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.