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A survey of intelligent assistants for data analysis


Serban, Floarea; Vanschoren, Joaquin; Kietz, Jörg-Uwe; Bernstein, Abraham (2013). A survey of intelligent assistants for data analysis. ACM Computing Surveys, 45(3):1-35.

Abstract

Research and industry increasingly make use of large amounts of data to guide decision-making. To do this, however, data needs to be analyzed in typically non-trivial refinement processes, which require technical expertise about methods and algorithms, experience with how a precise analysis should proceed, and knowledge about an exploding number of analytic approaches. To alleviate these problems, a plethora of different systems have been proposed that ``intelligently'' help users to analyze their data.This article provides a first survey to almost 30 years of research on Intelligent Discovery Assistants (IDAs). It explicates the types of help IDAs can provide to users and the kinds of (background) knowledge they leverage to provide this help. Furthermore, it provides an overview of the systems developed over the past years, identifies their most important features, and sketches an ``ideal'' future IDA as well as the challenges on the road ahead.

Abstract

Research and industry increasingly make use of large amounts of data to guide decision-making. To do this, however, data needs to be analyzed in typically non-trivial refinement processes, which require technical expertise about methods and algorithms, experience with how a precise analysis should proceed, and knowledge about an exploding number of analytic approaches. To alleviate these problems, a plethora of different systems have been proposed that ``intelligently'' help users to analyze their data.This article provides a first survey to almost 30 years of research on Intelligent Discovery Assistants (IDAs). It explicates the types of help IDAs can provide to users and the kinds of (background) knowledge they leverage to provide this help. Furthermore, it provides an overview of the systems developed over the past years, identifies their most important features, and sketches an ``ideal'' future IDA as well as the challenges on the road ahead.

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16 citations in Web of Science®
31 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Date:2013
Deposited On:19 Mar 2013 08:23
Last Modified:05 Apr 2016 16:28
Publisher:ACM
ISSN:0360-0300
Additional Information:© ACM, 2013. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.
Publisher DOI:https://doi.org/10.1145/2480741.2480748
Other Identification Number:merlin-id:6753

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