Temporal semantic assumptions and their use in databases

Data explicitly stored in a temporal database are often associated with certain semantic assumptions. Each assumption can be viewed as a way of deriving implicit information from explicitly stored data. Rather than leaving the task of deriving (possibly infinite) implicit data to application programs, as is the case currently, it is desirable that this be handled by the database management system. To achieve this, this paper formalizes and studies two types of semantic assumptions: point-based and interval-based. The point-based assumptions include those assumptions that use interpolation methods over values at different time instants, while the interval-based assumptions include those that involve the conversion of values across different time granularities. The paper presents techniques on: 1) how assumptions on specific sets of attributes can be automatically derived from the specification of interpolation and conversion functions, and 2) given the representation of assumptions, how a user query can be converted into a system query such that the answer of this system query over the explicit data is the same as that of the user query over the explicit and the implicit data. To precisely illustrate concepts and algorithms, the paper uses a logic-based abstract query language. The paper also shows how the same concepts can be applied to concrete temporal query languages. © 1998 IEEE.