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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Notes
Not every problem at hand needs to be tackled by Machine Learning. For example, the detection of people’s resumes on the Web via Machine Learning has not shown to be advantageous over the manual design of an algorithm to discover resumes [15]: “Since everyone who has looked at or written a resume has a pretty good idea of what resumes contain, there was no mystery about what makes a Web page a resume.”
Often these concepts are not well separated. The most prominent example is the k‑means clustering model, where the default algorithm to solve it (Lloyd’s algorithm) is itself often called k‑means.
Usually the whole process is called Knowledge Discovery, while phase 4 is referred to as Data Mining.
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Günnemann, S. Machine Learning Meets Databases. Datenbank Spektrum 17, 77–83 (2017). https://doi.org/10.1007/s13222-017-0247-8
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DOI: https://doi.org/10.1007/s13222-017-0247-8