We propose incorporating a production rules facility into a relational database system. Such a facility allows definition of database operations that are automatically executed whenever certain conditions are met. In keeping with the set-oriented approach of relational data manipulation languages, our production rules are also set-oriented---they are triggered by sets of changes to the database and may perform sets of changes. The condition and action parts of our production rules may refer to the current state of the database as well as to the sets of changes triggering the rules. We define a syntax for production rule definition as an extension to SQL. A model of system behavior is used to give an exact semantics for production rule execution, taking into account externally-generated operations, selftriggering rules, and simultaneous triggering of multiple rules. 1 Introduction Recently, there has been considerable interest in integrating production rules systems and database manag...

In this paper, we describe an abstract framework and axioms under which exact local computation of marginals is possible. The primitive objects of the framework are variables and valuations. The primitive operators of the framework are combination and marginalization. These operate on valuations. We state three axioms for these operators and we derive the possibility of local computation from the axioms. Next, we describe a propagation scheme for computing marginals of a valuation when we have a factorization of the valuation on a hypertree. Finally we show how the problem of computing marginals of joint probability distributions and joint belief functions fits the general framework. 1.

. Static analysis methods are given for determining whether arbitrary sets of database production rules are (1) guaranteed to terminate

Integrating a production rules facility into a database system provides a uniform mechanism for a number of advanced database features including integrity constraint enforcement, derived data maintenance, triggers, alerters, protection, version control, and others. In addition, a database system with rule processing capabilities provides a useful platform for large and efficient knowledge-base and expert systems. Database systems with production rules are referred to as active database systems, and the field of active database systems has indeed been active. This chapter summarizes current work in active database systems; topics covered include active database rule models and languages, rule execution semantics, and implementation issues. 1 Introduction Conventional database systems are passive: they only execute queries or transactions explicitly submitted by a user or an application program. For many applications, however, it is important to monitor situations of interest, and to ...

Database researchers have recognized that integrating a production rules facility into a database system provides a uniform mechanism for a number of advanced database features including integrity constraint enforcement, derived data maintenance, triggers, protection, version control, and others. In addition, a database system with rule processing capabilities provides a useful platform for large and efficient knowledge-base and expert systems. Database systems with production rules are referred to as active database systems, and the field of active database systems has indeed been active. This paper summarizes current work in active database systems and suggests future research directions. Topics covered include database rule languages, rule processing semantics, and implementation issues. 1 Introduction Database systems provide persistent storage for massive amounts of data and powerful interfaces for querying and modifying this data. Even so, most database systems are passive, si...

Soar is an architecture for a system that is intended to be capable of general intelligence. Chunking, a simple experience-based learning mechanism, is Soar's only learning mechanism. Chunking creates new items of information, called chunks, based on the results of problem-solving and stores them in the knowledge base. These chunks are accessed and used in appropriate later situations to avoid the problem-solving required to determine them. It is already well-established that chunking improves performance in Soar when viewed in terms of the subproblems required and the number of steps within a subproblem. However, despite the reduction in number of steps, sometimes there may be a severe degradation in the total run time. This problem arises due to expensive chunks, i.e., chunks that require a large amount of effort in accessing them from the knowledge base. They pose a major problem for Soar, since in their presence, no guarantees can be given about Soar's performance.

This paper gives methods for statically analyzing sets of active database rules to determine if the rules are (1) guaranteed to terminate, (2) guaranteed to produce a unique final database state, and (3) guaranteed to produce a unique stream of observable actions. If the analysis determines that one of these properties is not guaranteed, it isolates the rules responsible for the problem and determines criteria that, if satisfied, guarantee the property. The analysis methods are presented in the context of the Starburst Rule System. 1 Introduction Rules in active database systems allow specification of data manipulation operations that are executed automatically whenever certain events occur or conditions are met [DHW94,DW92,HW93]. Active database rules provide a general and powerful mechanism for many database features, including integrity constraint enforcement, derived data maintenance, triggers, alerters, authorization checking, and versioning. In addition, active database sy...

The subject of this paper is the description of a process-centered software development environment called MERLIN which monitors and guides teams of software developers and managers in producing software objects. Software objects (or objects for short) include all sorts of documents like the requirements analysis, design, code, user manuals, contracts etc. For each user, MERLIN automatically displays a specific working context which contains information like objects, their relations, their current development state, and corresponding tools. This information is filtered according to the (access) rights and duties a particular user has in a particular project, i.e. the working context depends on the user's role (e.g. programmer, designer, manager). Internally, the computation of the information to be contained in a working context, is based on a rule-like definition of a software process and a flexible interpretation mechanism to enact such a process definition.The main feature of the in...

This paper describes our development of the Starburst Rule System, an active database rules facility integrated into the Starburst extensible relational database system at the IBM Almaden Research Center. The Starburst rule language is based on arbitrary database state transitions rather than tuple- or statement-level changes, yielding a clear and flexible execution semantics. The rule system has been implemented completely. Its rapid implementation was facilitated by the extensibility features of Starburst, and rule management and rule processing is integrated into all aspects of database processing. Index terms: active database systems, database production rules, extensible database systems, expert database systems 1 Introduction Active database systems allow users to create rules---rules specify data manipulation operations to be executed automatically whenever certain events occur or conditions are met. Active database rules provide a general and powerful mechanism for traditiona...

Expert database systems extend the functionality of conventional database systems by providing a facility for creating and automatically executing Condition-Action rules. While Condition-Action rules in database systems are very powerful, they also can be very difficult to program, due to the unstructured and unpredictable nature of rule processing. We provide methods for static analysis of Condition-Action rules; our methods determine whether a given rule set is guaranteed to terminate, and whether rule execution is confluent (has a guaranteed unique final state). Our methods are based on previous methods for analyzing rules in active database systems. We improve considerably on the previous methods by providing analysis criteria that are much less conservative: our methods often determine that a rule set will terminate or is confluent when previous methods could not. Our improved analysis is based on a "propagation" algorithm, which uses a formal approach based on an extended relatio...