Toward Compact Data from Big Data
This paper addresses the problem of efficiently utilizing big data for personalized applications without the overhead of complex big data systems, which could benefit users and organizations dealing with large datasets.
This paper proposes 'compact data,' a method to optimize large datasets into smaller, more manageable forms while retaining maximum knowledge patterns. The goal is to enable effective and personalized utilization of big data systems without directly handling complex big data.
Bigdata is a dataset of which size is beyond the ability of handling a valuable raw material that can be refined and distilled into valuable specific insights. Compact data is a method that optimizes the big dataset that gives best assets without handling complex bigdata. The compact dataset contains the maximum knowledge patterns at fine grained level for effective and personalized utilization of bigdata systems without bigdata. The compact data method is a tailor-made design which depends on problem situations. Various compact data techniques have been demonstrated into various data-driven research area in the paper.