This calls for sophisticated tools for selective and context-sensitive information extraction and information fusion. Data Repositories of interest are physically distributed . Given the large amounts of data that is being gathered and stored at these repositories, and the fact that users are typically interested not in the raw data, but in results of analysis of the data in a given context, it is desirable to process the data in a distributed fashion wherever the data is located and selectively transmit the results of analysis. Data sources are often autonomous and the nature of access to data that is available is often restricted due to privacy and security considerations. Thus there is a need for systematic analysis of the information requirements of data analysis or decision making algorithms in such environments.
Data sources are dynamic.
The goals and consequently information needs of users as well as the data sources can change over time. This calls for development of information extraction and fusion algorithms and data mining algorithms that can dynamically adjust to shifting goals and changing constraints. Development of effective solutions to this class of problems has to necessarily incorporate recent advances in machine learning, knowledge representation, databases, distributed computing, and related areas. The workshop is open to all members of the AI community. However, the number of participants is strictly limited.