PUBLICATION DATE

2017-01-31

AUTHORS

Stephan Günnemann

TITLE

Machine Learning Meets Databases

ISSUE

1

VOLUME

17

ISSN (print)

1618-2162

ISSN (electronic)

1610-1995

ABSTRACT

Machine Learning has become highly popular due to several success stories in data-driven applications. Prominent examples include object detection in images, speech recognition, and text translation. According to Gartner’s 2016 Hype Cycle for Emerging Technologies, Machine Learning is currently at its peak of inflated expectations, with several other application domains trying to exploit the use of Machine Learning technology. Since data-driven applications are a fundamental cornerstone of the database community as well, it becomes natural to ask how these fields relate to each other. In this article, we will therefore provide a brief introduction to the field of Machine Learning, we will discuss its interplay with other fields such as Data Mining and Databases, and we provide an overview of recent data management systems integrating Machine Learning functionality.

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32 TRIPLES      29 PREDICATES      32 URIs      19 LITERALS

Subject Predicate Object
1 articles:9eb3a72ccd74f9cc56cbd0fe69f61936 sg:abstract Abstract Machine Learning has become highly popular due to several success stories in data-driven applications. Prominent examples include object detection in images, speech recognition, and text translation. According to Gartner’s 2016 Hype Cycle for Emerging Technologies, Machine Learning is currently at its peak of inflated expectations, with several other application domains trying to exploit the use of Machine Learning technology. Since data-driven applications are a fundamental cornerstone of the database community as well, it becomes natural to ask how these fields relate to each other. In this article, we will therefore provide a brief introduction to the field of Machine Learning, we will discuss its interplay with other fields such as Data Mining and Databases, and we provide an overview of recent data management systems integrating Machine Learning functionality.
2 sg:articleType OriginalPaper
3 sg:coverYear 2017
4 sg:coverYearMonth 2017-03
5 sg:ddsId s13222-017-0247-8
6 sg:ddsIdJournalBrand 13222
7 sg:doi 10.1007/s13222-017-0247-8
8 sg:doiLink http://dx.doi.org/10.1007/s13222-017-0247-8
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16 sg:hasJournalBrand journal-brands:d5556bf03c99b9c2b95ffd69ee27aa0f
17 sg:issnElectronic 1610-1995
18 sg:issnPrint 1618-2162
19 sg:issue 1
20 sg:language English
21 sg:license http://scigraph.springernature.com/explorer/license/
22 sg:pageEnd 83
23 sg:pageStart 77
24 sg:publicationDate 2017-01-31
25 sg:publicationYear 2017
26 sg:publicationYearMonth 2017-01
27 sg:scigraphId 9eb3a72ccd74f9cc56cbd0fe69f61936
28 sg:title Machine Learning Meets Databases
29 sg:volume 17
30 sg:webpage https://link.springer.com/10.1007/s13222-017-0247-8
31 rdf:type sg:Article
32 rdfs:label Article: Machine Learning Meets Databases
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