Manuel Fritz
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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Digital Library
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- Manuel Fritz, Michael Behringer, and Holger Schwarz. 2020. LOG-Means: Efficiently Estimating the Number of Clusters in Large Datasets. Proceedings of the VLDB Endowment 13, 11 (2020), 2118 – 2131.Google ScholarDigital Library
- Manuel Fritz, Osama Muazzen, Michael Behringer, and Holger Schwarz. 2019. ASAP-DM: a framework for automatic selection of analytic platforms for data mining. In Software-Intensive Cyber-Physical Systems. Springer Berlin Heidelberg.Google Scholar
- Manuel Fritz, Dennis Tschechlov, and Holger Schwarz. 2020. Learning from past observations: Meta-learning for efficient clustering analyses. In Lecture Notes in Computer Science, Vol. 12393 LNCS. 364–379.Google Scholar
- Manuel Fritz, Dennis Tschechlov, and Holger Schwarz. 2021. Efficient Exploratory Clustering Analyses with Qualitative Approximations. In International Conference on Extending Database Technology (EDBT).Google Scholar
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- Dennis Tschechlov, Manuel Fritz, and Holger Schwarz. 2021. AutoML4Clust: Efficient AutoML for Clustering Analyses. In International Conference on Extending Database Technology (EDBT).Google Scholar
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