Lance Obermeyer

A new approach to modularity in rule-based programming

We describe a purely declarative method for introducing modularity into forward-chaining, rule-based languages and its embodiment in the Venus rule language. The method is enforced by the syntax of the language and includes the ability to parameterize the rule groups. Drawing from two of three Venus applications developed to date, we illustrate how this form of modularity contributes directly to the resolution of certain software engineering problems associated with rule languages.<<ETX>>

Selective indexing speeds production systems

Presents performance results for a production system environment, CLIPS++, that demonstrate the advantage of selectively building and applying simple index structures. We contrast this to the extensive body of work on matching, which, over the years, has evolved increasingly complex compositions of index structures which are then uniformly applied to all the data types and rules in a production system program. Over a set of benchmark programs, the fastest executions are always attained by carefully selecting a good mixture of indexes rather than universal use of a single index.

MODULARITY AND RULE-BASED PROGRAMMING

In this paper we describe a purely declarative method for introducing modularity into forward-chaining, rule-based languages. The method is enforced by the syntax of the language and includes the ability to parameterize the rule groups. We also describe the Venus programming environment, which implements the presented ideas. Drawing from two of three Venus applications developed to date, we illustrate how this form of modularity contributes directly to the resolution of certain software engineering problems associated with rule languages. We also discuss key implementation details and present performance data.

Evaluating triggers using decision trees

This paper presents an algorithm for implementing r ule filtering in active and trigger enabled databases. The algorithm generates one or more decision trees that determine what rules or triggers might be enabled b y an individual database element, reducing the number of rules or triggers that must be evaluated. The algo rithm operates by symbolically representing the space of database elements and subdividing the space based o n rule predicates. Regions of the state space repres nt particular combinations of enabled rules. Decision trees are then generated based on the subdivided state sp c . The trees have the important property that no indiv idual test is repeated. The ordered binary decision diag ram (BDD) data structure is used to represent and manipulate the state space.

Porting an expert database application to an active database: an experience report

This paper reports on our experience porting the ALEXSYS program to the Venus active database environment. Our experience shows that a deep understanding of program behavior but only moderate code changes may be required to revise an existing application to operate in an active environment. Furth er, a technique where common cases are handled by the new active database application and anomalous cases are handled by the existing expert database application may be an appropriate first step. 2. Background