By Yong Tang, Zewu Peng, Dongning Liu, Wenshen Zhang (auth.), Prof. Yong Tang, Xiaoping Ye, Na Tang (eds.)
"Temporal info Processing know-how and Its purposes" systematically reports temporal info processing know-how and its purposes. The publication covers following matters: 1) time version, calculus and good judgment; 2) temporal information versions, semantics of temporal variable ‘now’ temporal database thoughts; three) temporal question language, a customary temporal database administration approach: TempDB; four) temporal extension on XML, workflow and information base; and, five) implementation styles of temporal purposes, a customary instance of temporal program. The booklet is meant for researchers, practitioners and graduate scholars of databases, data/knowledge administration and temporal info processing. Dr. Yong Tang is a professor on the machine tuition, South China general collage, China.
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Additional info for Temporal Information Processing Technology and Its Application
In the 1980s, due to the rapid development of computer-related technology, especially network and multimedia technology, many demands and applications of temporal information were implemented. The main application areas included geographic information systems, agricultural information systems, telecommunications information systems, e-commerce, intelligent decision support systems, data warehouse and data mining, in particular temporal spatio-temporal information technology and temporal applications of multimedia information technology.
In (t, T ) Holds ( p, t)) The negation of a property p is defined as Holds (not ( p), T ) ( t. In (t, T ) ¬Holds ( p, t)) This means that if a property p holds over an interval T, it holds over all sub-intervals of T. However, Allen could not define events and actions using “Holds” predicate. Therefore, he defines another predicate “Occur”. Reason for the use of another predicate is his definition of “Holds” predicate. Holds (p, T ) says that property p holds over an interval T as well as all sub-intervals of T.
When we query the value of current data, we need to backtrack to the last changed status. For example, Fig. 1 presents that there have been three changes in the identity of Li since 1997, so only three time points need to be recorded when we adopt a stepwise model. If the identity of Li is queried on the time point Now, the value returned is not Null, but the value “associate professor” by backtracking from time axis. This model is different from continuous model as we are not able to get the value of the point between any two points through interpolation method.
Temporal Information Processing Technology and Its Application by Yong Tang, Zewu Peng, Dongning Liu, Wenshen Zhang (auth.), Prof. Yong Tang, Xiaoping Ye, Na Tang (eds.)