A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Data Analysis – Basic Overview
Data can be grouped into the following: continuous, ordinal, and nominal. Most of the data in railway track engineering are contained in the form of relational database management and are either in the form of structured query language (SQL) or non‐SQL management systems. There are a few examples of this, including track data management system (TDMS), which is the collection of geometry data that can be used, among other things, to perform basic statistical analysis and visualization of the defects. Exploratory data analysis (EDA) looks at the data in different ways to detect or explore interesting features, including anomalies in the data. Symbolic data analysis (SDA) provides a framework for the representation and analysis of railway track data that comprehends inherent variability. There are two different types of SDA: temporal aggregation and contemporary aggregation. There are a few advances in methods, which include multiple imputation, maximum likelihood methods, and Bayesian simulation methods for big data.
Data Analysis – Basic Overview
Data can be grouped into the following: continuous, ordinal, and nominal. Most of the data in railway track engineering are contained in the form of relational database management and are either in the form of structured query language (SQL) or non‐SQL management systems. There are a few examples of this, including track data management system (TDMS), which is the collection of geometry data that can be used, among other things, to perform basic statistical analysis and visualization of the defects. Exploratory data analysis (EDA) looks at the data in different ways to detect or explore interesting features, including anomalies in the data. Symbolic data analysis (SDA) provides a framework for the representation and analysis of railway track data that comprehends inherent variability. There are two different types of SDA: temporal aggregation and contemporary aggregation. There are a few advances in methods, which include multiple imputation, maximum likelihood methods, and Bayesian simulation methods for big data.
Data Analysis – Basic Overview
Attoh‐Okine, Nii O. (author)
2017-06-26
10 pages
Article/Chapter (Book)
Electronic Resource
English
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