A knowledge-based framework for the management of imprecision in data

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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.