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

Authors: Doina Caragea, Jun Zhang, Jie Bao, Jyotishman Pathak, and 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.

Source: Algorithmic Learning Theory, 16th International Conference, ALT 2005, Singapore, October 2005, Proceedings, (Sanjay Jain, Hans Ulrich Simon and Etsuji Tomita, Eds.), Lecture Notes in Artificial Intelligence 3734, pp. 13 - 44, Springer 2005.

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. This paper introduces some of the algorithmic and statistical problems that arise in such a setting, describes algorithms for learning classifiers from distributed data that offer rigorous performance guarantees (relative to their centralized or batch counterparts). It also describes 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.

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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