Advances in Data Modeling Research

In this paper, we summarize the discussions of the panel on Advances in Data Modeling Research, held at the Americas Conference on Information Systems (AMCIS) in 2005. We focus on four primary areas where data modeling research offers rich opportunities: spatio-temporal semantics, genome research, ontological analysis and empirical evaluation of existing models. We highlight past work in each area and also discuss open questions, with a view to promoting future research in the overall data modeling area

ssMousetrack: Analysing computerized tracking data via Bayesian state-space models in {R}

Recent technological advances have provided new settings to enhance individual-based data collection and computerized-tracking data have became common in many behavioral and social research. By adopting instantaneous tracking devices such as computer-mouse, wii, and joysticks, such data provide new insights for analysing the dynamic unfolding of response process. ssMousetrack is a R package for modeling and analysing computerized-tracking data by means of a Bayesian state-space approach. The package provides a set of functions to prepare data, fit the model, and assess results via simple diagnostic checks. This paper describes the package and illustrates how it can be used to model and analyse computerized-tracking data. A case study is also included to show the use of the package in empirical case studies