Review of data analysis framework for variety of big data
Emerging Trends in Expert Applications and Security: Proceedings of ICETEAS 2018, 2019•Springer
Big Data is too large to be handled by traditional methods for analysis. It is a new ubiquitous
term, which describes huge amount of data. Dealing with “Variety”, one of the five
characteristics of Big Data is a great challenge. Variety means a range of formats such as
structured tables, semi-structured log files, and unstructured text, audio, and video data.
Every format of data has its unique framework for analyzing it. In this paper, we present a
detailed study about various frameworks for analyzing structured, semi-structured, and …
term, which describes huge amount of data. Dealing with “Variety”, one of the five
characteristics of Big Data is a great challenge. Variety means a range of formats such as
structured tables, semi-structured log files, and unstructured text, audio, and video data.
Every format of data has its unique framework for analyzing it. In this paper, we present a
detailed study about various frameworks for analyzing structured, semi-structured, and …
Abstract
Big Data is too large to be handled by traditional methods for analysis. It is a new ubiquitous term, which describes huge amount of data. Dealing with “Variety”, one of the five characteristics of Big Data is a great challenge. Variety means a range of formats such as structured tables, semi-structured log files, and unstructured text, audio, and video data. Every format of data has its unique framework for analyzing it. In this paper, we present a detailed study about various frameworks for analyzing structured, semi-structured, and unstructured data individually. In addition, some frameworks, which deal with all the three formats together, are also explained.
Springer
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