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Digital Library

of the European Council for Modelling and Simulation

 

Title:

Data Stream Harmonization For Heterogeneous Workflows

Authors:

Eleftherios Bandis, Nikolaos Polatidis, Maria Diapouli, Stelios Kapetanakis

Published in:

 

 

(2021). ECMS 2021, 35th Proceedings
Edited by: Khalid Al-Begain, Mauro Iacono, Lelio Campanile, Andrzej Bargiela, European Council for Modelling and Simulation.

 

DOI: http://doi.org/10.7148/2021

ISSN: 2522-2422 (ONLINE)

ISSN: 2522-2414 (PRINT)

ISSN: 2522-2430 (CD-ROM)

 

ISBN: 978-3-937436-72-2
ISBN: 978-3-937436-73-9(CD)

 

Communications of the ECMS , Volume 35, Issue 1, June 2021,

United Kingdom

 

Citation format:

Eleftherios Bandis, Nikolaos Polatidis, Maria Diapouli, Stelios Kapetanakis (2021). Data Stream Harmonization For Heterogeneous Workflows, ECMS 2021 Proceedings Edited By: Khalid Al-Begain, Mauro Iacono, Lelio Campanile, Andrzej Bargiela European Council for Modeling and Simulation. doi: 10.7148/2021-0042

DOI:

https://doi.org/10.7148/2021-0042

Abstract:

Transport infrastructure relies heavily on extended multi sensor networks and data streams to support its advanced real time monitoring and decision making. All relevant stakeholders are highly concerned on how travel patterns, infrastructure capacity and other internal / external factors (such as weather) affect, deteriorate or improve performance. Usually new network infrastructure can be remarkably expensive to build thus the focus is constantly in improving existing workflows, reduce overheads and enforce lean processes. We propose suitable graph-based workflow monitoring met­hods for developing efficient performance measures for the rail industry using extensive business process workflow pattern analysis based on Case-based Reasoning (CBR) combined with standard Data Mining methods. The approach focuses on both data preparation, cleaning and workflow integration of real network data. Preliminary results of this work are promising since workflow integration seems efficient against data complexity and domain peculiarities as well as scale on demand whilst demonstrating efficient accuracy. A number of modelling experiments are presented, that show that the approach proposed here can provide a sound basis for the effective and useful analysis of operational sensor data from train Journeys.

 

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