user guided discovery of declarative process models

User-Guided Discovery of Declarative Process Models

Fabrizio Maria Maggi, Arjan Mooij,

Wil van der Aalst

Environment with a lot of variability

Department of Mathematics and Computer Science PAGE 223-09-14

Discovery of Spaghetti-like models


Declarative approaches


Declarative process discovery

• Avoid the discovery of spaghetti-like models• Traditional discovery techniques explicitly specify all

possible behaviours (closed models)

• Declarative process discovery: process behaviour described as a compact set of rules (open models)



• Possibility to guide the discovery process towards specific properties of interest




A is always eventually followed by

B




B

A or B always

occur but never

together




B

A or B always

occur but never

together

A and B never

occur in sequence

Declare

• A is always eventually followed by B• RESPONSE• User-friendly graphical representation

• Semantics specified through LTL (for finite traces)


Core algorithm

PAGE 1723-09-14

LOG

User-guided discovery


Core algorithm


• W = {(A C B C), (C B A C), (A C A C A C B)}

Core algorithm



Tuning the discovery process: PoE

• Percentage of Events (PoE) avoids the discovery of less-relevant constraints referring to event classes which rarely occur in the log• This parameter has also a positive effect on the execution

time of the algorithm


PoE parameter



PoE parameter


• W = {(A C B C), (C B A C), (A C A C A C B)}− f(A) = 5/15

− f(B) = 3/15

− f(C) = 7/15

PoE parameter



− f(B) = 3/15

− f(C) = 7/15

Tuning the discovery process: PoI

• Percentage of Instances (PoI) specifies that a constraint can still be discovered even if it does not hold for all process instances of the log • This parameter is useful in case of noisy logs, where rules

are violated in exceptional cases, but hold for most cases


PoI parameter



PoI parameter



3/32/3

2/31/3

1/3 1/3

Truncated semantics for DECLARE constraints


• W = {(A C D B C D A E F B A), (C A D B C A D C B F D A D B C A D E F), (A C B D A E B F A E F)}


• Relevant when logs are not complete

• Literature on Truncated Semantics• C. Eisner, D. Fisman, J. Havlicek, A. Mcisaac, Y. Lustig, and D. V.

Campenhout, “Reasoning with Temporal Logic on Truncated Paths,” in In CAV Proceedings, LNCS 2725, pp. 27–40, 2003




• After every prefix, four evaluations of a constraint− Satisfied : independent of future

− Temporarily satisfied : satisfied if this is the end of the log− Temporarily violated : violated if this is the end of the log− Violated : independent of future

• Three kinds of semantics:• Weak: temporarily xxx satisfied

• Neutral: temporarily xxx xxx

• Strong: temporarily xxx violated




(Weak semantics)

Vacuity detection in DECLARE discovery


• W = {(C B C B E F ), (C B C B C F B C B E F), (C B E F E F)}



• Literature on Vacuity Detection• O. Kupferman and M. Y. Vardi, “Vacuity Detection in Temporal

Model Checking,” International Journal STTT, vol. 4, pp. 224–233, 2003



• A constraint is vacuously satisfied if the constraint is not really “activated”

• Instead of checking the validity of a constraint c we check the validity of witness(c) to be sure that the constraint is non-vacuously satisfied



c



witness(c)

Conclusion


• Novel approach to discover declarative models from logs that allows users to guide the discovery process towards specific properties

• Results on truncated semantics can be used to obtain significant results in the case that only partial logs are available

• Vacuity detection to identify the percentage of process instances where a constraint is really activated

Present and future work


• Better performance of the discovery algorithm• equivalent combinations

• combination of event classes occurring in the same trace

Present and future work


• Application of the approach to several case studies

• Given a constraint and a process instance where it is non-vacuously satisfied how many times it has been “activated” in the process instance

• Given a constraint and a process instance where it is violated level of “healthiness” of the process instance based on the number of violations

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http://www.win.tue.nl/declare/declare-miner/


http://www.win.tue.nl/declare/declare-miner/declareminer-2/

user guided discovery of declarative process models

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