Bibliography | Fritz, Manuel; Shao, Gang; Schwarz, Holger: Automatic Selection of Analytic Platforms with ASAP-DM. In: Proceedings of the 33rd International Conference on Scientific and Statistical Database Management. University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology. pp. 220-225, english. ACM, July 2021. ISBN: 9781450384131; DOI: 10.1145/3468791.3468802. Article in Proceedings (Conference Paper).
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CR-Schema | H.2.8 (Database Applications)
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Abstract | The plethora of available analytic platforms escalates the difficulty of selecting the most appropriate platform for a certain data mining task and datasets with varying characteristics. Especially novice analysts experience difficulties to keep up with the latest technical developments. In this demo, we present the ASAP-DM framework. ASAP-DM is able to automatically select a well-performing analytic platform for a given data mining task via an intuitive web interface, thus especially supporting novice analysts. The take-aways for demo attendees are: (1) a good understanding of the challenges of various data mining workloads, dataset characteristics, and the effects on the selection of analytic platforms, (2) useful insights on how ASAP-DM internally works, and (3) how to benefit from ASAP-DM for exploratory data analysis.
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Department(s) | University of Stuttgart, Institute of Parallel and Distributed Systems, Applications of Parallel and Distributed Systems
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Project(s) | INTERACT
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Entry date | August 12, 2021 |
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