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| Content Provider | ACM Digital Library |
|---|---|
| Author | Ross, Ronald G. |
| Abstract | Point-and-Click Expression Builders, for instance limits and type consistency.Structured English, for more complex restrictions and logical inferences.Entity Life History or State Transition Diagrams, for both basic and more advanced state transition rules.Data Model or Class Model extensions, for basic property rules.No matter how the rules are captured, there should be a single, unified conceptual representation “inside” of the man-machine boundary. “Inside” here means transparent to the specifiers, but visible to analysis tools (e.g., for conflict analysis) and to rule engines or business logic servers (for run-time processing).Inside, there may be still other representations. For processing and performance reasons, there might be many physical representations of the rules, optimized for particular tools or hardware/software environments.The result is actually three layers of representation: external, conceptual, and internal. This is strongly reminiscent of the old ANSI/SPARC three-schema architecture for data. This should not be surprising since rules simply build on terms and facts, which can be ultimately represented by data. Where is this research now? A new, more concise representation scheme is under development. One focus of this scheme is a formal expression of how non-atomic rule types are derived from atomic ones. This would allow reduction of rules to a common base of fundamental rule types, in order to support automatic analysis of conflict and overlap in systematic fashion.This is opening exciting new avenues of research, and significant opportunities for those interested in getting involved. |
| Starting Page | 515 |
| Ending Page | 516 |
| Page Count | 2 |
| File Format | |
| ISBN | 1581132174 |
| DOI | 10.1145/342009.335457 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2000-05-16 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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