Cross-domain graph based similarity measurement of workflows
Abstract The aim of this article is to analyze search and retrieval of workflows. It represents workflows relatedness based on transfer learning. Workflows from different domains (e.g. scientific or business) have similarities and, more important, differences between themselves. Some concepts and solutions developed in one domain may be readily applicable to the other. This paper proposes a cross-domain concept extraction by similarity measurement and has a new research effort at the intersection of workflow domains. It deals with the huge amount of structured and unstructured data (Big Data) that is a demanding task when working on real-life event logs. The proposed method in this paper gives a general solution in the sense that it can be coupled to any Process Aware Information System..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2018 |
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| Erschienen: |
2018 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:5 |
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| Enthalten in: |
Journal of Big Data - 5(2018), 1, Seite 16 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Tahereh Koohi-Var [VerfasserIn] |
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| Links: |
doi.org [kostenfrei] |
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| Themen: |
Big Data |
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| doi: |
10.1186/s40537-018-0127-6 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
DOAJ044429541 |
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| 520 | |a Abstract The aim of this article is to analyze search and retrieval of workflows. It represents workflows relatedness based on transfer learning. Workflows from different domains (e.g. scientific or business) have similarities and, more important, differences between themselves. Some concepts and solutions developed in one domain may be readily applicable to the other. This paper proposes a cross-domain concept extraction by similarity measurement and has a new research effort at the intersection of workflow domains. It deals with the huge amount of structured and unstructured data (Big Data) that is a demanding task when working on real-life event logs. The proposed method in this paper gives a general solution in the sense that it can be coupled to any Process Aware Information System. | ||
| 650 | 4 | |a Scientific workflows | |
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