Abstract

One of the most crucial problems in computer community is time management. Applications such as financial, medical, expert systems and artificial intelligence (AI) are all related to temporal data. Moreover, it is becoming common that temporal data contains some kinds of uncertainty. This uncertainty should have a sound foundational model, which can be used by those applications. Therefore, many researchers have focused on designing uncertain data models. Unfortunately, established models can't adequately address the challenges posed by uncertain temporal information, and can't adapt to all sorts of involved applications. In this paper, we propose a temporal data model, named NLTM, in which temporal primitives are divided into two parts. One is used to store date elements year, month and day. The other stores finer granularity hour, minute and so on. This model can cater to different query requirements and reduce the cost of needless query. Besides, we establish the predicates and functions of temporal primitives so as to deal with all kinds of queries.

Paper Details