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Sustainability 2018, 10(2), 441; https://doi.org/10.3390/su10020441

Ontology Design for Solving Computationally-Intensive Problems on Heterogeneous Architectures

1
Computer Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Abbassia 11566, Cairo, Egypt
2
Heinz-Nixdorf Endowed Chair for Distributed Information Systems, Friedrich-Schiller-Universitat Jena, 07743 Jena, Germany
3
Faculty of Computing and Information Technology, King Abdulaziz University, 21589 Jeddah, Saudi Arabia
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 13 November 2017 / Revised: 7 December 2017 / Accepted: 15 December 2017 / Published: 8 February 2018
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Abstract

Viewing a computationally-intensive problem as a self-contained challenge with its own hardware, software and scheduling strategies is an approach that should be investigated. We might suggest assigning heterogeneous hardware architectures to solve a problem, while parallel computing paradigms may play an important role in writing efficient code to solve the problem; moreover, the scheduling strategies may be examined as a possible solution. Depending on the problem complexity, finding the best possible solution using an integrated infrastructure of hardware, software and scheduling strategy can be a complex job. Developing and using ontologies and reasoning techniques play a significant role in reducing the complexity of identifying the components of such integrated infrastructures. Undertaking reasoning and inferencing regarding the domain concepts can help to find the best possible solution through a combination of hardware, software and scheduling strategies. In this paper, we present an ontology and show how we can use it to solve computationally-intensive problems from various domains. As a potential use for the idea, we present examples from the bioinformatics domain. Validation by using problems from the Elastic Optical Network domain has demonstrated the flexibility of the suggested ontology and its suitability for use with any other computationally-intensive problem domain. View Full-Text
Keywords: ontology design; knowledge management; heterogeneous architectures; Big Data ontology design; knowledge management; heterogeneous architectures; Big Data
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Faheem, H.M.; König-Ries, B.; Aslam, M.A.; Aljohani, N.R.; Katib, I. Ontology Design for Solving Computationally-Intensive Problems on Heterogeneous Architectures. Sustainability 2018, 10, 441.

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