Algorithms and Software for Collaborative Discovery from Autonomous, Semantically Heterogeneous, Distributed, Information Sources
(invited lecture for ALT 2005)

Author: Vasant Honavar

Affiliation: Artificial Intelligence Research Laboratory,
Center for Computational Intelligence, Learning, and Discovery,
Department of Computer Science, Iowa State University, Ames, Iowa, U.S.A.

Abstract. Development of high throughput data acquisition technologies, together with advances in computing, and communications have resulted in an explosive growth in the number, size, and diversity of potentially useful information sources. This has resulted in unprecedented opportunities in data-driven knowledge acquisition and decision-making in a number of emerging increasingly data-rich application domains such as bioinformatics, environmental informatics, enterprise informatics, and social informatics (among others). However, the massive size, semantic heterogeneity, autonomy, and distributed nature of the data repositories present significant hurdles in acquiring useful knowledge from the available data. In this talk, I will introduce some of the algorithmic and statistical problems that arise in such a setting. I will describe algorithms for learning classifiers from distributed data that offers rigorous performance guarantees (relative to their centralized or batch counterparts). I will describe how this approach can be extended to work with autonomous, and hence, inevitably semantically heterogeneous data sources, by making explicit, the ontologies (attributes and relationships between attributes) associated with the data sources and reconciling the semantic differences among the data sources from a user's point of view. This allows user or context-dependent exploration of semantically heterogeneous data sources. The resulting algorithms have been implemented in INDUS - an open source software package for collaborative discovery from autonomous, semantically heterogeneous, distributed data sources. I will briefly describe some representative applications of INDUS to data-driven knowledge acquisition tasks in bioinformatics and computational biology. I will conclude the talk with a summary of the main results, a brief discussion of related work, and an outline of some directions for further research on this topic.

Acknowledgements: Much of this work has been carried out in collaboration with members of the ISU Artificial Intelligence Research Laboratory and has been supported in part by Iowa State University and grants from the National Science Foundation (IIS 0219699) and the National Institutes of Health (GM 0066387).

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