In many real-world applications, temporal information is often imprecise about the temporal location of events (indeterminacy) and comes at different granularities (Dyreson and Snodgrass 1995). Temporal granularity and indeterminacy are thus emerging as crucial requirements for the advancement of intelligent information systems which have to store, manage, and reason about temporal data. Consider, for example, these events taken from the application-a temporal database for cardiological patients- we are considering in our research (Combi and Chittaro 1999): '‘between 2 p.m. and 4 p.m. on May 5, 1996, the patient suffered from a myocardial infarction’', '‘he started the therapy with thrombolytics in July 1995'', '‘on October 12, 1996, he had a follow-up visit’'. The three events happened at the hours, months, and days timelines, respectively.
Extending the Event Calculus with Temporal Granularity and Indeterminacy*
Chittaro L.;Combi C.
2005-01-01
Abstract
In many real-world applications, temporal information is often imprecise about the temporal location of events (indeterminacy) and comes at different granularities (Dyreson and Snodgrass 1995). Temporal granularity and indeterminacy are thus emerging as crucial requirements for the advancement of intelligent information systems which have to store, manage, and reason about temporal data. Consider, for example, these events taken from the application-a temporal database for cardiological patients- we are considering in our research (Combi and Chittaro 1999): '‘between 2 p.m. and 4 p.m. on May 5, 1996, the patient suffered from a myocardial infarction’', '‘he started the therapy with thrombolytics in July 1995'', '‘on October 12, 1996, he had a follow-up visit’'. The three events happened at the hours, months, and days timelines, respectively.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
