Toward Compact Data from Big Data

TL;DR

This paper discusses the concept of compact data as an optimized, distilled form of big data that retains essential knowledge patterns for efficient, personalized analysis without handling large datasets.

Contribution

It introduces the idea of compact data as a tailored approach to optimize big data for specific problem situations, with various techniques demonstrated across research areas.

Findings

Compact data retains maximum knowledge patterns at a fine-grained level.

Various compact data techniques are applicable across multiple data-driven fields.

Compact data enables effective utilization of big data without handling its complexity.

Abstract

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.