The Advanced Integration and Mining Lab (AIM Lab) was established in 2005 to conduct data integration and data mining research in the Computer and Information Science Department at the University of Oregon. The AIM lab has been an important part of the department's strategic research initiatives in Distributed Informatics, Intelligent Information Systems, and Data Science.
There are large amounts of data stored in different data repositories, such as databases, data warehouses, the Web, and the emerging Semantic Web. They may use different structures (schemas) or semantics (ontologies) to describe their data even in the same domain. How to integrate these heterogenous data resources is still a big challenge to both database and Semantic Web research. Finding the relationships (mappings) is the first step to do data integration and it may need human involvement. Then the mappings can be used for data translation or query answering across different data resources. Our lab is developing ontology-based information systems to help both general users and domain experts (e.g., biologists, doctors, neuroscientists) to integrate, process and analyze their data.
Data mining is a useful technique for finding interesting trends or patterns in those large datasets to guide decisions about future activities. Data mining has been used in many fields. In our lab, we have been interested in using data mining to study the semantic structures of biomedical data, web data and network data. How to mine the interesting relationships across heterogenous data resources is also a very interesting topic to us. There can be some interaction between data mining and data integration tasks. For example, the generated data mining rules across different data resources can be used to guide data integration, and the integrated data can be used for finding more interesting trends and patterns.