Advanced Data Integration with Signifiers: Case Studies for Rail Automation

Wurl, Alexander; Falkner, Andreas A.; Haselböck, Alois; Mazak, Alexandra

doi:10.1007/978-3-319-94809-6_5

Cited by 3 publications

(2 citation statements)

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“…The data of hardware components is collected from a set of rail automation projects and installed railway stations. Before analysis starts, the collected data are preprocessed to ensure a suitable data quality (Wurl et al, 2017), i.e., ambiguities caused by data integration are resolved. The data set retrieved is a data frame structured as follows: each row represents a project/station, and per project/station there exists quantitative information of hardware components and features represented by columns.…”

Section: Case Study Designmentioning

confidence: 99%

“…These data stem from heterogeneous data sources. Apart from data integration techniques, highlighted in (Wurl et al, 2017), selecting relevant features that return a suitable quantity estimation poses various challenges particularly in terms of outliers in data. In previous work (Wurl et al, 2018) we aim for finding proper regression models.…”

Section: Introductionmentioning

confidence: 99%

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Exploring Robustness in a Combined Feature Selection Approach

Wurl

Falkner

Haselböck

et al. 2019

Proceedings of the 8th International Conference on Data Science, Technology and Applications

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A crucial task in the bidding phase of industrial systems is a precise prediction of the number of hardware components of specific types for the proposal of a future project. Linear regression models, trained on data of past projects, are efficient in supporting such decisions. The number of features used by these regression models should be as small as possible, so that determining their quantities generates minimal effort. The fact that training data are often ambiguous, incomplete, and contain outlier makes challenging demands on the robustness of the feature selection methods used. We present a combined feature selection approach: (i) iteratively learn a robust well-fitted statistical model and rule out irrelevant features, (ii) perform redundancy analysis to rule out dispensable features. In a case study from the domain of hardware management in Rail Automation we show that this approach assures robustness in the calculation of hardware components.

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Section: Case Study Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%