Innovative Applications of Big Data in the Railway Industry

"This book explores how big data technologies can address the operational challenges in the railway industry. It assists academics working in the big data domain to gain an understanding of the problems faced by the rail industry, enabling them to start developing their existing tools and technologies to meet the needs of this dynamic new application domain"--

Shruti Kohli is working as a lead researcher in an area of Big Data Analytics, Birmingham Centre for Railway Research and Education. Her current research revolves around Big Data application and machine learning, working on IoT sensor data for mining health of rail assets. On Academics front, She holds Ph.D. in Technology and MPhil in Operational research, and has worked with Indias leading Universities, BIT Mesra Deemed University, IP University, UPT University for 12+ years doing research and teaching in Computer Sciences. With two Ph.D. near submission, she has built a strong research base. She has a keen interest in an area of Information retrieval, Operational Research, Data Mining, Web Analytics, Simulation, and Modelling. She had been working with open crowd source data and is totally fascinated by the variety of user behavior patterns over the net and have the keen interest in web analytics. She has many publications in national and international journals and has presented the paper at international conferences

Dr. A.V.Senthil Kumar obtained his BSc Degree (Physics) in 1987, P.G.Diploma in Computer Applications in 1988, MCA in 1991 from Bharathiar University. He obtained his Master of Philosophy in Computer Science from Bharathidasan University, Trichy during 2005 and his Ph.D in Computer Science from Vinayaka Missions University during 2009. To his credit he had industrial experience for five years as System Analyst in a Garment Export Company. Later he took up teaching and attached to CMS College of Science and Commerce, Coimbatore. He is an Editor-in-Chief for International Journal titled International Journal of Data Mining and Emerging Technologies, International Journal of Image Processing and Applications and International Journal of Advances in Knowledge Engineering & Computer Science. Key Member for India, Machine Intelligence Research Lab (MIR Labs). He is an Editorial Board Member and Reviewer for various International Journals. He is also a Committee member for various International Conferences. He is a Life member of International Association of Engineers (IAENG), Systems Society of India (SSI), member of The Indian Science Congress Association, member of Internet Society (ISOC), International Association of Computer Science and Information Technology (IACSIT), Indian Association for Research in Computing Science (IARCS), and committee member for various International Conferences. He has many awards from National and International Societies. Also a freelance writer for Tamil Computer (a fortnightly) and PC Friend (monthly).

John Easton is a Lecturer currently working in the Birmingham Centre for Railway Research and Education at the University of Birmingham. His current research interests centre on methods for the storage, processing and display of railway related datasets; in particular data representation and exchange via ontologies and parallel, semi-autonomous processing by software agents. Selected work in this area has included the TRIME third-rail monitoring system, which in 2012 was the joint winner of the Stephenson Award for Engineering Innovation at the National Rail Awards, and a range of EU-funded projects including INTERAIL, AUTOMAIN, OnTime, Capacity4Rail, and In2Rail. On a day-to-day basis, John is heavily involved in the Centres £1.65 million Strategic Partnership with Network Rail on the theme of data management and integration. He also sits on the IETs Rail Technical and Professional Network executive team, and on the NSAR-led Routes into Rail group. From July 2015 to March 2016 he worked with the network simulation team on the cross industry Digital Railway programme.