An Information Audit of a Business Intelligence Portal

This paper examines issues in promoting explicit and tacit knowledge management practices, particularly within the use of intranets. Specifically, this paper presents an information audit of the Frank Hawkins Kenan Institute of Private Enterprise's Business Intelligence Portal. The purpose of this audit is to identify the information environment of the Institute as well as its knowledge management practices. Overall, the findings of this audit indicate problems in finding, retrieving, organizing, and sharing needed information as well as a lack of collaboration and communication amongst centers. Recommendations for new technology and practices to remedy existing information and knowledge management problems are offered. This paper also acts as a roadmap for other information professionals interested in conducting information audits for their organizations

A user-centric approach for developing and deploying service front-ends in the future internet of services

Service-Oriented Architectures (SOAs) based on web services have attracted a great deal of interest and Internet Technology (IT) investment over the last few years, principally in the context of business-to-business integration within corporate intranets. However, they are now evolving and breaking through enterprise boundaries in a revolutionary attempt to make the approach pervasive. This is leading to what we call a user-centric SOA. A user-centric SOA is an SOA conceived as an internet of services made up of compositional resources empowering end users to collaboratively remix and ubiquitously exploit these resources. In this paper we explore the architectural basis, technologies, frameworks and tools considered necessary to tackle this novel vision of SOA. We also present the rationale behind Ez Web/FAST, an ongoing EU-funded project whose first outcomes could serve as a preliminary proof of concept

Modeling network traffic on a global network-centric system with artificial neural networks

This dissertation proposes a new methodology for modeling and predicting network traffic. It features an adaptive architecture based on artificial neural networks and is especially suited for large-scale, global, network-centric systems. Accurate characterization and prediction of network traffic is essential for network resource sizing and real-time network traffic management. As networks continue to increase in size and complexity, the task has become increasingly difficult and current methodology is not sufficiently adaptable or scaleable. Current methods model network traffic with express mathematical equations which are not easily maintained or adjusted. The accuracy of these models is based on detailed characterization of the traffic stream which is measured at points along the network where the data is often subject to constant variation and rapid evolution. The main contribution of this dissertation is development of a methodology that allows utilization of artificial neural networks with increased capability for adaptation and scalability. Application on an operating global, broadband network, the Connexion by Boeingʼ network, was evaluated to establish feasibility. A simulation model was constructed and testing was conducted with operational scenarios to demonstrate applicability on the case study network and to evaluate improvements in accuracy over existing methods --Abstract, page iii

Information Outlook, March 2004

Volume 8, Issue 3https://scholarworks.sjsu.edu/sla_io_2004/1002/thumbnail.jp

Network Performance Management Using Application-centric Key Performance Indicators

The Internet and intranets are viewed as capable of supplying Anything, Anywhere, Anytime and e-commerce, e-government, e-community, and military C4I are now deploying many and varied applications to serve their needs. Network management is currently centralized in operations centers. To assure customer satisfaction with the network performance they typically plan, configure and monitor the network devices to insure an excess of bandwidth, that is overprovision. If this proves uneconomical or if complex and poorly understood interactions of equipment, protocols and application traffic degrade performance creating customer dissatisfaction, another more application-centric, way of managing the network will be needed. This research investigates a new qualitative class of network performance measures derived from the current quantitative metrics known as quality of service (QOS) parameters. The proposed class of qualitative indicators focuses on utilizing current network performance measures (QOS values) to derive abstract quality of experience (QOE) indicators by application class. These measures may provide a more user or application-centric means of assessing network performance even when some individual QOS parameters approach or exceed specified levels. The mathematics of functional analysis suggests treating QOS performance values as a vector, and, by mapping the degradation of the application performance to a characteristic lp-norm curve, a qualitative QOE value (good/poor) can be calculated for each application class. A similar procedure could calculate a QOE node value (satisfactory/unsatisfactory) to represent the service level of the switch or router for the current mix of application traffic. To demonstrate the utility of this approach a discrete event simulation (DES) test-bed, in the OPNET telecommunications simulation environment, was created modeling the topology and traffic of three semi-autonomous networks connected by a backbone. Scenarios, designed to degrade performance by under-provisioning links or nodes, are run to evaluate QOE for an access network. The application classes and traffic load are held constant. Future research would include refinement of the mathematics, many additional simulations and scenarios varying other independent variables. Finally collaboration with researchers in areas as diverse as human computer interaction (HCI), software engineering, teletraffic engineering, and network management will enhance the concepts modeled

The implementation of IS on the knowledge management and mental models at the decision-making process

The emphasis of this research is to leverage Knowledge Flow Theory to enhance extant Decision Theory and decision support systems to improve the decision-making process in military organizations in general and to outline a research agenda for subsequent application to the Hellenic Navy. Thus, the challenge of this research effort is to expose the major factors that define the problem of the decision maker for correct decisions through their synthesis. The areas of the dynamic knowledge as well as the mental models have special gravity among the military personnel since they determine the decision making process. Therefore, the intention of the author will be to conduct an introduction of the existed literature and provide greater fidelity and insight into the mechanism within which the emerging technology can either support or in some cases improve our decisions. Hence, the basis of this thesis is to enlighten the technological approach for timely integrated decisions. The method that will be followed focuses on theoretical integration and is expected to result in a general decision-making process for the military that reflects explicit incorporation of Knowledge Flow Theory.http://archive.org/details/theimplementatio109455768Approved for public release; distribution is unlimited

Information Outlook, November/December 2013

Volume 17, Issue 6https://scholarworks.sjsu.edu/sla_io_2013/1005/thumbnail.jp

Survival in the e-conomy: 2nd Australian information warfare & security conference 2001

This is an international conference for academics and industry specialists in information warfare, security, and other related fields. The conference has drawn participants from national and international organisations