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Rehman, M.H., Liew, C.S., and Wah, T.Y.: ‘Frequent pattern mining in mobile devices: A feasibility study’, in Editor (Ed.)^(Eds.): ‘Book Frequent pattern mining in mobile devices: A feasibility study’ (IEEE, 2014, edn.), pp. 351-356.

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Article

Pattern-based Data Sharing in Big Data Environments

1Faculty of computer science and information technology, University of Malaya, Kuala Lumpur, Malaysia

2Department of Computer Science, Iqra University, Islamabad, Pakistan


Digital Technologies. 2015, Vol. 1 No. 1, 39-42
DOI: 10.12691/dt-1-1-8
Copyright © 2015 Science and Education Publishing

Cite this paper:
Muhammad Habib ur Rehman, Aisha Batool. Pattern-based Data Sharing in Big Data Environments. Digital Technologies. 2015; 1(1):39-42. doi: 10.12691/dt-1-1-8.

Correspondence to: Muhammad  Habib ur Rehman, Faculty of computer science and information technology, University of Malaya, Kuala Lumpur, Malaysia. Email: habibcomsats@gmail.com

Abstract

The staggering growth in Internet of Things (IoTs) technologies is the key driver for generation of massive raw data streams in big data environments. In addition, the collection of raw data streams in big data systems increases computational complexity and resource consumption in cloud-enabled data mining systems. In this paper, we are introducing the concept of pattern-based data sharing in big data environments. The proposed methodology enables local data processing near the data sources and transforms the raw data streams into actionable knowledge patterns. These knowledge patterns have dual utility of availability of local knowledge patterns for immediate actions as well as for participatory data sharing in big data environments. The proposed concept has the wide potential to be applied in numerous application areas.

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