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Books > Professional & Technical > Transport technology > Railway technology & engineering
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Big Data and Differential Privacy - Analysis Strategies for Railway Track Engineering (Hardcover)
Loot Price: R3,409
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Big Data and Differential Privacy - Analysis Strategies for Railway Track Engineering (Hardcover)
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A comprehensive introduction to the theory and practice of
contemporary data science analysis for railway track engineering
Featuring a practical introduction to state-of-the-art data
analysis for railway track engineering, Big Data and Differential
Privacy: Analysis Strategies for Railway Track Engineering
addresses common issues with the implementation of big data
applications while exploring the limitations, advantages, and
disadvantages of more conventional methods. In addition, the book
provides a unifying approach to analyzing large volumes of data in
railway track engineering using an array of proven methods and
software technologies. Dr. Attoh-Okine considers some of today s
most notable applications and implementations and highlights when a
particular method or algorithm is most appropriate. Throughout, the
book presents numerous real-world examples to illustrate the latest
railway engineering big data applications of predictive analytics,
such as the Union Pacific Railroad s use of big data to reduce
train derailments, increase the velocity of shipments, and reduce
emissions. In addition to providing an overview of the latest
software tools used to analyze the large amount of data obtained by
railways, Big Data and Differential Privacy: Analysis Strategies
for Railway Track Engineering: Features a unified framework for
handling large volumes of data in railway track engineering using
predictive analytics, machine learning, and data mining Explores
issues of big data and differential privacy and discusses the
various advantages and disadvantages of more conventional data
analysis techniques Implements big data applications while
addressing common issues in railway track maintenance Explores the
advantages and pitfalls of data analysis software such as R and
Spark, as well as the Apache Hadoop(R) data collection database and
its popular implementation MapReduce Big Data and Differential
Privacy is a valuable resource for researchers and professionals in
transportation science, railway track engineering, design
engineering, operations research, and railway planning and
management. The book is also appropriate for graduate courses on
data analysis and data mining, transportation science, operations
research, and infrastructure management. NII ATTOH-OKINE, PhD, PE
is Professor in the Department of Civil and Environmental
Engineering at the University of Delaware. The author of over 70
journal articles, his main areas of research include big data and
data science; computational intelligence; graphical models and
belief functions; civil infrastructure systems; image and signal
processing; resilience engineering; and railway track analysis. Dr.
Attoh-Okine has edited five books in the areas of computational
intelligence, infrastructure systems and has served as an Associate
Editor of various ASCE and IEEE journals.
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