To understand the evolution of software researchers have developed a plethora of tools to parse, model, and analyze the history of systems. Despite their usefulness, a common down...
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Composition of temporal and spatial properties of real world objects in a unified data framework results into Moving Object Databases (MOD). MODs are able to process, manage and a...
This paper presents a parallel visualization pipeline implemented at the Pittsburgh Supercomputing Center (PSC) for studying the largest earthquake simulation ever performed. The ...
Modeling the evolution of topics with time is of great value in automatic summarization and analysis of large document collections. In this work, we propose a new probabilistic gr...
Ramesh Nallapati, Susan Ditmore, John D. Lafferty,...