3 research outputs found
HMAP - A Temporal Data Model Managing Intervals with Different Granularities and Indeterminacy from Natural Language Sentences
The granularity of given temporal information is
the level of abstraction at which information is expressed. Different units of measure allow one to represent different granularities.
Indeterminacy is often present in temporal information
given at different granularities: temporal indeterminacy is
related to incomplete knowledge of when the considered fact
happened.
Focusing on temporal databases, different granularities
and indeterminacy have to be considered in expressing
valid time, i.e., the time at which the information is true in
the modeled reality. In this paper, we propose HMAP, a temporal
data model extending the capability of defining valid
times with different granularity and/or with indeterminacy. In
HMAP, absolute intervals are explicitly represented by their
start, end, and duration: in this way, we can represent valid
times as “in December 1998 for five hours”, “from July 1995,
for 15 days”, “from March 1997 to October 15, 1997, between
6 and 6:30 p.m.”. HMAP is based on a three-valued logic, for
managing uncertainty in temporal relationships. Formulas involving different temporal relationships between intervals, instants, and durations can be defined, allowing one to query the
database with different granularities, not necessarily related
to that of data. In this paper, we also discuss the complexity
of algorithms, allowing us to evaluate HMAP formulas, and
showthat the formulas can be expressed as constraint networks
falling into the class of simple temporal problems, which can
be solved in polynomial time