Lila Rao

Towards defining dimensions of knowledge systems quality

Knowledge management systems (KMS) are extremely important for organisations, primarily because they help to manage a key organisational resource - intellectual capital with the potential to produce a competitive advantage. The usefulness of this resource, however, is only as good as the quality of the knowledge that it contains. In order to improve the quality of KMS, a set of test measures is required. The purpose of this paper is to define some dimensions that can be used to measure the quality of the knowledge management system and to compare KMS quality across systems.

Building ontology based knowledge maps to assist business process re-engineering

Business Process Re-engineering (BPR) is being used to improve the efficiency of the organizational processes, however, a number of obstacles have prevented its full potential from being realised. One of these obstacles is caused by an emphasis on the business process itself at the exclusion of considering other important knowledge of the organization. Another is due to the lack of tools for identifying the cause of the inefficiencies and inconsistencies in BPR. In this paper we propose a methodology for BPR that overcomes these two obstacles through the use of a formal organizational ontology and knowledge structure and source maps. These knowledge maps are represented formally to facilitate an inferencing mechanism which helps to automatically identify the causes of the inefficiencies and inconsistencies. We demonstrate the applicability of this methodology through the use of a case study of a university domain.

Articles: An approach for ontology development and assessment using a quality framework

Ontologies have been identified as important components of a number of types of information systems, including data warehouses, e-commerce systems and knowledge management systems, and the quality of such systems is therefore likely to be heavily dependent on the quality of the embedded ontology. An ontology can be studied from two perspectives; the Artificial Intelligence (AI) perspective and the philosophical perspective. The research presented in this paper takes the AI perspective in which an ontology is considered to be an engineering artefact that can be represented using a specific vocabulary. The paper describes an approach to the development, representation and evaluation of formal ontologies with the explicit aim being to develop a set of techniques that will improve the coverage of the ontology, and thus its overall quality. The proposed approach will be illustrated by applying it to the development and evaluation of an ontology for the information technology infrastructure at a university campus.

Profiling internet banking users: A knowledge discovery in data mining process model based approach

Analysing datasets using data mining techniques can enhance decision making in organizations. However, to ensure that the full potential of these techniques is realised it is important that decision makers understand there are Knowledge Discovery and Data Mining (KDDM) processes that are mature enough to be adopted. This paper demonstrates the benefits of using a KDDM process to evaluate survey data for internet banking users in Jamaica which includes demographic as well as attitudinal and behavioral variables. The major benefits of following this process include the selection of a set of models, rather than a single model, which are more relevant to the business/research objectives and use of a more targeted knowledge discovery process as the data mining analyst is now directed to consider the effects the decisions in each phase will have on subsequent phases. This leads to more relevant knowledge being extracted from the data mining process.

An approach for incorporating quality-based cost---benefit analysis in data warehouse design

Data are considered to be important organizational assets because of their assumed value, including their potential to improve the organizational decision-making processes. Such potential value, however, comes with various costs, including those of acquiring, storing, securing and maintaining the given assets at appropriate quality levels. Clearly, if these costs outweigh the value that results from using the data, it would be counterproductive to acquire, store, secure and maintain the data. Thus cost–benefit assessment is particularly important in data warehouse (DW) development; yet very few techniques are available for determining the value that the organization will derive from storing a particular data table and hence determining which data set should be loaded in the DW. This research seeks to address the issue of identifying the set of data with the potential for producing the greatest net value for the organization by offering a model that can be used to perform a cost–benefit analysis on the decision support views that the warehouse can support and by providing techniques for estimating the parameters necessary for this model.

The Role of Ontologies in Developing Knowledge Technologies

Knowledge management has been dependent largely on technologies that are used to manage data and information. However, it is widely accepted that there is an important distinction between knowledge and data and information and until there is a focus on building strategies and technologies specific to knowledge management, the full potential of knowledge cannot be realized. Within an organization knowledge resides in numerous sources of different types such as human experts, processes, and data stores. Therefore the development of the specific technologies should focus on the management of this knowledge within these different sources. Many of these technologies need access to the knowledge of the domain which can be formally represented using an ontology. In this chapter we describe three ontology-driven knowledge technologies and discuss how they can be beneficial in harnessing knowledge in these varied sources.

Understanding and Applying Knowledge Management and Knowledge Management Systems in Developing Countries: Some Conceptual Foundations

In this chapter we provide an overview on knowledge management (KM) and knowledge management systems. Foundational concepts that are relevant to the other chapters are discussed. This chapter along with the other chapters aims to provide guidance to researchers and practitioners who are looking to address KM in developing countries.

An Approach to Using Ontologies for the Development of High Quality Disaster Recovery Plans

Disasters have the potential to cripple a country and those countries that are particularly susceptible to disasters must have effective disaster recovery plans (DRP) in place to ensure that the country can return to normalcy as soon as possible after the devastation. However, for the plan to be effective it must be of high quality, which is often viewed as a multidimensional concept containing essential factors for DRP, such as consistency, completeness, reliability and feasibility. Therefore, any methodology for the development of DRP must take these dimensions into account as their affect on quality is considerable. In this regard, the authors describe a quality based methodology for the development of DRP, including a methodology that makes use of ontologies containing properties that are suited to the development of these high quality plans. The applicability of the proposed methodology will be demonstrated through a case study of an electric utility company in Jamaica. point where the society is unable to continue to function using only its resources (e.g., human, monetary). These disasters are often classified as either natural (e.g., hurricanes, earthquakes) or manmade (e.g., terrorism, oil spills) (AlcantaraAyala, 2002; Faulkner, 2001; IDNDR, 1992). Regardless of their classification, disasters are likely to inflict extensive damage on a country’s society and infrastructure and the DOI: 10.4018/jiscrm.2010040103 36 International Journal of Information Systems for Crisis Response Management, 2(2), 35-53, April-June 2010 Copyright

Data preparation: Art or science?

Data preparation is often cited as the most time consuming phase of a Knowledge Discovery and Data Mining (KDDM) process. This is attributed to the fact that this phase is highly dependent on the expertise of the analyst. Although process models exist for KDDM the description of their phases of the process focus on outlining what must be done but often do not detail how this should be done. While there is some research in addressing the how of the phases, the data preparation phase is thought to be the most challenging and is often described as an art rather than a science. The tasks defined in this phase are thought to be highly dependent on the expertise of the analyst and the context. While we are of the view that there will always be an art to data preparation we will demonstrate that the science can actually enhance the art. We further contend that as more research of this kind is published, that demonstrates a variety of data preparation techniques that enhance the data mining process, the more effective will be the science of data preparation.

Governing knowledge commons in Caribbean disaster management: A comparative institutional analysis

This paper is based on research conducted as an initiative under the Open and Collaborative Science in Development Network (OCSDNet) to explore new innovative mechanisms that can enhance collaborative disaster recovery planning, knowledge management, and learning in the Caribbean. The need for enhanced knowledge management to mitigate disaster risk through the sharing of information and knowledge is a strategic imperative of the Caribbean Disaster Management community. The devastation of the 2017 Hurricane season was a stark reminder of the Caribbean’ vulnerability to natural disasters and underscores the urgency for Collective Action in the common challenge to mitigate the effects of these disasters and to preserve the sustainability and livelihoods of the region and it’s citizens. We employ a preliminary conceptual application of the Knowledge Commons/IAD Framework to illustrate how this kind of institutional analytic process can illuminate and inform strategy, governance and desirable collective action, as well as the merits of alternative enabling technologies. The study contributes to arguments challenging the neutrality of infrastructure for collective action. It highlights the importance, and perhaps imperative, of an institutional approach to the design and implementation of socio-technical systems.