Grant: Discovery Grants Program - Individual

Imprecision is intrinsic to many data, especially in complex biological, ecological, and medical systems. Often, real-life decisions are made based on subjective, uncertain, and imprecise data. Thus, the management of imprecision is one of the most critical issues in data sharing, decision making, information disclosure, and data mining. Imprecision has been studied in the context of several disciplines and various methods have been developed to represent imprecise data in databases, and, recently, in the Web repositories. However, the current approaches do not address sufficiently the interpretation of imprecision in the context of domain knowledge. Yet, the interpretation of imprecision is highly contextual. For example, a statement ""blood pressure above normal"" may be sufficiently precise in one context or it could be insufficient in another context and a measurement such as ""160/90"" and additional information ""no antihypertensive medication"" may be needed. The proposed research project will answer three questions: (1) how to model different types and levels of imprecision in data, (2) how to integrate data characterized by various levels of imprecision, and (3) how to model and manage the notion of acceptable and unacceptable imprecision. The project will create a practical knowledge-based framework and software tools to represent and manage various forms of imprecision in data. The framework and tools will be evaluated in the context of two complex domains: medical decision-making and biological data classification. The evaluation will be based on two real-life cases: (1) construction of a medical data warehouse for the diagnosis and treatment of sleep disorders and (2) construction of a botanical data warehouse for the classification and analysis of mistletoe plants. The results will be applicable to data representation and processing in other knowledge-intensive domains, such as psychology, cognitive science, and ecology.