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.
Highlights
There are varieties of diversified portfolio of applications getting deployed in the Enterprise Infrastructure space, and each application has a different trend of arrival patterns which generates machine data that need to be captured and processed to gain business insights
Context aware filtering is the process of recognizing the machine data based on the pattern
The generation of machine data is in multiple phases, so the pattern recognizer will be a logical independent component which can be made as non-intrusive deployment whenever any transaction happens
Summary
There are varieties of diversified portfolio of applications getting deployed in the Enterprise Infrastructure space, and each application has a different trend of arrival patterns which generates machine data that need to be captured and processed to gain business insights. The problem starts from collection and filtering and processing of the data becomes difficult due to the rate in which the data getting generated is huge [1] This requires an efficient way to interpret and bring relevance to the particular context the business deals with. The preferred way to look at this issue is to bring in relevance when the data are getting generated real time so that only the relevance and needed machine data are getting captured by leaving the unwanted machine data The purpose of this Context Aware Transactional framework is to categorize the patterns and filter the machine data based on the relevance of each transaction getting performed. The objective of this paper is to propose a methodological approach to implement a non-intrusive component which can be plugged into the existing enterprise infrastructure layer to bring out all the insights business wants by capturing only the relevant business oriented machine data [3].
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