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, 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 pipeline. The task to identify critical steps turns out to be rather 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.
The research reported in this paper has been supported by the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry for Digital and Economic Affairs, and the Province of Upper Austria in the frame of the COMET center SCCH.
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References
Al-Naymat, G.H.: New methods for mining sequential and time series data. Ph.D. thesis, University of Sydney (2009). https://doi.org/10.1.1.877.2611, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.877.2611&rep=rep1&type=pdf
Carbin, M., Rinard, M.: Automatically identifying critical input regions and code in applications. In: Proceedings of the 19th International Symposium on Software Testing and Analysis (ISSTA 2010), pp. 37–48 (2010)
Chen, A., Wu, Y., Haeberlen, A., Zhou, W., Loo, B.T.: The good, the bad, and the differences: better network diagnostics with differential provenance. In: Proceedings of SIGCOMM 2016, pp. 115–128 (2016). https://doi.org/10.1145/2934872.2934910
Fernando, T.: WorkflowDSL: scalable workflow execution with provenance. Master thesis, KTH Royal Institute of Technology, School of Information and Communication Technology, Stockholm, Sweden (2017). http://www.diva-portal.org/smash/get/diva2:1149093/FULLTEXT01.pdf
Fu, T.C.: A review on time series data mining. Eng. Appl. Artif. Intell. 24, 164–181 (2011). https://doi.org/10.1016/j.engappai.2010.09.007
Gulzar, M.A., Interlandi, M., Han, X., Li, M., Condie, T., Kim, M.: Automated debugging in data-intensive scalable computing. In: Proceedings of SoCC 2017, pp. 520–534 (2017). https://doi.org/10.1145/3127479.3131624
Kagermann, H., Wahlster, W., Helbig, J.: Recommendations for implementing the strategic initiative INDUSTRIE 4.0, April 2013. https://www.din.de/blob/76902/e8cac883f42bf28536e7e8165993f1fd/recommendations-for-implementing-industry-4-0-data.pdf
Polyzotis, N., Roy, S., Whang, S.E., Zinkevich, M.: Data lifecycle challenges in production machine learning: a survey. SIGMOD Rec. 47(2), 17–28 (2018)
Wang, X., Dong, X.L., Meliou, A.: Data X-RAy: a diagnostic tool for data errors. In: Proceedings of SIGMOD 2015, pp. 1231–1245 (2015). https://doi.org/10.1145/2723372.2750549
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, 2nd edn. Morgan Kaufmann, Burlington (2005)
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Kossak, F., Zwick, M. (2019). ML-PipeDebugger: A Debugging Tool for Data Processing Pipelines. In: Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2019. Lecture Notes in Computer Science(), vol 11707. Springer, Cham. https://doi.org/10.1007/978-3-030-27618-8_20
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DOI: https://doi.org/10.1007/978-3-030-27618-8_20
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