| Summary: | 碩士 === 國立中央大學 === 資訊及電子工程研究所 === 83 === Conventional databases concern itself with the most recent
data. As new data values become available through updates, the
existing values are replaced by the new data. However, the data
which human concern are not limited to recent data values. To
meet the requirement of temporal data including both past and
current data, traditional databases become insufficient. Many
researchers have made efforts toward achieving temporal
databases that handle temporal data. Behavior affects data
value, that is, behavior dominates the change of data value.
Therefore, a temporal databases should include data evolution
histories and event traces. Data evolution histories describe
how data evolve with events. Event traces depict the reason why
events take place. Nevertheless, existing temporal databases
concern nothing about behavioral aspect. They contribute to
keeping and retrieving different versions of data rather than
complete data evolution histories and event traces. This
article aims at constructing a temporal data model and query
language which incorporate behavioral aspect. This data model
depicts the structures of data evolution histories and event
traces. By using the query language data evolution histories
and event traces could be retrieved. In this article, we
construct a temporal data model, TORI model, in which finite
state machines are used to depict the structures of data
evolution histories. The structures of event traces are
described by defining the invocation relationships between
methods of objects. Besides, a pattern-based query language,
BQL, is devised to facilitate users to retrieve complete data
evolution histories and event traces from databases. This query
language also avails to handle uncertain navigation path.
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