Jon R. Wright

An industrial-strength description logic-based configurator platform

Modern communications equipment is highly modular and can scale to a wide range of applications. Usually, the equipments cost and complexity requires that it be manufactured to order, or at least assembled-to-order. In this context, orders double as specifications, describing what should be manufactured as well as how the product should be installed. Producing a correct and complete order for such equipment can be challenging when requirements are incomplete, inconsistent, or when the final product is large and complicated. A good order is technically correct and meets customer requirements for network capacity and growth without over-engineering. Incomplete configurations can lead to cost overruns if the missing elements are discovered during manufacturing. If they are not, faulty products can result. Either way, the customers are unhappy. We have tackled the configuration problem for a number of large telecommunications products. Our Prose configurators are based on CLASSIC, a description logic-based knowledge representation system. We have found it to be well suited to our configurator needs. Because it attempts to provide predictable performance in all cases, CLASSIC is less expressive than many description logic systems, but it has been widely used in both industrial applications and academic systems. Some of our configurators have been in use since 1990. They have processed more than

Conceptual modelling for configuration: A description logic-based approach

4.5 billion in orders and have documented many benefits, including reduced order processing time, reduced staffing, and product-knowledge consistency checking.

Data quality through knowledge engineering

Representing objects and their interactions can be quite challenging when an application requires many complicated, interconnected objects that are restricted in how they can be instantiated. In this paper, we present our approach to conceptual modelling. We have used this approach with success in a number of applications, the largest of which is the PROSE family of configurators. PROSE was first deployed in 1990 and has been used to configure over 4 billion dollars worth of AT&T and Lucent telecommunications equipment. We will discuss our approach to conceptual modelling, which is based on knowledge representation, show how it meets our representation and reasoning needs, and then discuss the relative merits of the approach.2

ACE: Going from prototype to product with an expert system

Traditionally, data quality programs have acted as a preprocessing stage to make data suitable for a data mining or analysis operation. Recently, data quality concepts have been applied to databases that support business operations such as provisioning and billing. Incorporating business rules that drive operations and their associated data processes is critically important to the success of such projects. However, there are many practical complications. For example, documentation on business rules is often meager. Rules change frequently. Domain knowledge is often fragmented across experts, and those experts do not always agree. Typically, rules have to be gathered from subject matter experts iteratively, and are discovered out of logical or procedural sequence, like a jigsaw puzzle. Our approach is to impement business rules as constraints on data in a classical expert system formalism sometimes called production rules. Our system works by allowing good data to pass through a system of constraints unchecked. Bad data violate constraints and are flagged, and then fed back after correction. Constraints are added incrementally as better understanding of the business rules is gained. We include a real-life case study.

Information quality for network monitoring and analysis

ACE (Automated Cable Expertise) is a knowledge-based expert system that provides trouble-shooting and diagnostic reports for telephone company managers. Its application domain is telephone cable maintenance. ACE departs from standard expert system architecture in that a separate data base system is used as its primary source of information. ACE designers were influenced by the R1/XCON project, and ACE uses techniques similar to those of R1/XCON. This paper reports the progress of ACE as it moves out of experimentation and into a live software environment, and characterizes it in terms of current expert system technology.

A Knowledge-Based Configurator that Supports Sales, Engineering, and Manufacturing at AT&T Network Systems

Networks based on the Internet Protocol (IP) generate a large amount of technical data, making monitoring and analysis by administrators a tough problem that requires technical knowledge. Network monitoring and analysis as currently practiced are largely data-limited activities, and could be improved by supplying an integrated database of network data. We have been designing a collection system, named Amber, to address the information quality issues we have encountered in data needed to support management of IP networks. Our experiences in implementing Amber illustrate the importance of paying attention to and understanding the underlying data-generating processes.