Ontology tailoring in the Semantic Grid

Flahive, Andrew; Taniar, David; Rahayu, Wenny; Apduhan, Bernady O.

doi:10.1016/j.csi.2008.03.016

Cited by 34 publications

(18 citation statements)

References 10 publications

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“…The support for Cloud platforms such as OpenStack and EUCALYPTUS (Nurmi et al, 2009) also needs to be provided, along with support for application hosting, job distribution and concurrency. The infrastructure interoperability across disparate platforms also needs a solution for semantic interfaces in Clouds and Grids (Flahive et al, 2009;Flahive et al, 2011) and this is one of the services that we need to integrate in the Glueing Platform in future.…”

Section: Discussionmentioning

confidence: 99%

Glueing grids and clouds together: a service-oriented approach

Anjum

Hill

McClatchey

et al. 2012

IJWGS

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Section: Discussionmentioning

confidence: 99%

Glueing grids and clouds together: a service-oriented approach

Anjum

Hill

McClatchey

et al. 2012

IJWGS

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“…Previous work on sub-ontology extraction and optimization are described in [2], [3], [4]. Likewise, [5] introduced Ontology-Based Feature Modeling and Application-Oriented Tailoring which feature models and architecture which have been widely adopted in capturing and specifying domain requirements.…”

Section: Related Workmentioning

confidence: 99%

On a Prototype Environment for Sub-ontology Extraction in Semantic Grid

Uchibayashi

Apduhan

Shiratori³

et al. 2010

2010 International Conference on Computational Science and Its Applications

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To efficiently utilize the large-scale ontology data distributed on the Semantic Grid is a problem issue of high concern. Sub-ontology extraction that reflects the user requirements is one method of addressing the issue. Largescale ontology data can be optimized by extracting the required sub-ontology. In this paper, we construct a Semantic Grid prototype environment to extract the sub-ontology and conducted preliminary evaluation through illustrations using UMLSSN ontology data.

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“…Grid computing has emerged as a wide area distributed paradigm for solving large-scale problems in science, engineering, etc., known as the family of eScience Grid-enabled applications (Costantini et al, 2010), as well for advanced services (Flahive et al, 2009). A computational grid involves the combination of many computing resources into a network for the execution of computational tasks.…”

Section: Introductionmentioning

confidence: 99%

Hybrid algorithms for independent batch scheduling in grids

Xhafa

Kołodziej

Barolli

et al. 2012

IJWGS

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Grid computing has emerged as a wide area distributed paradigm for solving large-scale problems in science, engineering, etc. and is known as the family of eScience grid-enabled applications. Computing planning of incoming jobs effi ciently with available machines in the grid system is the main requirement for optimised system performance. One version of the problem is that of Hybrid algorithms for independent batch scheduling in grids 135 independent batch scheduling, in which jobs are assumed to be independent and are scheduled in batches aimed at minimising the makespan and fl owtime. Given the hardness of the problem, heuristics are used to fi nd high quality solutions for practical purposes of designing effi cient grid schedulers. Recently, considerable efforts were spent in implementing and evaluating not only stand-alone heuristics and meta-heuristics, but also their hybridisation into even higher level algorithms. In this paper, we present a study on the performance of two popular algorithms for the problem, namely Genetic Algorithms (GAs) and Tabu Search (TS) and two hybridisations involving them, namely, the GA (TS) and GA-TS, which differ in the way the main control and cooperation among GA and TS are implemented. The hierarchic and simultaneous optimisation modes are considered for the bi-objective scheduling problem. Evaluation is done using different grid scenarios generated by a grid simulator. The computational results showed that the hybrid algorithm outperforms both the GA and TS for the makespan parameter, but not for the fl owtime parameter.

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Ontology tailoring in the Semantic Grid

Cited by 34 publications

References 10 publications

Glueing grids and clouds together: a service-oriented approach

Glueing grids and clouds together: a service-oriented approach

On a Prototype Environment for Sub-ontology Extraction in Semantic Grid

Hybrid algorithms for independent batch scheduling in grids

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