ML-PipeDebugger: A debugging tool for data processing pipelines?

F. Kossak, M. Zwick. ML-PipeDebugger: A debugging tool for data processing pipelines?. volume 11707, pages 263-272, DOI 10.1007/978-3-030-27618-8_20, 8, 2019.

Autoren
  • Felix Kossak
  • Michael Zwick
Editoren
  • S. Hartmann
  • J. Küng
  • S. Chakravarthy
  • G. Anderst-Kotsis
  • o.Univ.Prof. Dipl.Ing. Dr. A Min Tjoa
  • I. Khalil Ibrahim
BuchDatabase and Expert Systems Applications - Proc. DEXA 2019, Part II
TypIn Konferenzband
VerlagSpringer
SerieLecture Notes of Computer Science
Band11707
DOI10.1007/978-3-030-27618-8_20
ISBN978-3-030-27617-1
Monat8
Jahr2019
Seiten263-272
Abstract

Data pre-processing for data analysis usually requires a considerable number of interdependent steps, many of which are liable to errors or to introduce unwanted biases. Such errors can lead to cases where predictions for similar data instances differ unexpectedly much. An important question is then to find out where in the data processing pipeline the deviation was caused. We present a tool that can help identify critical data processing steps in such cases, allowing to \debug" or improve data pre-processing and model generation. More generally, the tool gives a view of how different data instances behave in relation to each other throughout a data processing pipeline. The task to identify critical steps turns out to be surprisingly complex, mostly because features of different types and ranges have to be compared, because required statistical measures must be obtained from often small samples, and because time series can be involved.