Aligning Event Logs And Declare Models for Conformance Checking Massimiliano de Leoni, Fabrizio Maggi Wil van der Aalst

Conformance Checking of Declare Models A large stream of research about conformance checking of procedural models For declarative models, existing approach tends to provide poor diagnostics. Impossible to determine to which extent traces are conforming −Only yes/no answers are provided −Only capable to determine whether single constraints are violated No way to pinpoint where deviations mostly occur and how they can be fixed PAGE 1

Contribution Aligning Event Logs And Declare Models for Conformance Checking, thus Pinpointing where deviations occur for each trace Highlighting which Declare constraints and activities are more subject to misconformances. For Declarative model, everything is allowed unless explicitly forbidden. The set of behaviours is far larger, compared with procedural models Essential to prevent from visiting large portions of the search space PAGE 2

Example / 1 Declare model D = (A,Π) where A is a set of activities Π is a set of Declare constraints over A. PAGE 3

Log Preprocessing The activity set in the log may include all model activities and more Every activity in the log is mapped to √ if it does not have counterpart in the model Not possible to simply ignore log activities with no counterpart, due to the «next» operator! PAGE 4 L =

Process Behaviour Set PAGE 5 For each constraint, an automaton is created. All automata are defined over the input alphabet ∑ = A ⋃ { √ } The process behaviour set P D = ∑* is the set of sequences accepted by all automata π ϵ Π

Aligning Declare Models and Logs PAGE 6 L = Not a “complete” alignment! “Complete” alignment!

Alignment cost The cost of each alignment step is given by a function: k : ∑ A  N 0 where ∑ A =( ∑ ⋃ {  } ) x ( ∑ ⋃ {  } ) / {( ,  )} The cost of an alignment γ ϵ ∑ A * is: K( γ ) = ∑ (a’,a’’) γ k (a’,a’’) PAGE 7 : Cost of “move on model” : Cost of “move on log” √ 23

Optimal alignment Given a log trace σ L and a declare model D, we aim at find an optimal alignment μ’ between σ L and D −Optimal alignment only if complete alignment −For all possible complete alignments μ’ between σ L and D, K( γ ) >= K( γ ’) In order to find an optimal alignment, we use the A* algorithm. SEARCH-SPACE NODES: alignments SUCCESSORS OF A NODE: alignments extended with a move in both or, else, with a move only in log/process GOAL NODES: complete alignments PAGE 8

Search Space Tree Strategy: Best First Search based on heuristics (A*) Expand a state with the least cost until the state with the least cost is also a goal state The cost of a certain state is determined as the sum of two parts: 1.The actual cost undertaken to reach the current state from the initial state 2.The estimated cost to reach a final state from the current state PAGE Cost 3 … … … … …

Pruning of the search space Declare models allow for more flexibility. The search space in the A* algorithm may be extremely large. Important to prune the search space for «useless» nodes, i.e. certaintly yielding no optimal solutions. PAGE 10

Pruning: Alignment Equivalence Alignments can be divided in equivalence classes Two alignments are equivalent: All automata are in the same state, after giving the aligned process as input They align the same sequence prefix of the log trace PAGE 11

Alignment Equivalence & Pruning Equivalent alignment can be extended with the same alignment steps PAGE 12

Performance Experiments on Synthetic Logs Generated a set of synthetic logs using CPNTools with various degrees of non-conformance: 0% till 90% of constraints are violated. PAGE 13

Performances on the WABO Log Used 2 logs from the «WABO» Logs. We have mined a Declare Model from the first log We have checked the conformance of the mined model again the second log. PAGE 14

Why do I need this move? Our technique is capable to indicate the constraint(s) which are solved by a move only in log/model PAGE 15 √ LIC CQ √ CQ SQ √ √ √ Replaying on all automata to check acceptance NOT ACCEPTED BY THE CO-EXISTENCE AUTOMATON √ CQ √ CQ SQ √ √ √ Replaying on all automata to check acceptance NOT ACCEPTED BY THE RESPONSE AUTOMATON

A Screenshot PAGE 16

Diagnostics at Log Level Not easy to sum up where deviations mostly occurrence by looking at the simple alignments. Computation of the «Degree of Conformance» of activities and constraints and projection on the model PAGE 17 Degree of Activity Conform. Degree of Constraint Conform.

Conclusion Many conformance checking approaches defined for procedural models not applicable to declarative models Many approaches tend to provide poor diagnostics We adapted alignment-based approach to declarative models To deal with large search space due to the high flexibility The approach seems practically feasible as it prevents from visiting large portion of the search space. We provide a number of diagnostics both at trace level and at log level PAGE 18