ASAP-DM: a framework for automatic selection of analytic platforms for data mining

Fritz, Manuel; Muazzen, Osama; Behringer, Michael; Schwarz, Holger

doi:10.1007/s00450-019-00408-7

Cited by 2 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, extensions, e. g., for deep learning tasks, are also possible within ASAP-DM. Further details can be found in the ASAP-DM paper [5].…”

Section: Core Conceptsmentioning

confidence: 99%

“…Meta-feature set B contains a simpler collection of meta-features and was already used similarly in the ASAP-DM paper [5]. Here, we solely focus on the file size, the number of entities and the number of features in a dataset across all data mining tasks.…”

Section: Meta-featuresmentioning

confidence: 99%

“…In previous work, we presented major concepts of the ASAP-DM framework [5]. This framework enables analysts to use multiple APs through one generic API and performs an automatic selection of these APs based on data characteristics of previously analyzed datasets.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Automatic Selection of Analytic Platforms with ASAP-DM

Fritz

Shao

Schwarz

2021

33rd International Conference on Scientific and Statistical Database Management

Self Cite

View full text Add to dashboard Cite

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. CCS CONCEPTS• Information systems → Data mining.

show abstract

“…Therefore, extensions, e. g., for deep learning tasks, are also possible within ASAP-DM. Further details can be found in the ASAP-DM paper [5].…”

Section: Core Conceptsmentioning

confidence: 99%

Section: Meta-featuresmentioning

confidence: 99%