Developing effective visual analytics systems demands care in characterization of domain
problems and integration of visualization techniques and computational models. Urban …

With the ever-increasing amount of data, the world has stepped into the era of “Big Data”.
Presently, the analysis of massive and complex data and the extraction of relevant …

Recent advances in technology have enabled social media services to support space-time
indexed data, and internet users from all over the world have created a large volume of time …

Traditional association rule mining algorithms only generate a large number of highly
frequent rules, but these rules do not provide useful answers for what the high utility rules …

Predictive hotspot mapping plays a critical role in hotspot policing. Existing methods such as
the popular kernel density estimation (KDE) do not consider the temporal dimension of …

Visual analytics employs interactive visualizations to integrate users' knowledge and
inference capability into numerical/algorithmic data analysis processes. It is an active …

It has been widely suggested that a key goal of visualization systems is to assist decision
making, but is this true? We conduct a critical investigation on whether the activity of …

Cities are inherently dynamic. Interesting patterns of behavior typically manifest at several
key areas of a city over multiple temporal resolutions. Studying these patterns can greatly …

This paper introduces HadoopViz; a MapReduce-based framework for visualizing big spatial
data. HadoopViz has three unique features that distinguish it from other techniques.(1) It …

We present a method to interactively explore multiple attributes in trajectory data using
density maps, ie, images that show an aggregate overview of massive amounts of data. So …