Paton, N.W., Supporting Production Rules Using ECA Rules in an Object-Oriented Context. Information and Software Technology, 1995, 37(12): p. 691-699.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....of both high level rule types are incrementally matched using a complex event network that resembles the RETE like discrimination networks met in production systems. Similar attempts to integrate deductive and production rules into an active OODB system with event driven rules can be found in [10, 22] respectively. They follow a simpler approach, by emulating each high level rule using many event driven rules. The main advantages of our approach are: a) easier rule maintenance, b) centralised rule selection and execution control, c) straightforward implementation of traditional conflict ....

....main advantages of our approach are: a) easier rule maintenance, b) centralised rule selection and execution control, c) straightforward implementation of traditional conflict resolution strategies of KBSs, and d) net effect of events. Furthermore, the performance comparison of our approach with [22] (in [7] showed that DEVICE is faster for incremental insertion and deletion of data, while it can also be considerably faster for bulk data loading under a set oriented rule execution scheme. DEVICE has been designed for implementation on any suitably extensible active OODB system, to support ....

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N.W. Paton, Supporting production rules using ECA rules in an object-oriented context, Information and Software Technology 37(12) (1995) 691-699.

....the OODB, therefore the low level function would either access data by passing the OO interface, which violates encapsulation and integrity, or it would use the message passing facility of the OODB, which makes it no longer low level. Recently, an extension to the ADAM OODB [13, 24] called OPRA [29], incorporated production rules into an active OODB, by emulating implicit triggers into an event driven rule environment. However, OPRA follows a simpler approach, where a production rule is translated into a set of ECA rules that collectively bear the same functionality. The main advantages of ....

....main advantages of our approach are: a) easier rule maintenance, b) centralised rule selection and execution control, c) straightforward implementation of traditional conflict resolution strategies of KBSs, and d) net effect of events. Furthermore, the performance comparison of our approach with [29] (in [3] showed that DEVICE is faster in the cases of bulk data loading and value based joins, while the multiple rule approach is better at joins through object references. Our approach instead, incorporates data driven rules into a system that supports only eventdriven rules, by translating ....

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Paton, N W, Supporting production rules using ECA rules in an object-oriented context, Information and Software Technology 37(12) (1995) 691-699.

....rules (constraints) but their use is rather primitive because triggers must be explicitly evoked by member functions and all relevant constraints are inefficiently checked every time an object is accessed. There is no notion of incremental condition checking. The Chimera prototype [20] and OPRA [31], an extension of the ADAM EXACT OODB [12, 11] support deductive rules and production rules, respectively (but not both) by translating each high level rule into a set of ECA rules using the technique first described by Ceri and Widom [8] This approach is simpler than ours but has certain ....

....using the technique first described by Ceri and Widom [8] This approach is simpler than ours but has certain drawbacks, such as the delay of propagating the effects of object deletion due to re derivation of previously derived objects. Furthermore, the performance comparison of our approach with [31] (in [4] showed that, in cases of bulk data loading and value based joins, E DEVICE is faster while the one to many approach is better at joins through object references. There are several relational database systems, on the other hand, that support multiple rule types. For example, Ariel [23] ....

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N.W. Paton, "Supporting production rules using ECA rules in an object-oriented context," Information and Software Technology, Vol. 37, No. 12, 1995, pp. 691-699.

....that support both rule types, either in a generic manner or by some sort of translation between the rule formats. Furthermore, some systems support or emulate deductive rules as well. For example, Ariel [28] supports both implicit and explicit triggers under a data driven rule scheme, while OPRA [35], a recent extension to the ADAM OODB, emulates implicit triggers into an event driven rule environment. The 3 RDL1 system [31] supports deductive rules in a production rule environment, while it provides active rule extensions (A RDL) for specifying and detecting events in the deductive rule ....

.... extensions (A RDL) for specifying and detecting events in the deductive rule language [40] Finally, POSTGRES [38] and Chimera [8] support backward chaining deductive rules through data retrieval events, while deductive rules have been integrated in Starburst [9] using a similar technique with [35]. Ariel and Datex support data driven rules at the bottom level, that are efficiently matched using the special discrimination networks A TREAT [28] and LEAPS [7] respectively. Event driven rules, in Ariel only, are emulated using data driven rules and special differential files (or delta ....

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N.W. Paton, Supporting production rules using ECA rules in an object-oriented context, Information and Software Technology 37(12) (1995) 691-699.

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Paton, N.W., Supporting Production Rules Using ECA Rules in an Object-Oriented Context. Information and Software Technology, 1995, 37(12): p. 691-699.

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