1 Graph-Based Process Model Matching Name: Christina Tsagkani Phd Candidate National and Kapodistrian University of Athens Supervisor: Dr. Tsalgatidou Aphrodite

Presentation Outline  Problem Statement and Research Questions  Work Foundation  Results to date  Next steps Christina Tsagkani, Graph-Based Process Model Matching

Application Domains  Areas of application: Process Improvement Business merging - prevent duplication of processes Reuse purposes in Business Process Design- no need to define whole process Christina Tsagkani, Graph-Based Process Model Matching

The need for Process matching  The Big Picture  Business processes are becoming quite complex  Process repositories typically consist of hundreds even thousands process models  In the repository a business process may be represented by several process models having different levels of detail  Heterogeneous environments  Design from scratch is error-prone  Near match 4 Christina Tsagkani, Graph-Based Process Model Matching

Problem Statement and Research Questions  Research questions raised What happens when there is no perfect match amongst graphs? What happens when process repositories become quite large and heterogeneous? What happens with models of different size? (the aim is to make use of the extra knowledge acquired and different process abstraction levels) 5 Christina Tsagkani, Graph-Based Process Model Matching

Research aim  “process model matchmaking mechanism”. Matching based on different metrics aiming at improving the quality of the result and different process aspects (events data input/output, roles), Able to handle large and complex business processes by reducing the search space 6 Christina Tsagkani, Graph-Based Process Model Matching

Presentation Outline  Problem Statement and Research Questions  Work Foundation  Results to date  Next steps Christina Tsagkani, Graph-Based Process Model Matching

Differences in Business Processes  Authorization differences  Behavioral representation( same behavior achieved with different structures),  Labelling styles (eg. verb-object), terminology (different labels same semantics),  Level of granularity (one activity- collection of activities) and  Projections (parts of a model my be left out in another model)  Control-flow differences: same activity different dependencies (pre/post), additional dependencies, disjoint dependencies(no same dependencies), iterative activities/once-off, different conditions for the occurrence of same activity 8 Christina Tsagkani, Graph-Based Process Model Matching

Differences in Business Processes 9 Christina Tsagkani, Graph-Based Process Model Matching

Processes as Attributed Graphs 10 Christina Tsagkani, Graph-Based Process Model Matching

Processes as Attributed Graphs 11 Christina Tsagkani, Graph-Based Process Model Matching

Processes as Attributed Graphs 12 Christina Tsagkani, Graph-Based Process Model Matching

Process Matchmaking  Process matchmaking problem  graph matching problem: as a basis to define similarity measures  Definition: Matching is the process of discovering mappings between two graphs through the application of a matching algorithm Christina Tsagkani, Graph-Based Process Model Matching

Matching Aspects of BPGs 14 Node/Edge Matching Aspects Type Label Attribute Christina Tsagkani, Graph-Based Process Model Matching

Matchmaking process  It uses similarity measurements to identify the optimal process. There are different similarity aspects: Label:  Syntactic: based on a comparison of the labels that appear in the process models (task labels, event labels, etc.) using syntactic  Semantically: based on a comparison of the labels that appear in the process models (task labels, event labels, etc.) using semantics eg.“Customer inquiry processing” and “ Client inquiry query processing” Structural: based on the topology of the process models including syntactic and/or semantic similarity Behavioral: based on the execution semantics of process models Contextual: pre/post conditions. It considers the reasoning behind the design (its goals, intentions…) Christina Tsagkani, Graph-Based Process Model Matching

Matching techniques (metrics)  Label similarity Syntactic: string-edit distance, n-gram, morphological analysis (stemming) and non-stop-word elimination techniques Semantic: synonym and other semantic relations captured in the Thesauri (e.g. Wordnet)  Structural Similarity: can be based on graph-edit distance A* Algorithm Heuristic search Similarity flooding  Behavioral similarity: techniques are based on comparison of traces, simulation (state transition) and causal footprints (E, L lb, L la ) Christina Tsagkani, Graph-Based Process Model Matching

Matchmaking process  The outcome of the process: Exact: equivalent process models Plug-in: request is sub-concept Subsume request is super-concept Intersection: their intersection is satisfiable Disjoint: incompatible Christina Tsagkani, Graph-Based Process Model Matching

Process Matchmaking  Process matchmaking problem  graph matching problem: as a basis to define similarity measures  Definition: Matching is the process of discovering mappings between two graphs through the application of a matching algorithm Christina Tsagkani, Graph-Based Process Model Matching

Review of graph matching algorithms [1/2] We distinguish:  Exact match: perfect match eg. Ulman algorithm.  Inexact matching: finds mappings that minimize the matching cost  Graph edit distance that is well suited for structural similarity.  Algorithms based on branch and bound  Artificial neural networks  Relaxation labeling  Expectation maximization algorithm  Graduated assignment  Random walks in graphs  Random graphs  Etc. Inexact matches problem: existing techniques focus on performance and no systematic evaluation of solution quality Christina Tsagkani, Graph-Based Process Model Matching

Review of graph matching algorithms [2/2]  Exact matching: exponential time O (2 n )  Inexact matching: Optimal inexact matching: always find a solution that is the global minimum of the matching cost. Approximate or suboptimal: only ensure to find a local minimum of the matching cost, execute in polynomial time O(n K ) Christina Tsagkani, Graph-Based Process Model Matching

Inexact Matching algorithms- Graph Edit Distance  The challenge for inexact matching: how much the graphs differ. Why choose GED as a graph matching technique? “Compared to other approaches to graph matching, graph edit distance is known to be very flexible since it can handle arbitrary graphs and any type of node and edge labels. Furthermore, by defining costs for edit operations the concept of edit distance can be tailored to specific applications”  GED definition: The idea of graph edit distance is to define the dissimilarity of graphs by the amount of distortion that is needed to transform one graph into another. Christina Tsagkani, Graph-Based Process Model Matching

Graph edit distance computation Christina Tsagkani, Graph-Based Process Model Matching

A* Algorithm  Is designed to provide exact match  Is based on Graph Edit Distance  Mapping means edit operations Christina Tsagkani, Graph-Based Process Model Matching

A* Algorithm  Example Graph Q: Graph T: Solution: fae bdg fae baedae gae C=4 C=1 C=5 gbegde C=2 C=4 gbd C=3 Christina Tsagkani, Graph-Based Process Model Matching

The Process matching Contest AuthorsNode Similarity Structural Similarity Behavioural Similarity SyntacticSemantic Triple-S Cayoglu, Oberweis, Schoknecht, Ullrich √√√ Business Process Graph Matching Dijkman, Dumas, Garcia- Banuelos √√√ RefMod- Mine/NSCM Thaler, Hake, Fettke, Loos √√ RefMod- Mine/ESGM Hake, Thaler, Fettke, Loos √√ Bag-of-Words Similarity with label Pruning Klinkmuller, Weber, Mendling, Leopold, Ludwig √√ ICoP framework Weidlich, Dijkman, Mendling √ √

Presentation Outline  Problem Statement and Research Questions  Work Foundation  Results to date  Next steps Christina Tsagkani, Graph-Based Process Model Matching

Originality  The matcher: The matcher will support inexact matches Similarity measurement based on different viewpoints other than structural viewpoint (data manipulation, data inputs/outputs and resource allocation) Different Process Granularity Different process projections Search space limitations The quality of the results will be evaluated using heuristics and further pruned

Extension of the A* algorithm  Reduce the search space: arrange nodes in sets based on type  Alter the algorithm so that the following statement is true: “When a leaf “A” and a child node have the same smallest cost value, then to output the path “A” as the solution (and not to expand the child node) Christina Tsagkani, Graph-Based Process Model Matching

Results to Date  The matchmaking models’ functionality presented in steps: 29 Christina Tsagkani, Graph-Based Process Model Matching

Presentation Outline  Problem Statement and Research Questions  Work Foundation  Results to date  Next steps Christina Tsagkani, Graph-Based Process Model Matching

Next Steps  Implement the algorithm  Algorithm Evaluation  Address the issue of Different Process Granularity  Finalize the matcher architecture 31