Discovering Declare Maps

R.P. Jagadeesh Chandra Bose (JC)

Joint Work with

Fabrizio M. Maggi and Wil M.P. van der Aalst

The Apriori Approach

• Discover Frequent Activity Sets (Candidate Sets)• {A, B}, {C, E}, {A, E}, …

• Generate Dispositions• (A, B), (B, A), (C, E), (E, C), (A, E), (E, A), …

• Instantiate Constraints• response (A,B), response (B,A), …

• Assess Significance and Prune Constraints• Support, confidence, interest factor, …

F.M. Maggi, R.P.J.C. Bose and W.M.P. van der Aalst. Efficient Discovery of Understandable Declarative Process Models from Event Logs, CAiSE 2012, pp 270-285

The Problem of Too Many Constraints

Naïve approach

Apriori approach

Dealing with Redundancy

Retain the strongest

F.M. Maggi, R.P.J.C. Bose and W.M.P. van der Aalst. A Knowledge-Based Integrated Approach for Discovering and Repairing Declare Maps, CAiSE 2013 (to appear)

Dealing with Redundancy

transitive reduction

Case, M.L.: Online Algorithms To Maintain A Transitive Reduction. In: Department of EECS, University of California, Berkeley, CS 294-8 (2006)

Transitive Reduction (Example)

Transitive Reduction (Mixed Constraints)

Reduction Rules

Putting it all together

Integrating Domain Knowledge

Conceptual Grouping of Activities

Intra-group constraints Inter-group constraints

Conceptual Grouping of Activities

Apriori Declare Map

• Reference set of templates/activities• Repair the map

• add stronger constraints• remove constraints that no longer hold

• Use for selecting pruning metric thresholds

Repairing a Declare Map (Example)

Extending with Data

• Issues• Too many constraints (not all may be interesting)• ambiguities in associating events

− <a, b, c, b>, <a, b, a, b>• Lack of diagnostic information

R.P.J.C. Bose, F.M. Maggi and W.M.P. van der Aalst. Enhancing Declare Maps Based on Event Correlations, BPM 2013 (to appear)

Declare Model with Correlations

Discovering Correlations

• Relationship between attributes• continuous (<, <=, >, >=, =, !=)• string/boolean (=, !=)• timestamps (before, after, time diff)

• Comparable attributes• apriori knowledge• attributes of the same type

Framework

<a, c, b>, <a, a, c, b> (non-ambiguous)<a, b, b>, <a, a, b, b> (ambiguous)

# instances where correlation is true

# instancesSupport (correlation) =

Discovered Correlations (Example)

A = First outpatient consultation,B = administrative fee - the first polC = unconjugated bilirubin D = bilirubin- totalE = rhesus factor d - Centrifuge methodF = red cell antibody screening

Pruning Constriants

Discriminatory Patterns

• Constraint activations can be classified into different categories• conformant vs. non-conformant• slow, medium, fast based on their response times• …

Framework

• Class Labeling• Feature Extraction

• feasible correlations• antecedent activity attributes• case-level attributes

• Discover Patterns

Discriminatory Patterns (Example)

• response (A,B): 517 non-ambiguous instances: 60 violations

A = First outpatient consultation,B = administrative fee - the first pol

A.Section is Section 5 AND DiagnosisCodeSet is {106; 823} then violation (TP=5, FP=1)A.Section is not equal to Section 5 AND A.Producercode is SGSX then violation (TP=3, FP=1)

Declare Map with Correlations