A neuro-fuzzy approach in student modeling

Book synopsis: The International User Modeling Conferences are the events at which research foundations are being laid for the personalization of computer systems. In the last 15 years, the field of user modeling has produced significant new theories and methods to analyze and model computer users in short and long term interactions. A user model is an explicit representation of properties of individual users or user classes. It allows the system to adapt its performance to user needs and preferences. Methods for personalizing human computer interaction based on user models have been successfully developed and applied in a number of domains, such as information filtering, adaptive natural language and hypermedia presentation, tutoring systems, e commerce and medicine. There is also a growing recognition of the need to evaluate the results of new user modeling methods and prototypes in empirical studies and a growing focus on evaluation methods. New trends in HCI create new and interesting challenges for user modeling. While consolidating results in traditional domains of interest, the user modeling field now also addresses problems of personalized interaction in mobile and ubiquitous computing and adaptation to user attitudes and affective states. Finally, with the spread of user modeling in everyday applications and on the Web, new concerns about privacy preservation are emerging. All these topics are covered in the proceedings of UM 2003, the 9th International Conference on User Modeling

Path prediction and predictive range querying in road network databases

In automotive applications, movement-path prediction enables the delivery of predictive and relevant services to drivers, e.g., reporting traffic conditions and gas stations along the route ahead. Path prediction also enables better results of predictive range queries and reduces the location update frequency in vehicle tracking while preserving accuracy. Existing moving-object location prediction techniques in spatial-network settings largely target short-term prediction that does not extend beyond the next road junction. To go beyond short-term prediction, we formulate a network mobility model that offers a concise representation of mobility statistics extracted from massive collections of historical object trajectories. The model aims to capture the turning patterns at junctions and the travel speeds on road segments at the level of individual objects. Based on the mobility model, we present a maximum likelihood and a greedy algorithm for predicting the travel path of an object (for a time duration h into the future). We also present a novel and efficient server-side indexing scheme that supports predictive range queries on the mobility statistics of the objects. Empirical studies with real data suggest that our proposals are effective and efficient