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

Non-Intrusive Context Aware Transactional Framework to Derive Business Insights on Big Data
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摘要 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. 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.
出处 《Journal of Signal and Information Processing》 2015年第2期73-78,共6页 信号与信息处理(英文)
关键词 CONTEXT Aware PATTERN Recognizer BIG DATA Context Aware Pattern Recognizer Big Data
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