engleski

Managing Temporal Knowledge in Port Menagment Systems

Large ports need to deal with a number of disparate activities: the movement of ships, containers and other cargo, the loading and unloading of ships and containers, customs activities. As well as human resources, anchorages, channels, lighters, tugs, berths, warehouse and other storage spaces have to be allocated and released. The efficient management of a port involves managing these activities and resources, managing the flows of money involved between the agents providing and using these resources, and providing management information. Many and information systems will be involved. Many applications have to deal with a large amount of data which not only represent the perceived state of the real world at present, but also past and/or future states. These applications are not served adequately by today's computer management and database systems. In particular, deletions and updates in such systems have destructive semantics. This means that previous database contents (representing previous perceived states of the real world) cannot be accessed anymore. Moreover, the formulation of queries which access different database states is often tedious and hence error prone without special support for the temporal dimension. Temporal knowledge in databases is concerned with extracting useful information from data stores. Consequently, we strongly believe that a temporal strategic aims to predict the future trends of a data based on its current values, and to give user some suggestions. A review of how define temporal data models, based on generalizing a non-temporal data model in to a temporal one to improve port management is presented. This paper describes a practical experiment which supports managing temporal data along with the corresponding prototype implementations.

strategies ; temporal knowledge ; temporal database ; deductive database

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