are checked for redundancies  More details in the paper SEITE 59 http://github.com/cdc08x/minerful
The application of the method to minimise the model SEITE 60 BPIC 2012
Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Inspect the imperative counterpart  Simulate runs  Increase the expressiveness of discovered models  Specify templates SEITE 61
From declarative to imperative models SEITE 62 Response(Queued, Accepted) NotChainSuccession(Queued, Completed) Response(Queued, Completed) NotChainSuccession(Queued, Unmatched) Response(Accepted, Completed) End(Completed) NotSuccession(Completed, Unmatched) AtMostOne(Unmatched) RespondedExistence(Unmatched, Accepted) AlternateResponse(Unmatched, Completed)    …
Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Inspect the imperative counterpart  Simulate runs  Increase the expressiveness of discovered models  Specify templates SEITE 63
Generation of logs SEITE 64 http://github.com/cdc08x/minerful http://github.com/processmining/synthetic-log-generator
Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Inspect the imperative counterpart  Simulate runs  Increase the expressiveness of discovered models  Specify templates SEITE 65
Target-Branched Declare Support for  Response(A, {B, C})  1.0 B A A B A B C D A A D B B A A D C B A D D A D D A B E C A B C D A D B D A E A C A A A D B D A D D B D A A A D C D A D D D A C A A B E D E B C E E C B E SEITE 66 Activation Target Branching factor = 2
Objective SEITE 67 + Expressive Power + Conciseness
Evaluation: constraints pruning SEITE 68 BPI Challenge 2012 (0) 6,654,480 (1) 10,676 (2) 1446 (3) 12
Evaluation: performance SEITE 69 BPI Challenge 2012 (KB) 00:07.274 (Const’s) 25:51.678 (Total) 26:11.380 http://github.com/cdc08x/minerful
Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Inspect the imperative counterpart  Simulate runs  Increase the expressiveness of discovered models  Specify templates SEITE 70
Semantics of Declare: LTL and LTLf  Linear Temporal Logic (LTL) initially was a specification language for the execution of (endless) concurrent programs (Pnueli, 1977)  Syntax (let A be a propositional symbol):  DECLARE was initially based on LTL SEITE 71 “Until” “Eventually”“Always” “Next”
Semantics of Declare: LTL SEITE 72
Declarative process modelling  “Open model”  Specify constraints for permitted behaviour  Every execution that complies with them is acceptable  Works best with flexible processes  The set of DECLARE templates is extendible SEITE 73
Extendibility of DECLARE: A clear business impact SEITE 74 Source: http://businessvalueexchange.com/
Semantics of Declare: LTL and LTLf  Linear Temporal Logic (LTL) initially was a specification language for the execution of (endless) concurrent programs (Pnueli, 1977)  Syntax (let A be a propositional symbol):  Interpretation over infinite traces, i.e., an infinite sequence of consecutive instants of time  LTLf formulae are meant to be interpreted over finite traces “Until” “Eventually”“Always” “Next” SEITE 75
Semantics of Declare: LTLf SEITE 76
Semantics of Declare: SCIFF SEITE 77
Semantics of Declare: R/I-nets SEITE 78
Semantics of Declare: FOL over finite traces SEITE 79
Semantics of Declare: Regular expressions SEITE 80
More alternatives for DECLARE spec.: A clear business impact SEITE 81 Source: http://businessconsultantsnewyork.blogspot.co.at/
My long-term objective  Establish an algebra of constraint templates, built on a basis of behavioural relations that are:  orthogonal to one another (no entailments, subsumptions…)  covering multiple trace-based behavioural relations definition languages by linear composition  have clear semantics (last but for sure NOT least)  ... SEITE 82
Further reading  Presented:  About MINERful:  Claudio Di Ciccio, Massimo Mecella: On the Discovery of Declarative Control Flows for Artful Processes. ACM Trans. Management Inf. Syst. 5(4): 24:1-24:37 (2015)  Simulation of DECLARE models:  Claudio Di Ciccio, Mario Luca Bernardi, Marta Cimitile, Fabrizio Maria Maggi: Generating Event Logs Through the Simulation of Declare Models. EOMAS@CAiSE 2015: 20-36  Discovery of target-branched DECLARE models:  Claudio Di Ciccio, Fabrizio Maria Maggi, Jan Mendling: Efficient discovery of Target-Branched Declare constraints. Inf. Syst. 56: 258-283 (2016)  Getting rid of redundancies and inconsistencies:  Claudio Di Ciccio, Fabrizio Maria Maggi, Marco Montali, Jan Mendling: Ensuring Model Consistency in Declarative Process Discovery. BPM 2015: 144-159  From DECLARE to Petri nets:  Johannes Prescher, Claudio Di Ciccio, Jan Mendling: From Declarative Processes to Imperative Models. SIMPDA 2014: 162-173  More:  First steps towards data awareness of discovered DECLARE models:  Stefan Schönig, Claudio Di Ciccio, Fabrizio Maria Maggi, Jan Mendling: Discovery of Multi-perspective Declarative Process Models. ICSOC 2016: 87-103  Against the little learnibility of DECLARE:  Michael Hanser, Claudio Di Ciccio, Jan Mendling: A New Notational Framework for Declarative Process Modeling. Softwaretechnik-Trends 36(2) (2016)  Guarantee of the significance of discovered DECLARE models:  Fabrizio Maria Maggi, Marco Montali, Claudio Di Ciccio, Jan Mendling: Semantical Vacuity Detection in Declarative Process Mining. BPM 2016: 158-175 SEITE 83

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Automated Discovery of Declarative Process Models

  • 1. The Automated Discovery of Declarative Process Models Claudio Di Ciccio [email protected] Humboldt-Universität zu Berlin, 7 December 2016
  • 3. Control-flow discovery ? Objective: understanding the temporal structure that best describes the process behind the event log SEITE 3
  • 7. Declarative modelling of processes  Usage of constraints  “Open model”  Declare  state-of-the-art language If A is performed, B must be performed, no matter if before or afterwards (responded existence) Whenever B is performed, C must be performed afterwards and B can not be repeated until C is done (alternate response) SEITE 7
  • 8. Workflow Nets as process models SEITE 8
  • 11. Declarative process modelling  “Open model”  Specify constraints for permitted behaviour  Every execution that complies with them is acceptable  Works best with flexible processes  The set of DECLARE templates is extendible SEITE 11
  • 12. A fragment of declarative process model  If an abstract is submitted, a new paper had been written or will be written  After the paper submission, a confirmation email is sent  After the paper submission, the paper will be reviewed; there can be no review without a preceding submission  A paper can be accepted only after it has been reviewed  After the rejection, no further submission follows  Paper cannot be both accepted and rejected SEITE 12 Submit abstract Write new paper Submit paper Send confirmation email Submit paper Review paper Review paper Accept paper Reject paper Submit paper Accept paper Reject paper = activation task Responded existence(Submit abstract, Write new paper) Response(Submit paper, Send confirmation email) Succession(Submit paper, Review paper) Precedence(Review paper, Accept paper) Not succession(Reject paper, Submit paper) Not co-existence(Accept paper, Reject paper) Template Tasks
  • 13. A fragment of declarative process model SEITE 13 Submit abstract Write new paper Submit paper Send confirmation email Review paper Accept paper Reject paper
  • 14. Declare: Existence Constraint Templates Existence(n, A) Activity A occurs at least n times in the process instance BCAAC ✓ BCAAAC ✓ BCAC ✗ (for n = 2) Absence(A) Activity A does not occur in the process instance BCC ✓ BCAC ✗ Absence(n+1, A) Activity A occurs at most n-1 times in the process instance BCAAC ✗ BCAC ✓ BCC ✓ (for n = 2) Exactly(n, A) Activity A occurs exactly n times in the process instance BCAAC ✓ BCAAAC ✗ BCAC ✗ (for n = 2) Init(A) Activity A is the first to occur in each process instance BCAAC ✗ ACAAAC ✓ BCC ✗ Absence(2, A) ≡ AtMostOne(A) Existence(1, A) ≡ Participation(A) SEITE 14
  • 15. Declare: Relation Constraint Templates RespondedExistence(A, B) If A occurs in the process instance, then B occurs as well CAC ✗ CAACB ✓ BCAC ✓ BCC ✓ SEITE 15
  • 16. Declare: Relation Constraint Templates RespondedExistence(A, B) If A occurs in the process instance, then B occurs as well CAC ✗ CAACB ✓ BCAC ✓ BCC ✓ Response(A, B) If A occurs in the process instance, then B occurs after A BCAAC ✗ CAACB ✓ CAC ✗ BCC ✓ SEITE 16
  • 17. Declare: Relation Constraint Templates RespondedExistence(A, B) If A occurs in the process instance, then B occurs as well CAC ✗ CAACB ✓ BCAC ✓ BCC ✓ Response(A, B) If A occurs in the process instance, then B occurs after A BCAAC ✗ CAACB ✓ CAC ✗ BCC ✓ AlternateResponse(A, B) Each time A occurs in the process instance, then B occurs afterwards, before A recurs BCAAC ✗ CAACB ✗ CACB ✓ CABCA ✗ BCC ✓ CACBBAB ✓ SEITE 17
  • 18. Declare: Relation Constraint Templates RespondedExistence(A, B) If A occurs in the process instance, then B occurs as well CAC ✗ CAACB ✓ BCAC ✓ BCC ✓ Response(A, B) If A occurs in the process instance, then B occurs after A BCAAC ✗ CAACB ✓ CAC ✗ BCC ✓ AlternateResponse(A, B) Each time A occurs in the process instance, then B occurs afterwards, before A recurs BCAAC ✗ CAACB ✗ CACB ✓ CABCA ✗ BCC ✓ CACBBAB ✓ ChainResponse(A, B) Each time A occurs in the process instance, then B occurs immediately afterwards BCAAC ✗ BCAABC ✗ BCABABC ✓ Activation Target
  • 19. Declare: Relation Constraint Templates RespondedExistence(B, A) If B occurs in the process instance, then A occurs as well CAC ✓ CAACB ✓ BCAC ✓ BCC ✗ Precedence(A, B) B occurs in the process instance only if preceded by A BCAAC ✗ CAACB ✓ CAC ✓ BCC ✓ AlternatePrecedence(A, B) Each time B occurs in the process instance, it is preceded by A and no other B can recur in between BCAAC ✗ CAACB ✓ CACB ✓ CABCA ✓ BCC ✗ CACBAB ✓ ChainPrecedence(A, B) Each time B occurs in the process instance, then B occurs immediately beforehand BCAAC ✗ BCAABC ✗ CABABCA ✓ Target Activation
  • 20. Declare: Relation Constraint Templates CoExistence(A, B) ≡ Resp’dEx.(A, B) ∧ Resp’dEx.(B, A) If B occurs in the process instance, then A occurs, and viceversa CAC ✗ CAACB ✓ BCAC ✓ BCC ✗ Succession(A, B) ≡ Response(A, B) ∧ Precedence(A, B) A occurs if and only if it is followed by B in the process instance BCAAC ✗ CAACB ✓ CAC ✗ BCC ✗ AlternateSuccession(A, B) ≡ Alt.Resp.(A, B) ∧ Alt.Prec.(A, B) A and B occur in the process instance if and only if the latter follows the former, and they alternate each other in the trace BCAAC ✗ CAACB ✗ CACB ✓ CABCA ✗ BCC ✗ CACBAB ✓ ChainSuccession(A, B) ≡ ChainResp.(A, B) ∧ ChainPrec.(A, B) A and B occur in the process instance if and only if the latter immediately follows the former BCAAC ✗ BCAABC ✗ CABABC ✓ Activation Target Target Activation
  • 21. Declare: Negative Relation Constraint Templates NotCoExistence(A, B) A and B never occur together in the process instance CAC ✓ CAACB ✗ BCAC ✗ BCC ✓ NotSuccession(A, B) A can never occur before B in the process instance BCAAC ✓ CAACB ✗ CAC ✓ BCC ✓ NotChainSuccession(A, B) A and B occur in the process instance if and only if the latter does not immediately follows the former BCAAC ✓ BCAABC ✗ CBACBA ✓ Activation Target Target Activation
  • 24. Constraints mining ? Objective: understanding the constraints that best define the allowed behaviour of the process behind the event log SEITE 24
  • 25. Mining declarative processes: ingredients “Submit abstract”, “Submit paper”, “Accept paper”, … SEITE 25 A, B, C, … Activities Process alphabet
  • 26. Mining declarative processes RespondedExistence(A, B) ? RespondedExistence(A, C) ? … Response(A, B) ? Response(A, C) ? … SEITE 26 • Support: fraction of cases fulfilling the constraint • Confidence: support scaled by fraction of traces in which the activation occurs • Interest factor: confidence scaled by fraction of traces in which the target occurs Support Conf. I.F.
  • 27. Mining declarative processes RespondedExistence(A, B) ? RespondedExistence(A, C) ? … Response(A, B) ? Response(A, C) ? … SEITE 27 Support Conf. I.F. • Support: fraction of cases fulfilling the constraint • Confidence: support scaled by fraction of traces in which the activation occurs • Interest factor: confidence scaled by fraction of traces in which the target occurs
  • 28. Mining declarative processes RespondedExistence(A, B)  RespondedExistence(A, C)  … Response(A, B) ? Response(A, C)  … SEITE 28 Support Conf. I.F. • Support: fraction of cases fulfilling the constraint • Confidence: support scaled by fraction of traces in which the activation occurs • Interest factor: confidence scaled by fraction of traces in which the target occurs
  • 29. Mining declarative processes RespondedExistence(A, B)  RespondedExistence(A, C)  … Response(A, B)  Response(A, C)  … SEITE 29 Support Conf. I.F. • Support: fraction of cases fulfilling the constraint • Confidence: support scaled by fraction of traces in which the activation occurs • Interest factor: confidence scaled by fraction of traces in which the target occurs
  • 30. Constraints mining An example SEITE 30 A A B C A B C A C B C D A C C A B B C B C A C B B D C C C C C A A B C A A B A A B A B B B D C B A B D A B B D A A A C A C B D A B C D C A B A A C C B B D B C C D C A A C C C A A B C B C C B D
  • 31. The role of Support (event-based) A A B C A B C A C B C D A C C A B B C B C A C B B D C C C C C A A B C A A B A A B A B B B D C B A B D A B B D A A A C A C B D A B C D C A B A A C C B B D B C C D C A A C C C A A B C B C C B D  Support for  Response(A, B)  1.0  Precedence(A, B)  0.926 (25/27)  Pruning on the basis of a support threshold  E.g., 0.95  A threshold equal to 1.0 for a constraint means “always valid in the log” Activation Target Target Activation SEITE 31 http://github.com/cdc08x/minerful Prom Nightly Builds > “Declare MINERful” plug-in
  • 32. The role of Support (trace-based) A A B C A B C A C B C D A C C A B B C B C A C B B D C C C C C A A B C A A B A A B A B B B D C B A B D A B B D A A A C A C B D A B C D C A B A A C C B B D B C C D C A A C C C A A B C B C C B D  Support for  Response(A, B)  1.0  Precedence(A, B)  0.818 (9/11)  Pruning on the basis of a support threshold  E.g., 0.95  A threshold equal to 1.0 for a constraint means “always valid in the log” SEITE 32 http://sourceforge.net/projects/prom/files/ProM/6.3
  • 36. Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Increase the expressiveness of discovered models  Specify templates SEITE 36
  • 37. Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Increase the expressiveness of discovered models  Specify templates SEITE 37 © Granger, NYC, source: https://www.granger.com/
  • 38. Redundancies in discovered constraints  E.g.,  A trace like A B A B C A B C C satisfies (w.r.t. A and B):  RespondedExistence(A, B), RespondedExistence(B, A), Response(A, B), AlternateResponse(A, B), ChainResponse(A, B), Precedence(A, B), AlternatePrecedence(A, B), ChainPrecedence(A, B), CoExistence(A, B), CoExistence(B, A), Succession(A, B), AlternateSuccession(A, B), ChainSuccession(A, B)  Among them, the mining algorithm should return only the most restricting constraint – i.e., ChainSuccession(A, B)  In order to face this issue, returned constraints are pruned on the basis of the subsumption hierarchy of constraints SEITE 38
  • 40. Pruning redundant constraints 0.8 0.8 0.9 0.90.9 0.9 0.7 0.7 0.9 0.9 0.9 ✖ ✖ ? ? ✖ ✖ ✖ ? ? S u p p o r t ✖ ✖ ✖ ✖ SEITE 40
  • 41. Mining declarative processes RespondedExistence(A, B)  RespondedExistence(A, C) ? … Response(A, B)  Response(A, C)  … SEITE 41 Support Conf. I.F.
  • 42. Mining declarative processes RespondedExistence(A, B)  RespondedExistence(A, C) ? … Response(A, B)  Response(A, C)  … SEITE 42 Support Conf. I.F.
  • 43. Mining declarative processes RespondedExistence(A, B)  RespondedExistence(A, C)  … Response(A, B)  Response(A, C)  … SEITE 43 Support Conf. I.F. http://github.com/cdc08x/minerful Prom Nightly Builds > “Declare MINERful” plug-in http://sourceforge.net/projects/prom/files/ProM/6.3
  • 44. Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Increase the expressiveness of discovered models  Specify templates SEITE 44
  • 45. Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Increase the expressiveness of discovered models  Specify templates Interlude DECLARE & Regular Expressions SEITE 45
  • 46. Mining declarative processes RespondedExistence(A, B) RespondedExistence(A, C) and Response(A, B) Response(A, C) and … SEITE 46
  • 47. Semantics of Declare: Regular Expressions SEITE 47
  • 48. From constraints-based model to FSA SEITE 48 [^a]*((a.*b.*)|(b.*a.*))*[^a]* [^a]*(a.*c)*[^a]*  Regular Expression Deterministic Finite State Automaton RespondedExistence(A, B) RespondedExistence(A, C) and Response(A, B) Response(A, C) and …
  • 49. Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Increase the expressiveness of discovered models Reprise SEITE 49
  • 50. While mining a real-life log… SEITE 50
  • 51. Time to challenge the X SEITE 51 
  • 53. The problem  When support threshold is lower than 100%, constraints can be valid through most of the log, though being in conflict  Example: an event log consists of two traces: 1. <A, B, A, B, A, B, C> 2. <A, B, A, B, A, C>  Support threshold: 0.7 • a is always the first  Init(A) • c is always the last  End(C) • In 6 cases over 8 (75%), a and c do not directly follow each other  NotChainSuccession(A, C) • In 5 cases over 7 (71.143%), b and c do not directly follow each other  NotChainSuccession(B, C) SEITE 53
  • 54. The problem  When support threshold is lower than 100%, constraints can be valid through most of the log, though being in conflict  Example: an event log consists of two traces: 1. <A, B, A, B, A, B, C> 2. <A, B, A, B, A, C>  Support threshold: 0.7 • a is always the first  Init(A) • c is always the last  End(C) • In 6 cases over 8 (75%), a and c do not directly follow each other  NotChainSuccession(A, C) • In 5 cases over 7 (71.143%), a and b do not directly follow each other  NotChainSuccession(B, C)  Question: what can be done right before C?  inconsistency! SEITE 54
  • 55. The algorithm to detect inconsistencies SEITE 55  Init(a) Participation(b) AtMostOne(c) End(c) ChainPrecedence(a, b) … ChainSuccession(a, b) Response(a, b) ChainResponse(b, a) NotChainSuccession(a, c) ChainSuccession(b, a) NotChainSuccession(b, c) … … … 1
  • 56. The algorithm to detect inconsistencies  Init(a) Participation(b) AtMostOne(c) End(c) ChainPrecedence(a, b) … ChainSuccession(a, b) Response(a, b) ChainResponse(b, a) NotChainSuccession(a, c)  ChainSuccession(b, a) NotChainSuccession(b, c) … … … 1 2 SEITE 56 http://github.com/cdc08x/minerful
  • 58. Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Increase the expressiveness of discovered models SEITE 58 © Granger, NYC, source: https://www.granger.com/
  • 59. The application of the method to minimise the model  Rationale: 1. How to find redundancies among constraints?  Use the automaton-model correspondence  Same language recognised after the product?  Main difference with the inconsistency- checking algorithm  Constraints having support 100% are checked for redundancies  More details in the paper SEITE 59 http://github.com/cdc08x/minerful
  • 60. The application of the method to minimise the model SEITE 60 BPIC 2012
  • 61. Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Inspect the imperative counterpart  Simulate runs  Increase the expressiveness of discovered models  Specify templates SEITE 61
  • 62. From declarative to imperative models SEITE 62 Response(Queued, Accepted) NotChainSuccession(Queued, Completed) Response(Queued, Completed) NotChainSuccession(Queued, Unmatched) Response(Accepted, Completed) End(Completed) NotSuccession(Completed, Unmatched) AtMostOne(Unmatched) RespondedExistence(Unmatched, Accepted) AlternateResponse(Unmatched, Completed)    …
  • 63. Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Inspect the imperative counterpart  Simulate runs  Increase the expressiveness of discovered models  Specify templates SEITE 63
  • 64. Generation of logs SEITE 64 http://github.com/cdc08x/minerful http://github.com/processmining/synthetic-log-generator
  • 65. Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Inspect the imperative counterpart  Simulate runs  Increase the expressiveness of discovered models  Specify templates SEITE 65
  • 66. Target-Branched Declare Support for  Response(A, {B, C})  1.0 B A A B A B C D A A D B B A A D C B A D D A D D A B E C A B C D A D B D A E A C A A A D B D A D D B D A A A D C D A D D D A C A A B E D E B C E E C B E SEITE 66 Activation Target Branching factor = 2
  • 67. Objective SEITE 67 + Expressive Power + Conciseness
  • 68. Evaluation: constraints pruning SEITE 68 BPI Challenge 2012 (0) 6,654,480 (1) 10,676 (2) 1446 (3) 12
  • 69. Evaluation: performance SEITE 69 BPI Challenge 2012 (KB) 00:07.274 (Const’s) 25:51.678 (Total) 26:11.380 http://github.com/cdc08x/minerful
  • 70. Make mined models intelligible  Prune irrelevant/redundant information  Detect inconsistencies (yes, it happens)  Picture what the model tells  Inspect the imperative counterpart  Simulate runs  Increase the expressiveness of discovered models  Specify templates SEITE 70
  • 71. Semantics of Declare: LTL and LTLf  Linear Temporal Logic (LTL) initially was a specification language for the execution of (endless) concurrent programs (Pnueli, 1977)  Syntax (let A be a propositional symbol):  DECLARE was initially based on LTL SEITE 71 “Until” “Eventually”“Always” “Next”
  • 73. Declarative process modelling  “Open model”  Specify constraints for permitted behaviour  Every execution that complies with them is acceptable  Works best with flexible processes  The set of DECLARE templates is extendible SEITE 73
  • 74. Extendibility of DECLARE: A clear business impact SEITE 74 Source: http://businessvalueexchange.com/
  • 75. Semantics of Declare: LTL and LTLf  Linear Temporal Logic (LTL) initially was a specification language for the execution of (endless) concurrent programs (Pnueli, 1977)  Syntax (let A be a propositional symbol):  Interpretation over infinite traces, i.e., an infinite sequence of consecutive instants of time  LTLf formulae are meant to be interpreted over finite traces “Until” “Eventually”“Always” “Next” SEITE 75
  • 79. Semantics of Declare: FOL over finite traces SEITE 79
  • 80. Semantics of Declare: Regular expressions SEITE 80
  • 81. More alternatives for DECLARE spec.: A clear business impact SEITE 81 Source: http://businessconsultantsnewyork.blogspot.co.at/
  • 82. My long-term objective  Establish an algebra of constraint templates, built on a basis of behavioural relations that are:  orthogonal to one another (no entailments, subsumptions…)  covering multiple trace-based behavioural relations definition languages by linear composition  have clear semantics (last but for sure NOT least)  ... SEITE 82
  • 83. Further reading  Presented:  About MINERful:  Claudio Di Ciccio, Massimo Mecella: On the Discovery of Declarative Control Flows for Artful Processes. ACM Trans. Management Inf. Syst. 5(4): 24:1-24:37 (2015)  Simulation of DECLARE models:  Claudio Di Ciccio, Mario Luca Bernardi, Marta Cimitile, Fabrizio Maria Maggi: Generating Event Logs Through the Simulation of Declare Models. EOMAS@CAiSE 2015: 20-36  Discovery of target-branched DECLARE models:  Claudio Di Ciccio, Fabrizio Maria Maggi, Jan Mendling: Efficient discovery of Target-Branched Declare constraints. Inf. Syst. 56: 258-283 (2016)  Getting rid of redundancies and inconsistencies:  Claudio Di Ciccio, Fabrizio Maria Maggi, Marco Montali, Jan Mendling: Ensuring Model Consistency in Declarative Process Discovery. BPM 2015: 144-159  From DECLARE to Petri nets:  Johannes Prescher, Claudio Di Ciccio, Jan Mendling: From Declarative Processes to Imperative Models. SIMPDA 2014: 162-173  More:  First steps towards data awareness of discovered DECLARE models:  Stefan Schönig, Claudio Di Ciccio, Fabrizio Maria Maggi, Jan Mendling: Discovery of Multi-perspective Declarative Process Models. ICSOC 2016: 87-103  Against the little learnibility of DECLARE:  Michael Hanser, Claudio Di Ciccio, Jan Mendling: A New Notational Framework for Declarative Process Modeling. Softwaretechnik-Trends 36(2) (2016)  Guarantee of the significance of discovered DECLARE models:  Fabrizio Maria Maggi, Marco Montali, Claudio Di Ciccio, Jan Mendling: Semantical Vacuity Detection in Declarative Process Mining. BPM 2016: 158-175 SEITE 83