Applying Process Mining in SOA Environments

Khan, Altaf; Lodhi, Azeem; Köppen, Veit; Kassem, Gamal; Saake, Gunter

doi:10.1007/978-3-642-16132-2_28

Cited by 11 publications

(8 citation statements)

References 12 publications

(14 reference statements)

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“…They were motivated by the identification of: crosscutting concerns (aspects) [14], business processes [15], patterns of interests among service users [16], [17], and features either in OO systems [18] or SBSs [19]. Further related work focused on the identification of service composition patterns [20], i.e.…”

Section: B Knowledge Extractionmentioning

confidence: 99%

Improving SOA antipatterns detection in Service Based Systems by mining execution traces

Nayrolles

Moha

Valtchev

2013

2013 20th Working Conference on Reverse Engineering (WCRE)

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Abstract-Service Based Systems (SBSs), like other software systems, evolve due to changes in both user requirements and execution contexts. Continuous evolution could easily deteriorate the design and reduce the Quality of Service (QoS) of SBSs and may result in poor design solutions, commonly known as SOA antipatterns. SOA antipatterns lead to a reduced maintainability and reusability of SBSs. It is therefore important to first detect and then remove them. However, techniques for SOA antipattern detection are still in their infancy, and there are hardly any tools for their automatic detection. In this paper, we propose a new and innovative approach for SOA antipattern detection called SOMAD (Service Oriented Mining for Antipattern Detection) which is an evolution of the previously published SODA (Service Oriented Detection For Antpatterns) tool. SOMAD improves SOA antipattern detection by mining execution traces: It detects strong associations between sequences of service/method calls and further filters them using a suite of dedicated metrics. We first present the underlying association mining model and introduce the SBS-oriented rule metrics. We then describe a validating application of SOMAD to two independently developed SBSs. A comparison of our new tool with SODA reveals superiority of the former: Its precision is better by a margin ranging from 2.6% to 16.67% while the recall remains optimal at 100% and the speed is significantly reduces (2.5+ times on the same test subjects).

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Section: B Knowledge Extractionmentioning

confidence: 99%

Improving SOA antipatterns detection in Service Based Systems by mining execution traces

Nayrolles

Moha

Valtchev

2013

2013 20th Working Conference on Reverse Engineering (WCRE)

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“…The executional data (or process trace data [12,26]) can be used to specify the usage of which activities, organizational resources, and involved elements add more value to an enterprise. Enterprises can define their own metrics.…”

Section: A Modelling Patternsmentioning

confidence: 99%

An Analytical Business Process Modelling Language: An Illustrative Example

Lodhi

Köppen

Wind

et al. 2013

2013 IEEE 10th International Conference on E-Business Engineering

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Abstract-Fierce competition exists between companies due to globalization where even small companies have a reach to wider international markets using the internet (e-business). Evaluation of business processes is very important for analysis and improvement for an organization. Different methods are used to evaluate the performances of business processes. However, there is insufficient support to evaluate the processes with detailed graphical representations. In this paper, we focus on the relation of evaluation of business processes with its representation. We follow the design science methodology in order to provide business process models for performance analysis of processes. We also provide the constructs and patterns of business process modelling for analysis and improvement of business processes. The analytical business process modelling language is highlighted with the help of a case study.

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“…Different techniques related with process mining [1] attempt to solve the issues related with data collection [7,8] and its conversion [9,10]. Similarly in [11], author proposes a data format for recording business events.…”

Section: Framework For Improvementmentioning

confidence: 99%

Business Process Improvement Framework and Representational Support

Lodhi

Köppen

Saake

2012

Advances in Intelligent Systems and Computing

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Abstract. Business process management and improvement are vital for enterprises in competitive environments. Understanding of a process is a pre-requisite and important step for improvement. Interaction between humans, computers, and business objects provide excellent opportunities for knowledge extraction. However, the specification of a framework is required for business process improvement, which extends from data collection, analytical methods, storage, and representation of knowledge. The process models conceived for information system development are not sufficient for post execution analysis and improvement. In this paper, we specify such a framework briefly and focus on providing representational support for business process improvement. The main objective is to improve the overall improvement process by providing enriched graphical process models. Furthermore, we use a case study to explain the proposed usage and extensions of an existing modeling language for business process improvement.

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Applying Process Mining in SOA Environments

Cited by 11 publications

References 12 publications

Improving SOA antipatterns detection in Service Based Systems by mining execution traces

Improving SOA antipatterns detection in Service Based Systems by mining execution traces

An Analytical Business Process Modelling Language: An Illustrative Example

Business Process Improvement Framework and Representational Support

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