Overcoming individual process model matcher weaknesses using ensemble matching

Meilicke, Christian; Leopold, Henrik; Kuss, Elena; Stuckenschmidt, Heiner

doi:10.1016/j.dss.2017.02.013

Cited by 17 publications

(10 citation statements)

References 8 publications

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“…A graph-based process model consists of labeled nodes of different types (e.g. activities, gateways, events) and directed edges connecting them [12]. When two graph models have been constructed, pattern matching is done by the Graph-based Matching Approach proposed by [6] using a brute force algorithm and Phonetic Text Procedures in Neo4j.…”

Section: Graph-based Matching Approachmentioning

confidence: 99%

“…This method builds a business process model that captures the information arrangement of the business process, after which the similarity value can be derived by process matching, based on edit distance of process models on both of functional and structural similarity, while structure weights are specified to contribute to the conclusion of similarity value. The matching method of [12] identifies the correspondence between the activities in two business process models to calculate the similarity. They proposed the optimization method utilizing Markov logic for choosing the best linkages among results of some similarity matching methods.…”

Section: Introductionmentioning

confidence: 99%

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A New Similarity Method based on Weighted Graph Models for Matching Parallel Business Process Models

Setiawan¹,

Sungkono²,

Sarno³

2020

IJIES

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In business process, similarity is important for comparing between business process models. The existing similarity methods, such as Graph-based Matching Method (GMA), Weighted Graph Edit Distance (WGED), Weighted Node Adjacent Relation Similarity (WNARS), Tree Declarative Pattern Edit Distance (TPED) and Cosine-Tree Declarative Pattern (Cosine-TDP) can distinguish between AND, OR, and XOR relationships. However, they have drawbacks in detecting same relationships with different event logs. This paper proposes a new similarity method based on weighted graph models called weighted graph-based parallel process model matching (WGPPM) for computing the behaviour of parallel activity relationships. The proposed method utilizes the frequency of activity relationships as the weight in the graph model to measure the similarity between processes containing parallel relationships. WGPPM is compared with GMA, WGED, WNARS, TPED, and Cosine-TDP. The result shows that WGPPM is able to compute similarity between same parallel relationship with different event logs.

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Section: Graph-based Matching Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Similarity Method based on Weighted Graph Models for Matching Parallel Business Process Models

Setiawan¹,

Sungkono²,

Sarno³

2020

IJIES

View full text Add to dashboard Cite

show abstract

“…They also emphasize the importance of considering the semantics on PMM. Meilicke et al [63] address the problem of the varying performance of individual process model matching techniques. To this end, they introduce a matching approach that uses the correspondences generated by a set of matchers as input.…”

Section: Using Model Matching To Implement Service Discoverymentioning

confidence: 99%

Business Process Execution From the Alignment Between Business Processes and Web Services: A Semantic and Model-Driven Modernization Process

et al. 2020

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Many companies have implemented their business processes in Web applications which must be frequently adapted so as to stay aligned with new business process requirements. Service-oriented architectures (SOAs) constitute an appropriate option to manage the continuous changes in those processes by facilitating their alignment with the changing underlying system services. In this context, firms are trying to migrate their Web applications to new software architectures such as SOAs. However, this migration is usually carried out ad-hoc by means of non-reusable and error-prone manual processes. Similarly, the alignment between the business processes and the underlying services identified is usually done by hand. This work presents a model-driven semiautomatic approach to modernize legacy Web applications to SOAs. The approach is focused on an automatic semantic process aimed at discovering the services that can be used to implement the business processes (defined by the companies), then aligning these processes with the underlying services. A semantic algorithm is provided to aid the migration architect during the alignment process. The case study carried out shows that the alignment process results obtained by the semantic algorithm presented in this paper are similar to those obtained by the experts manually. Finally, SOA orchestration artifacts are generated from the semantic algorithm results. INDEX TERMS Legacy Web applications, service-oriented architecture, semantic algorithms, model-driven techniques.

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“…A few techniques also employ alternative strategies. Examples include matching techniques incorporating human feedback [21], techniques selecting the most promising similarity measures based on prediction [22], techniques selecting the best correspondences based on voting [23], and techniques that employ machine learning [24].…”

Section: Process Model Matchingmentioning

confidence: 99%

A probabilistic evaluation procedure for process model matching techniques

Kuss

Leopold

et al. 2018

Data & Knowledge Engineering

Self Cite

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Process model matching refers to the automatic identification of corresponding activities between two process models. It represents the basis for many advanced process model analysis techniques such as the identification of similar process parts or process model search. A central problem is how to evaluate the performance of process model matching techniques. Current evaluation methods require a binary gold standard that clearly defines which correspondences are correct. The problem is that often not even humans can agree on a set of correct correspondences. Hence, evaluating the performance of matching techniques based on a binary gold standard does not take the true complexity of the matching problem into account and does not fairly assess the capabilities of a matching technique. In this paper, we propose a novel evaluation procedure for process model matching techniques. In particular, we build on the assessments of multiple annotators to define the notion of a non-binary gold standard. In this way, we avoid the problem of agreeing on a single set of correct correspondences. Based on this non-binary gold standard, we introduce probabilistic versions of precision, recall, and F-measure as well as a distance-based performance measure. We use a dataset from the Process Model Matching Contest 2015 and a total of 16 matching systems to assess and compare the insights that can be obtained by using our evaluation procedure. We find that our probabilistic evaluation procedure allows us to gain more detailed insights into the performance of matching systems than a traditional evaluation based on a binary gold standard.

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Overcoming individual process model matcher weaknesses using ensemble matching

Cited by 17 publications

References 8 publications

A New Similarity Method based on Weighted Graph Models for Matching Parallel Business Process Models

A New Similarity Method based on Weighted Graph Models for Matching Parallel Business Process Models

Business Process Execution From the Alignment Between Business Processes and Web Services: A Semantic and Model-Driven Modernization Process

A probabilistic evaluation procedure for process model matching techniques

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