Daniel E. O'Leary

Enterprise knowledge management

Many enterprises downsize to adapt to more competitive environments, but unless they have captured the knowledge of their employees, downsizing can result in a loss of critical information. Similarly, as employees leave, organizations are likely to lose access to large quantities of critical knowledge. As companies expand internationally, geographic barriers can affect knowledge exchange and prevent easy access to information. These and other forces are pushing enterprises to explore better methods for knowledge management. Enterprise knowledge management entails formally managing knowledge resources, typically by using advanced information technology. KM is formal in that knowledge is classified and categorized according to a prespecified, but evolving, ontology into structured and semistructured data and knowledge bases. The overriding purpose of enterprise KM is to make knowledge accessible and reusable to the enterprise. The business world is becoming so concerned about knowledge management that, according to one report, over 40 percent of the Fortune 1000 now have a chief knowledge officer, a senior-level executive responsible for creating an infrastructure and cultural environment for knowledge sharing. This article surveys some components of this young field.

Using AI in knowledge management: knowledge bases and ontologies

This article looks at the use of artificial intelligence in knowledge management systems, focusing on such AI-related technologies as knowledge bases and ontologies. Because these technologies both depend on particular settings, the author discusses knowledge management as practiced at three major professional services firms.

Expert system verification and validation: a survey and tutorial

Assuring the quality of an expert system is critical. A poor quality system may make costly errors resulting in considerable damage to the user or owner of the system, such as financial loss or human suffering. Hence verification and validation, methods and techniques aimed at ensuring quality, are fundamentally important.This paper surveys the issues, methods and techniques for verifying and validating expert systems. Approaches to defining the quality of a system are discussed, drawing upon work in both computing and the model building disciplines, which leads to definitions of verification and validation and the associated concepts of credibility, assessment and evaluation. An approach to verification based upon the detection of anomalies is presented, and related to the concepts of consistency, completeness, correctness and redundancy. Automated tools for expert system verification are reviewed.Considerable attention is then given to the issues in structuring the validation process, particularly the establishment of the criteria by which the system is judged, the need to maintain objectivity, and the concept of reliability. This is followed by a review of validation methods for validating both the components of a system and the system as a whole, and includes examples of some useful statistical methods. Management of the verification and validation process is then considered, and it is seen that the location of methods for verification and validation in the development life-cycle is of prime importance.

Artificial intelligence and virtual organizations

Artificial Intelligence AND Virtual Or A VIRTUAL COMPANY HAS BEEN DEFINED AS ONE where “complementary resources existing in a number of cooperating companies are left in place, but are integrated to support a particular product effort for as long as it is viable to do so. . . . Resources are selectively allocated to the virtual company if they are underutilized or if they can be profitably utilized there more than in the ‘home’ company.” [4]. In addition, virtual organizations are designed to facilitate three types of capabilities:

Artificial Intelligence and Big Data

AI Innovation in Industry is a new department for IEEE Intelligent Systems, and this paper examines some of the basic concerns and uses of AI for big data (AI has been used in several different ways to facilitate capturing and structuring big data, and it has been used to analyze big data for key insights).

Knowledge management across the enterprise resource planning systems life cycle

Abstract This paper investigates the use of knowledge management to support enterprise resource planning (ERP) systems across their entire life cycle. Knowledge management can be used to support ERP system in their choice, implementation and use, both inputs and outputs. This paper summarizes a number of actual examples and discusses some emerging efforts, focusing on knowledge management, with particular interest in case-based knowledge management. A prototype system designed to support the use of an ERP system is presented.

Blog mining-review and extensions: From each according to his opinion

Blogs provide a type of website that contains information and personal opinions of the individual authors. The purpose of this paper is to review some of the literature aimed at gathering opinion, sentiment and information from blogs. This paper also extends the previous literature in a number of directions, extending the use of knowledge from tags on blogs, finding the need for domain specific terms to capture a richer understanding of mood of a blog and finding a relationship between information in message boards and blogs. The relationship between blog chatter and sales, and blogs and public image are also examined.

Gartner's hype cycle and information system research issues

This paper uses Gartner Groups hype cycle as a basis to analyze research issues and opportunities in information systems. The findings including, “where” we find a technology in the hype cycle can influence the kinds of research questions we can ask, the information available about that technology and the research methods that can be employed on the technology at that stage.

Wikis: 'From Each According to His Knowledge'

Wikis offer tremendous potential to capture knowledge from large groups of people, making tacit, hidden content explicit and widely available. They also efficiently connect those with information to those seeking it.

How knowledge reuse informs effective system design and implementation

The author analyzes knowledge reuse in a Big-5 professional service firm. He investigates how reuse affects design, provides a methodology for evaluating systems to increase reuse, and discusses the impact of differential costs and benefits on the organization.