Machine Learning Meets Databases View Full Text


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Article Info

DATE

2017-03

AUTHORS

Stephan Günnemann

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. More... »

PAGES

77-83

References to SciGraph publications

  • 2000. PROMISE: Predicting Query Behavior to Enable Predictive Caching Strategies for OLAP Systems in DATA WAREHOUSING AND KNOWLEDGE DISCOVERY
  • 2008. Autonomic Databases: Detection of Workload Shifts with n-Gram-Models in ADVANCES IN DATABASES AND INFORMATION SYSTEMS
  • 2005. Oracle Data Mining in DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s13222-017-0247-8

    DOI

    http://dx.doi.org/10.1007/s13222-017-0247-8

    DIMENSIONS

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