Title :
Metaheuristics in big data: An approach to railway engineering
Author :
Nunez, Silvia Galvan ; Attoh-Okine, Nii
Author_Institution :
Dept. of Civil & Environ. Eng., Univ. of Delaware, Newark, DE, USA
Abstract :
Big data is becoming increasingly important in various fields; railway engineering is no exception. The use of advanced analysis tools will lead to improved reliability and safety in railway systems. This paper addresses how metaheuristics can be used as an optimization technique to accurately analyze large data in railway engineering. Contributions in both optimization and application in railway engineering are also mentioned. Also, future research towards data analysis in real-life problems is discussed.
Keywords :
Big Data; data analysis; optimisation; railway engineering; railway safety; Big Data; data analysis tools; metaheuristics; optimization technique; railway engineering; railway reliability; railway safety; Algorithm design and analysis; Big data; Conferences; Data analysis; Genetic algorithms; Optimization; Railway engineering; Big Data; Metaheuristics; Railway;
Conference_Titel :
Big Data (Big Data), 2014 IEEE International Conference on
Conference_Location :
Washington, DC
DOI :
10.1109/BigData.2014.7004430
