Big Data Reduction Methods: A Survey

Research on big data analytics is entering in the new phase called fast data where multiple gigabytes of data arrive in the big data systems every second. Modern big data systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity in the acq...

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Vydáno v:Data science and engineering Ročník 1; číslo 4; s. 265 - 284
Hlavní autoři: ur Rehman, Muhammad Habib, Liew, Chee Sun, Abbas, Assad, Jayaraman, Prem Prakash, Wah, Teh Ying, Khan, Samee U.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2016
Springer Nature B.V
Témata:
Algorithm Analysis and Problem Complexity
Artificial Intelligence
Big Data
Chemistry and Earth Sciences
Computer Science
Data acquisition
Data compression
Data mining
Data Mining and Knowledge Discovery
Data reduction
Data systems
Data transmission
Database Management
Machine learning
Physics
Redundancy
Statistics for Engineering
Systems and Data Security
Dimensionality reduction
Data complexity
Big data
Data compression
Data reduction
ISSN:2364-1185, 2364-1541
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Shrnutí:Research on big data analytics is entering in the new phase called fast data where multiple gigabytes of data arrive in the big data systems every second. Modern big data systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity in the acquired data and consequently give rise to the 6Vs of big data. The reduced and relevant data streams are perceived to be more useful than collecting raw, redundant, inconsistent, and noisy data. Another perspective for big data reduction is that the million variables big datasets cause the curse of dimensionality which requires unbounded computational resources to uncover actionable knowledge patterns. This article presents a review of methods that are used for big data reduction. It also presents a detailed taxonomic discussion of big data reduction methods including the network theory, big data compression, dimension reduction, redundancy elimination, data mining, and machine learning methods. In addition, the open research issues pertinent to the big data reduction are also highlighted.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2364-1185
2364-1541
DOI:10.1007/s41019-016-0022-0