Go to DBLP homepageConceptual Modeling and Smart Computing for Big Transportation Data
Abstract: Technical advancements in recent decades have led to generation and collection of much more data at a rapid rate from a wide variety of rich data sources. The popularity of initiates of open data has also encouraged the sharing of these big data so that they have become publicly accessible. Examples of these big data include transportation data. Analyzing and mining these big transportation data help users (e.g., commuters, city planners) to take appropriate actions (e.g., making wise decisions), which in turn help building a smarter city. This leads to smart computing. Moreover, contents of available big transportation data may vary among cities, which lead to the conceptual modeling to describe- at a high level of abstraction-the semantics of data analytic and mining software applications on big transportation data. In this paper, we present conceptual modeling and smart computing for big transportation data. We illustrate our idea with real-life big transportation data from the Canadian city of Winnipeg and to show its practicality in real-life data.
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