Automatic service derivation from business process model repositories via semantic technology

Leopold, Henrik; Pittke, Fabian; Mendling, Jan

doi:10.1016/j.jss.2015.06.007

Cited by 19 publications

(9 citation statements)

References 44 publications

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“…Lastly, there are approaches that distill information by analyzing the labels of activities in a process model repository. Mostly, such techniques focus on the detection of service candidates, which correspond to process patterns that occur repeatedly throughout process models in a collection [100,142,168,207].…”

Section: Applications In Business Process Managementmentioning

confidence: 99%

Comparing and Aligning Process Representations

2018

Lecture Notes in Business Information Processing

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Section: Applications In Business Process Managementmentioning

confidence: 99%

Comparing and Aligning Process Representations

2018

Lecture Notes in Business Information Processing

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“…The first kind is a top-down domain-based decomposition method, which mainly decomposes business elements into a certain granularity, called a service [37]- [40]. The second kind is the bottom-up method, which mainly analyzes the existing sets of functions in information systems and extracts certain functions as services; these services can be then identified [41]. The third kind is a combination of the top-down and bottom-up approaches.…”

Section: Research On Service Identification Methodsmentioning

confidence: 99%

A Semi-Automatic Optimization Design Method for SvcV-5 in DoDAF 2.0 Based on Service Identification

Zhang¹,

Luo²,

Mao³

et al. 2020

IEEE Access

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As one of the products in the US Department of Defense Architecture Framework version 2.0 (DoDAF 2.0), the Operational Activity to Services Traceability Matrix product (SvcV-5) links the operational viewpoint (OV) and service viewpoint (SvcV). SvcV-5 is an essential product for optimizing the design of other service view products; thus, it is necessary to study how to optimize the design of SvcV-5. This paper studies the semi-automatic design method of SvcV-5 based on the service identification method. The main idea of this paper is to extract relevant information from previous architecture design products, such as OV-6b, CV-4 and CV-6, and then to study the method of taking service identification and designing the SvcV-5 product. First, a message-based service initial identification method is used to generate the initial service set. Second, considering the measure of the cohesion and the coupling degree of the service, a service improving algorithm is used to update the initial service set. In this process, a dependency matrix between operational activities is established; this matrix includes data dependencies and capability requirement dependencies. Then, according to the mapping result of the service identification method, service-related information can be determined manually by designers. Finally, SvcV-5 can be generated through an automatic algorithm based on the service identification result and the OV-6b product. This article verifies the proposed method using the motivation example of the earthquake rescue information system (ERIS) architecture product design. Through conducting experimental analysis and comparing our method with other service identification methods, we illustrate the effectiveness of the proposed SvcV-5 optimization design method.

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“…It also demonstrates a direction of an actor activity towards the objective [56]. No No Master Data and logical data model [40] No Yes Process and goal, action-rule [41] No No Business Process [42] No No Dependency among Business Process [43] No No Business Process Repository [44] No No BPs, goal, and data model [45] No Yes Partner's interaction, people, machine [36] No No System Usage [46] No No Data flow Diagram [47] No No Source Code [48] No No Data object Dependency [49] No Yes Collaborative business Process [10] No No Source Code [50] No No Data Flow Diagram [37] No No Business Processes Models [51] No No Access Logs [52] No No Multiple software products [7] Yes Yes Business Processes, dataflow dependency Within context consideration; A= SMEs, B = Collaboration, C = Input used for the SI analysis…”

Section: At Collaboration Activity and Simentioning

confidence: 99%