Workflow Clustering Using Semantic Similarity Measures

Bergmann, Ralph; Müller, Gilbert; Wittkowsky, Daniel

doi:10.1007/978-3-642-40942-4_2

Cited by 4 publications

(10 citation statements)

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“…Further cluster analysis as an established method allows discovering the structure in collections of data by exploring similarities between data points. By cluster analysis one can group data objects in such a way that data objects within a cluster are similar, while data objects of different clusters are dissimilar to one another [5].…”

Section: Workflow Representationmentioning

confidence: 99%

Scientific Workflow Clustering Based on Motif Discovery

Koohi-Var¹,

Zahedi²

2017

IJCSEIT

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In this paper, clustering of scientific workflows is investigated. It proposes a work to encode workflows through workflow representations as sets of embedded workflows. Then, it embeds extracted workflow motifs in sets of workflows. By motifs, common patterns of workflow steps and relationships are replaced with indices. Motifs are defined as small functional units that occur much more frequently than expected. They can show hidden relationships, and they keep as much underlying information as possible. In order to have a good estimate on distances between observed workflows, this work proposes the scientific workflow clustering problem with exploiting set descriptors, instead of vector based descriptors. It uses k-means algorithm as a popular clustering algorithm for workflow clustering. However, one of the biggest limitations of the k-means algorithm is the requirement of the number of clusters, K, to be specified before the algorithm is applied. To address this problem it proposes a method based on the SFLA. The simulation results show that the proposed method is better than PSO and GA algorithms in the K selection.

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Section: Workflow Representationmentioning

confidence: 99%

Scientific Workflow Clustering Based on Motif Discovery

Koohi-Var¹,

Zahedi²

2017

IJCSEIT

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“…The seamless combination of structure and semantic similarities is an urgent challenge for workflow retrieval. Bergmann and Gil presented a new similarity model which could seem as an enhancement of the well-known local/global approach [28]. However, it seems that they had not considered the constraints between semantic information of nodes and data types.…”

Section: Related Workmentioning

confidence: 99%

“…Definition 11 (distance metric [28]). Based on Definition 10, one uses a distance metric proposed in [28] for measuring the similarity between two constrained data oriented workflows CDW and CDW whose normalized matrices are, respectively, NM and NM . Their distance metric between NM and NM is defined as follows:…”

Section: Normalized Matrices and Distance Metricmentioning

confidence: 99%

“…In this paper, we will evaluate the effectiveness and efficiency of our method by comparing it with the three methods. We use the two indices: degree of richness [29] E x TMCS DRR MCS Method in [7] E x T CW DRR CW Method in [28] E x TSSW DRR SSW of retrieval (DRR) and time complexity. Evaluation of time complexity is mainly done by computing the execution time of workflow retrieval.…”

Section: Evaluation Indices Workflow Repository and Benchmarkmentioning

confidence: 99%

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Measuring Semantic and Structural Information for Data Oriented Workflow Retrieval with Cost Constraints

Zhang

Shi

2014

Mathematical Problems in Engineering

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The reuse of data oriented workflows (DOWs) can reduce the cost of workflow system development and control the risk of project failure and therefore is crucial for accelerating the automation of business processes. Reusing workflows can be achieved by measuring the similarity among candidate workflows and selecting the workflow satisfying requirements of users from them. However, due to DOWs being often developed based on an open, distributed, and heterogeneous environment, different users often can impose diverse cost constraints on data oriented workflows. This makes the reuse of DOWs challenging. There is no clear solution for retrieving DOWs with cost constraints. In this paper, we present a novel graph based model of DOWs with cost constraints, called constrained data oriented workflow (CDW), which can express cost constraints that users are often concerned about. An approach is proposed for retrieving CDWs, which seamlessly combines semantic and structural information of CDWs. A distance measure based on matrix theory is adopted to seamlessly combine semantic and structural similarities of CDWs for selecting and reusing them. Finally, the related experiments are made to show the effectiveness and efficiency of our approach.

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“…Costa also adopted the method of annotating workflow tags to determine the similarity of workflow [9]. Bergmann introduced semantics by adding tags to each node of workflow, and then summarized the similarity of workflow by comparing the semantics of nodes [10]. Stoyanovich also compares similarities by assigning keyword labels to workflows [11].…”

Section: Introductionmentioning

confidence: 99%