Using KBS verification techniques to demonstrate the existence of rule anomalies in ADBs

Abstract

As the field of verification and validation for knowledge-based systems (KBSs) has matured, much information, technology, and theory has become available. Though not all of the problems with respect to KBSs have been solved, many have been identified with solutions that can be used in an analogous manner in situations where the application is not necessarily a traditional KBS. As one example, the “active” component in an active database (ADB) consists of rules that execute as a result of database accesses and updates. In this paper, we demonstrate that anomalies found to impact the correctness of a KBS can also exist in ADBs. We first compare the rule structure of a KBS with the rule structures of various ADBs. To show their existence, we convert the rule syntax of the ADBs into a consistent format for analysis and anomaly detection. Once converted, we apply KBS verification techniques to isolate these anomalies. Due to the more increasing use of triggered rules in ADBs, this work illustrates the danger these anomalies can pose and the ever increasing need for ADB verification techniques to exist.

Introduction

An active database (ADB) is a database that dynamically executes specified actions when certain conditions arise, without any user interaction. ADBs have grown in importance because they can be used to enforce integrity constraints, trigger actions based on occurrences of a particular database state or transition between states, and perform knowledge based processing within the database system [1]. Oracle 8 is one such widely used, commercial database. The “active” component in an ADB consists of rules that execute as a result of database accesses and updates. The incorporation of an “active” component or a rule system to a passive database increases the overall power of the database by promoting inference with the data available. This additional power provides the flexibility of interpreting information from data that would otherwise have little meaning.

Verification involves demonstrating that the system behaves as expected. We use the term “anomaly” for an execution or structural attribute of the system that causes unexpected behavior. Not all anomalies lead to errors. Knowledge-based system (KBS) verification includes examining the structure and semantics of a knowledge base, which mainly consists of sets of rules. Primary verification tools generally analyze the knowledge base for anomalies, which may be logical, epistemological, or semantic, and often fall into the following categories [2], [3]: redundancy, inconsistency, and incompleteness. Redundancy is the presence of rules that add no new knowledge to the database. Inconsistency refers to conflicts among rules when more than one rule can execute but with contradictory consequences. Incompleteness occurs when the rule system cannot achieve a goal state from a legal initial state. These categories of anomalies and methods of their detection have already been widely researched for various types of KBSs, including rule-based and hybrid systems that may include objects, message passing, and procedures e.g. [2], [3], [4], [5], [6], [7], [8], [9], [10]. We use these definitions to determine whether reliability can be compromised with respect to the rule activation and execution in ADBs.

In this paper, we demonstrate via KBS verification techniques that anomalies exist in rule execution of an ADB that can go undetected. The paper is organized as follows: Section 2 provides brief background material on ADBs. Section 3 discusses verification as it is applied to the rules in KBSs. This section also includes an examination of the verification anomalies: redundancy, incompleteness, and inconsistency. Section 4 compares the rule structures of KBS rules to ADB rules, showing that the overall rule formats between these systems are the same. In addition, this section presents our method for demonstrating these anomalies exist, which includes placing the ADB rule in an intermediate representation and using KBS verification analysis over that representation. Section 5 demonstrates the existence of rule anomalies in ADBs and discusses their impact on its integrity. Section 6 concludes the paper with discussion.