Will Skynet Need a Librarian? A Literature Review of Machine Learning–Based Data Organization and Use Strategies.

9 Pages Posted: 22 May 2018 Last revised: 23 Jul 2018

Date Written: May 7, 2018

Abstract

The increasing use of machine learning in everyday life, from loan applications to morning coffee, has developed alongside the rocketing growth of data professionally, attempting to leave no stone unturned in the quest for new insights, and personally, as digitally-connected people both produce and consume more data than ever before. Learning how machine learning models and algorithms engage with data organization is important in understanding how to improve the utility and benefits of current and future machine learning uses. In surveying the literature landscape, we aim to look at the intersection of machine learning and data management from a few different fields for a comprehensive, albeit brief, view of this fascinating area of modern science and technology. In this literature review, databases, both relational and nonrelational, are covered, along with four types of machine learning: symbolic machine learning, neural networks, simulated evolution and genetic algorithms, and particle swarm optimization. We’ll also take a look at machine learning-focused information retrieval strategies and how selected fields utilize and advance machine learning.

Keywords: Machine Learning, Algorithms, Data Management, Artificial Intelligence, Database, Neural Networks, Symbolic Learning

Suggested Citation

Coles, Andrew, Will Skynet Need a Librarian? A Literature Review of Machine Learning–Based Data Organization and Use Strategies. (May 7, 2018). Available at SSRN: https://ssrn.com/abstract=3174904 or http://dx.doi.org/10.2139/ssrn.3174904

Andrew Coles (Contact Author)

University of Texas at Austin ( email )

2317 Speedway
Austin, TX 78712
United States

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