Improving Railway Maintenance Actions with Big Data and Distributed Ledger Technologies
Big Data Technologies (BDTs) and Distributed Ledger Technologies (DLTs) can bring disruptive innovation in the way we handle, store, and process data to gain knowledge. In this paper, we describe the architecture of a system that leverages on both these technologies to better manage maintenance actions in the railways context. On one side we employ a permissioned DLT to ensure the complete transparency and auditability of the process, the integrity and availability of the inserted data and, most of all, the non-repudiation of the actions performed by each participant in the maintenance management process. On the other side, exploiting the availability of the data in a single repository (the ledger) and with a standardised format, thanks to the utilisation of a DLT, we adopt BDTs to leverage on the features of each maintenance job, together with external factors, to estimate the maintenance restoration time.
KeywordsBig data analytics Distributed Ledger Technologies Railway maintenance actions
This research has been supported by the European Union through the projects IN2DREAMS (European Union’s Horizon 2020 research and innovation programme under grant agreement 777596).
- 1.Androulaki, E., Barger, A., Bortnikov, V., Cachin, C., et al.: Hyperledger fabric: a distributed operating system for permissioned blockchains. In: EuroSys (2018)Google Scholar
- 2.Benbunan-Fich, R., Castellanos, A.: Digitalization of land records: from paper to blockchain. In: International Conference on Information Systems (2018)Google Scholar
- 4.De Kruijff, J., Weigand, H.: Understanding the blokchain using enterprise ontology. In: International Conference on Advanced Information Systems Engineering (2017)Google Scholar
- 6.Fumeo, E., Oneto, L., Anguita, D.: Condition based maintenance in railway transportation systems based on big data streaming analysis. In: INNS International Conference on Big Data (2015)Google Scholar
- 9.Thaduri, A., Galar, D., Kumar, U.: Railway assets: a potential domain for big data analytics. In: INNS International Conference on Big Data (2015)Google Scholar