Non-Intrusive Context Aware Transactional Framework to Derive Business Insights on Big Data


To convert invisible, unstructured and time-sensitive machine data into information for decision making is a challenge. Tools available today handle only structured data. All the transaction data are getting captured without understanding its future relevance and usage. It leads to other big data analytics related issue in storing, archiving, processing, not bringing in relevant business insights to the business user. In this paper, we are proposing a context aware pattern methodology to filter relevant transaction data based on the preference of business.

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Chidambaram, S. , Rubini, P. and Sellam, V. (2015) Non-Intrusive Context Aware Transactional Framework to Derive Business Insights on Big Data. Journal of Signal and Information Processing, 6, 73-78. doi: 10.4236/jsip.2015.62007.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Duggan, J. and Stonebraker, M. (2014) Incremental Elasticity for Array Databases. ACM SIGMOD-/PODS (SIGMOD 2014).
[2] Kalinin, A., Cetintemel, U. and Zdonik, S. (2014) Interactive Data Exploration Using Semantic Windows ACM. SIGMOD/PODS (SIGMOD 2014).
[3] Vojnovic, M., Xu, F. and Zhou, J.R. (2012) Sampling Based Range Partition Methods for Big Data Analytics. No. MSR-TR-2012-18.
[4] Sundaram, N., Turmukhametova, A., Satish, N., Mostak, T., Indyk, P., Madden, S. and Dubey, P. (2014) Streaming Similarity Search over One Billion Tweets Using Parallel Locality Sensitive Hashing. Annual Conference on Very Large Data Bases 2014 (VLDB 2014).
[5] Jun, S.-W., Liu, M. and Kermin Fleming, A. (2014) Scalable Multi-Access Flash Store for Big Data Analytics. 22nd ACM/SIGDA International Symposium on Field-Programmable Gate Arrays.

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