Search

This paper presents a mobile system that supports young students learning history at an archaeological site. It adopts gameplay as a novel and effective technique particularly suited for learning through mobile systems (m-learning). From a technological point of view, the main novelty of our system is its slim architecture. Minimal investments are required because the system runs on the students' own cellular phones. Experimental studies indicate that gameplay is able to trigger a desire to learn more about ancient history and to make archaeological visits more exciting and learning about the past more effective. [ABSTRACT FROM AUTHOR]

Association rules discovery is a fundamental task in spatial data mining where data are naturally described at multiple levels of granularity. ARES is a spatial data mining system that takes advantage from this taxonomic knowledge on spatial data to mine multi-level spatial association rules. A large amount of rules is typically discovered even from small set of spatial data. In this paper we present a graph-based visualization that supports data miners in the analysis of multi-level spatial association rules discovered by ARES and takes advantage from hierarchies describing the same spatial object at multiple levels of granularity. An application on real-world spatial data is reported. Results show that the use of the proposed visualization technique is beneficial. [ABSTRACT FROM AUTHOR]

This paper presents a tool to display patient histories and to visually query patient data, stored in the hospital database, using a mobile device. Employing Information Visualization techniques, the developed tool is able to accommodate on the screen a good amount of information that physicians require in their analysis of the clinical cases. This work has been motivated by specific requests of physicians of a pediatric hospital treating children with neurological diseases. [ABSTRACT FROM AUTHOR]

The abundance of data available nowadays fosters the need of developing tools and methodologies to help users in extracting significant information. Visual data mining is going in this direction, exploiting data mining algorithms and methodologies together with information visualization techniques. The demand for visual and interactive analysis tools is particularly pressing in the Association Rules context where often the user has to analyze hundreds of rules in order to grasp valuable knowledge. In this paper, we present a visual strategy that exploits a graph-based technique and parallel coordinates to visualize the results of association rule mining algorithms. This helps data miners to get an overview of the rule set they are interacting with and enables them to deeper investigate inside a specific set of rules. The tools developed are embedded in a framework for Visual Data Mining that is briefly described. [ABSTRACT FROM AUTHOR]