A Probabilistic Data Model and Its Semantics

As database systems are increasingly being used in advanced applications, it is becoming common that data in these applications contain some elements of uncertainty. These arise from many factors, such as measurement errors and cognitive errors. As such, many researchers have focused on defining comprehensive uncertainty data models of uncertainty database systems. However, existing uncertainty data models do not adequately support some applications. Moreover, very few works address uncertainty tuple calculus. In this paper we advocate a probabilistic data model for representing uncertain information. In particular, we establish a probabilistic tuple calculus language and its semantics to meet the corresponding probabilistic relational algebra

Propagating temporal relations of intervals by matrix

Traditional temporal relations propagating is based on Allen's Interval Algebra. This paper proposes an alternative method to propagate temporal relations among intervals, in which 5 × 5 matrices are used to represent temporal relations of intervals. Hence, the propagation of temporal relations is transformed into a numerical computation. For efficiency, we use the special values of the thirteen matrices to determine the possible temporal relations between two given intervals by using only the final resultant matrix so as to optimize the propagation. To evaluate the utility of the proposed technique, we have implemented the matrix representation in Java. The experimental results demonstrate that the approach is efficient and promising

Propagating Temporal Relations of Intervals by Matrix

Traditional temporal relations propagating is based on Allen ’ s Interval Algebra. This paper proposes an alternative method to propagate temporal relations among intervals, in which 5 £ 5 matrices are used to represent temporal relations of intervals. Hence, the propagation of temporal relations is transformed into a numerical computation. For ef ® ciency, we use the special values of the thirteen matrices to determine the possible temporal relations between two given intervals by using only the ® nal resultant matrix so as to optimize the propagation. To evaluate the utility of the proposed technique, we have implemented the matrix representation in Java. The experimental results demonstrate that the approach is ef ® cient and promising. In the real world, changes cannot be avoided. To conquer and exploit the real world for our life, we must catch the properties of time. So, Allen proposed a time world model of time interval (simply called interval) calculusÐ Interval Algebra (IA) (Allen 1983). The model divides the relationships among intervals into 13 kinds of temporal forms, laying a foundation for dealing with temporal relationships in applications. The major challenge in IA is the propagation of temporal relations. To confront this problem, traditional models are designed to improve IA. For example, a number of researches have been focused on constraint satisfactor