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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).

has been cited by the following article:

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

  AUTHORS: Siva Chidambaram, P. E. Rubini, V. Sellam

  KEYWORDS: Context Aware, Pattern Recognizer, Big Data

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.6 No.2, April 2, 2015

  ABSTRACT: 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.