Reusing Ontological Knowledge about Business Processes in IS Engineering: Process Configuration Problem

Ciuksys, Donatas; Čaplinskas, Albertas

doi:10.15388/informatica.2007.195

Cited by 11 publications

(4 citation statements)

References 21 publications

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“…They represent an efficient solution for modeling multi-tenant business processes [4]. Many configurable process modeling languages [3], [27], [28], [29] have been proposed for enabling customizable process constructs. However, these works have focused on the variability modeling in the control flow perspective and overlooked the resource perspective.…”

Section: A Results Analysismentioning

confidence: 99%

Simulation of Configurable Resource Allocation for Cloud-Based Business Processes

Ahmed-Nacer

Suri

Sellami

et al. 2017

2017 IEEE International Conference on Services Computing (SCC)

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Cloud computing has enabled provisioning of scalable and virtualized resources to organizations in an ubiquitous and on-demand manner. Likewise, configurable process modeling has enabled organizations to reuse their existing knowledge by sharing a reference process model between different tenants that can be customized according to specific needs. Nevertheless, there are some limitations in this context, (i) it is not costeffective to make use of real cloud infrastructure such as, Amazon EC2 for experimenting and analyzing the deployment of the best process variant, (ii) frequent changes in the configurations during experimentation using a real cloud setup involves various manual adjustments which is a time-consuming task, (iii) certain conditions prevailing in the internet-based cloud environments are beyond the control of the practitioners involved in such analysis. Thus, making use of simulation environments is one of the good alternative for testing and analyzing the best process variants with optimal resource allocation. Building upon our previous work, we propose in this paper, (i) a unified description model that allows the representation of process activities along with their possible cloud resource allocation alternatives, (ii) a methodology for simulating a configurable process along with its cloud resource consumption, and (iii) an extension of CloudSim framework in order to realize the simulation of the configurable resource allocation for the cloud-based business processes.

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Section: A Results Analysismentioning

confidence: 99%

Simulation of Configurable Resource Allocation for Cloud-Based Business Processes

Ahmed-Nacer

Suri

Sellami

et al. 2017

2017 IEEE International Conference on Services Computing (SCC)

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“…Ontologies are used for various purposes such as natural language processing (Staab et al, 1999), knowledge management (Davies et al, 2006), information extraction (Wiederhold, 1992), intelligent search engines (Heflin and Hendler, 2000), digital libraries (Kesseler, 1996), business process modeling (Brambilla et al, 2006;Ciuksys and Caplinskas, 2007;Magdalenic et al, 2009) etc. While the use of ontologies was primarily in the domain of academia, situation now improves with the advent of several methodologies for ontology manipulation.…”

Section: Review Of Related Approachesmentioning

confidence: 99%

Facilitating Ontology Development with Continuous Evaluation

Lavbič¹,

Krisper²

2010

Informatica

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In this paper we propose facilitating ontology development by constant evaluation of steps in the process of ontology development. Existing methodologies for ontology development are complex and they require technical knowledge that business users and developers don't poses. By introducing ontology completeness indicator developer is guided throughout the development process and constantly aided by recommendations to progress to next step and improve the quality of ontology. In evaluating the ontology, several aspects are considered; from description, partition, consistency, redundancy and to anomaly. The applicability of the approach was demonstrated on Financial Instruments and Trading Strategies (FITS) ontology with comparison to other approaches.

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“…Layer 1: Sensors (including preliminary data processing and transmission) (6) ontology-based approaches (e.g., [4]); (7) multicriteria multiple choice problem (e.g., [23]); (8) hierarchical multicriteria morphological design (HMMD) approach ( [7], [8], [9]); (9) AI techniques (e.g., [14], [15], [25], [26]); and (10) design grammars approaches (e.g., multidisciplinary grammar approach that includes production rules and optimization, graph grammar approach) (e.g., [20]). A survey of combinatorial optimization approaches to system configuration design is presented in [9].…”

Section: Introductionmentioning

confidence: 99%

Design of modular wireless sensor

Levin,

Fimin

2012

Preprint

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The paper addresses combinatorial approach to design of modular wireless sensor as composition of the sensor element from its component alternatives and aggregation of the obtained solutions into a resultant aggregated solution. A hierarchical model is used for the wireless sensor element. The solving process consists of three stages: (i) multicriteria ranking of design alternatives for system components/parts, (ii) composing the selected design alternatives into composite solution(s) while taking into account ordinal quality of the design alternatives above and their compatibility (this stage is based on Hierarchical Morphological Multicriteria Design -HMMD), and (iii) aggregation of the obtained composite solutions into a resultant aggregated solution(s). A numerical example describes the problem structuring and solving processes for modular alarm wireless sensor element.

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Reusing Ontological Knowledge about Business Processes in IS Engineering: Process Configuration Problem

Cited by 11 publications

References 21 publications

Simulation of Configurable Resource Allocation for Cloud-Based Business Processes

Simulation of Configurable Resource Allocation for Cloud-Based Business Processes

Facilitating Ontology Development with Continuous Evaluation

Design of modular wireless sensor

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