Analysing Transportation Data with Open Source Big Data Analytic Tools

By Y. Beeharry, T. Fowdur, V. Hurbungs, V. Bassoo, V. Ramnarain-Seetohul. 2017

5 citations recorded by the source corpus.

Big data analytics allows a vast amount of structured and unstructured data to be effectively processed so that correlations, hidden patterns, and other useful information can be mined from the data. Several open source big data analytic tools that can perform tasks such as dimensionality reduction, feature extraction, transformation, optimization, are now available. One interesting area where such tools can provide effective solutions is transportation. Big data analytics can be used to efficiently manage transport infrastructure assets such as roads, airports, bus stations or ports. In this paper an overview of two open source big data analytic tools is first provided followed by a simple demonstration of application of these tools on transport dataset.

Subfields: Building and Construction, Transportation, Signal Processing

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7. J. Hossen

Citations in the corpus

• A Survey on Cleaning Dirty Data Using Machine Learning Paradigm for Big Data Analytics (2018, methodology): “There are some free tools been work on data transformation [21] [22], such as, OpenRefine, plyr, and reshape2, although it is uncertain whether they can execute Big Data.”