Pattern-based Data Sharing in Big Data Environments

Muhammad Habib ur Rehman^1, and Aisha Batool²

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

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

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

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.

Keywords:
big data edge computing cloud computing internet of things

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Gubbi, J., Buyya, R., Marusic, S., and Palaniswami, M.: ‘Internet of Things (IoT): A vision, architectural elements, and future directions’, Future Generation Computer Systems, 2013, 29, (7), pp. 1645-1660.
[2]	Rehman, M.H., Liew, C.S., Wah, T.Y., Shuja, J., and Daghighi, B.: ‘Mining Personal Data Using Smartphones and Wearable Devices: A Survey’, Sensors, 2015, 15, (2), pp. 4430-4469.
[3]	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.
[4]	Haghighi, P.D., Krishnaswamy, S., Zaslavsky, A., Gaber, M.M., Sinha, A., and Gillick, B.: ‘Open mobile miner: a toolkit for building situation-aware data mining applications’, Journal of Organizational Computing and Electronic Commerce, 2013, 23, (3), pp. 224-248.
[5]	Gaber, M.M., Gomes, J.B., and Stahl, F.: ‘Pocket data mining’ (Springer, 2014. 2014).
[6]	Abdallah, Z.S., Gaber, M.M., Srinivasan, B., and Krishnaswamy, S.: ‘StreamAR: incremental and active learning with evolving sensory data for activity recognition’, in Editor (Ed.)^(Eds.): ‘Book StreamAR: incremental and active learning with evolving sensory data for activity recognition’ (IEEE, 2012, edn.), pp. 1163-1170.
[7]	Abdallah, Z.S., Gaber, M.M., Srinivasan, B., and Krishnaswamy, S.: ‘Adaptive mobile activity recognition system with evolving data streams’, Neurocomputing, 2015, 150, pp. 304-317.
[8]	Liu, P., Chen, Y., Tang, W., and Yue, Q.: ‘Mobile weka as data mining tool on android’: ‘Advances in Electrical Engineering and Automation’ (Springer, 2012), pp. 75-80.
[9]	Bonomi, F., Milito, R., Natarajan, P., and Zhu, J.: ‘Fog Computing: A Platform for Internet of Things and Analytics’: ‘Big Data and Internet of Things: A Roadmap for Smart Environments’ (Springer, 2014), pp. 169-186.
[10]	Jayaraman, P.P., Gomes, J.B., Nguyen, H.L., Abdallah, Z.S., Krishnaswamy, S., and Zaslavsky, A.: ‘CARDAP: A Scalable Energy-Efficient Context Aware Distributed Mobile Data Analytics Platform for the Fog’, in Editor (Ed.)^(Eds.): ‘Book CARDAP: A Scalable Energy-Efficient Context Aware Distributed Mobile Data Analytics Platform for the Fog’ (Springer, 2014, edn.), pp. 192-206.
[11]	Rehman, M.H., Liew, C.S., and Wah, T.Y.: ‘UniMiner: Towards a unified framework for data mining’, in Editor (Ed.)^(Eds.): ‘Book UniMiner: Towards a unified framework for data mining’ (IEEE, 2014, edn.), pp. 134-139.
[12]	Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A., and Khan, S.U.: ‘The rise of “big data” on cloud computing: Review and open research issues’, Information Systems, 2015, 47, pp. 98-115.
[13]	Gani, A., Siddiqa, A., Shamshirband, S., and Hanum, F.: ‘A survey on indexing techniques for big data: taxonomy and performance evaluation’, Knowledge and Information Systems, 2015, pp. 1-44.