1 Abstract We describe a set of abstractions for managing elaborate irregular data structures that communicate within Cartesian subspaces. These abstractions have been implemented as part of KeLPTools, a joint eeort involving research groups at UCSD and LBNL. We discuss the application of KeLPTools to a challenging problem involving structured adaptive mesh reenement, and discuss progress to da...

Large Volume of Data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through data mining and data science methodologies. Nevertheless these not tackle the issues in data quality clearly, leaving o...

Clustering is an important data mining technique which has gained a tremendous importance in recent times due to its inherent nature of capturing the hidden structure of the data. In Clustering, different objects that have some similarity based on their characteristics are brought together into a group. Hierarchical Clustering Analysis is one of the clustering techniques which play a significan...

We describe our experience developing a flexible architecture for collecting interaction data for a medical cognitive tutoring system. The architecture encompasses (1) an agent-based communications system for passing messages between all tutor components, as well as capturing and storing them in relational format, (2) a schema for managing all system data from low-level interface events to stud...