Article in Proceedings INPROC-2021-04

BibliographyFritz, 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).
CR-SchemaH.2.8 (Database Applications)

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.

Department(s)University of Stuttgart, Institute of Parallel and Distributed Systems, Applications of Parallel and Distributed Systems
Entry dateAugust 12, 2021
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