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An Overview of Transfer Learning Focused on Asymmetric Heterogeneous Approaches Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Advanced Data Integration with Signifiers: Case Studies for Rail Automation

Authors : Alexander Wurl, Andreas Falkner, Alois Haselböck, Alexandra Mazak

Published in: Data Management Technologies and Applications

Publisher: Springer International Publishing

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Abstract

In Rail Automation, planning future projects requires the integration of business-critical data from heterogeneous, often noisy data sources. Current integration approaches often neglect uncertainties and inconsistencies in the integration process and thus cannot guarantee the necessary data quality. To tackle these issues, we propose a semi-automated process for data import, where the user resolves ambiguous data classifications. The task of finding the correct data warehouse entry for a source value in a proprietary, often semi-structured format is supported by the notion of a signifier which is a natural extension of composite primary keys. In three different case studies we show that this approach (i) facilitates high-quality data integration while minimizing user interaction, (ii) leverages approximate name matching of railway station and entity names, (iii) contributes to extract features from contextual data for data cross-checks and thus supports the planning phases of railway projects.

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Metadata
Title
Advanced Data Integration with Signifiers: Case Studies for Rail Automation
Authors
Alexander Wurl
Andreas Falkner
Alois Haselböck
Alexandra Mazak
Copyright Year
2018
Book
Data Management Technologies and Applications

Print ISBN: 978-3-319-94808-9

Electronic ISBN: 978-3-319-94809-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-94809-6_5

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