Study on 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. The complexity and cost factor involved in data management like storing, archiving, backup, recovery, etc. can be reduced by this framework.