Monitoring temporal preconditions in a behaviour oriented object model

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Abstract

Modern database applications require advanced means for modelling system structure and dynamics. Temporal logic has been proven to be a suitable vehicle for specifying the possible evolution of objects to be stored in databases. Past-directed temporal logic, as a means to describe the influence of the historical evolution of a database on applicable state changes, is one facet for the specification of object behaviour. The conceptual modelling language TROLL emphasizes the behaviour of objects over the course of time. Especially the restriction of events with preconditions in past-directed temporal logic has to be monitored, when a system specified in TROLL is implemented or prototyped. In this report we introduce a technique for monitoring (past-directed) temporal preconditions during database r runtime. This technique avoids storing the whole database history for evaluating temporal preconditions. Instead, little information about the database history is derived for specific temporal preconditions using transitions graphs. This derived information is evaluated in later database states, when the temporal preconditions is to be checked. We also describe a possible integrity monitor able to check temporal preconditions during database runtime. Such a monitor is specified locally to objects with advantages for distributed implementations. The dependency of the checking procedure on update operations leads to an optimized monitoring process that makes an efficient control of dynamic integrity constraints possible. The monitoring process itself is specified with the language TROLL. An implementation of an integrity monitor can therefore be tackled together with the language implementation. The advantages and future extensions of the proposed monitoring process and its modelling are briefly discussed. © 1994.

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Schwiderski, S., Hartmann, T., & Saake, G. (1994). Monitoring temporal preconditions in a behaviour oriented object model. Data and Knowledge Engineering, 14(2), 143–186. https://doi.org/10.1016/0169-023X(94)90042-6

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