Browse > Article
http://dx.doi.org/10.7782/JKSR.2017.20.5.703

A Study on Data Mapping for Integrated Analysis of Railway Safety Data  

Byun, Hyun-Jin (Safety Innovation Headquarters, Korea Railroad Corp.)
Lee, Yong-Sang (Department of Railroad Management, Woosong University)
Publication Information
Journal of the Korean Society for Railway / v.20, no.5, 2017 , pp. 703-712 More about this Journal
Abstract
The railway system is an interface industry that can be safely operated by organically operating the lines, vehicles, controls, etc. Various data are generated in the operation and maintenance activities of the railway system. These data are utilized in cooperation with safety and maintenance activities in each field, but amount of data is insufficient for data analysis of safety management due to relevant data being produced without any synchronous criteria such as time or space. In particular, reference data such as location and time of failure data for each field are set to different criteria according to the work characteristics in each field. So, it is not easy to analyze data integrally based on location and time. Therefore, mapping of reference data can be required for integrated analysis of data defined in different formats. By selecting data mapping tools and verifying the results of safety relevant data with the same criteria, the purpose of this paper is to enable integrated analysis of railway safety management data occurring in different fields based on location and time.
Keywords
Railway; Risk management; Data mapping; Location information; Risk factor;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 N. Attoh-Okine (2014) Big data challenges in railway engineering, IEEE International Coference on Big Data, Washington DC, pp. 7-9.
2 H. Devid, K. Jason (2013) Big data and railroad analytics, Newsletter of the Railway Applications Section, p. 12.
3 Y. Ko, T. Seo (2005) A Study on the naming Rules of metadata based on ontology, Journal of the Korean Society for Information Management, 22(4), pp. 97-109.   DOI
4 T. Seo, D. T. Pham.(2007) Data modeling process to ensure semantic interoperability of data, Information Management Research, 37(1), pp. 59-73.
5 H. Kim, Y. Jeong, H. Kang, D. Park (2016) Data mapping and development guideline for PHR system establishment using CMD, Journal of Korea Institute of Information Technology, pp. 133-141.
6 C. Pluempitiwiriyawej, J. Hammer (2000), A classification scheme for semantic and schematic heterogeneities in XML data sources, Technical Report TR00-004, University of Florida, Gainesville, FL, pp. 36.
7 J. Han, M. Kamber (2011) Data Mining: Concepts and Techniques (Third Edition), Burlington, MA: Morgan Kaufmann.
8 U. Fayyad, G. Piatetsky-shapino, P. Smyth (1996) From data mining to knowledge discovery in databases, AI magazine, 17(3), pp. 37- 54.
9 S.D. Nelson (2016) Informatics and interoperability: Speaking the same language, ISPOR 20th Aunnual International Meeting.
10 Korea Data Agency (2016) Data Quality Management Guidelines ver 2.1.