Information Knowledge Systems Management

In this paper, the authors describe an innovative means of identifying patterns present in systems engineering activity when government organization acquire and build complex information systems. The research uses a Bayesian belief network to model causal relationships present in government acquisitions to create a systems engineering relative effectiveness index model that can be used to identify and analyze systems engineering patterns; and subsequently forecast possible areas of performance risks within projects and organizations.

This original experiment demonstrates knowledge workers’ ability to learn faster when a common knowledge base is represented in the recommended information structures. This paper describes the unique application of these new structures and closed knowledge system techniques in an open knowledge system employed as a collaborative environment. Information technology based collaborative environments can help teammates share by eliciting knowledge capture in these recommended information structure constructs. The new structure, named Multiple Informational Representations Required of Referent (MIRRoR) Knowledge, is shown to allow knowledge workers to learn faster and do better on posttest questions.

Findings: Knowledge bases represented in a MIRRoR Knowledge structure improve men's and women's ability to learn and remember knowledge base content, with 99% confidence.

Higher performing teams effectively leverage open knowledge systems to collaborate synergistically. Business stands to reap the practical rewards of higher performing teams when efficiency gains create more enterprise value sooner.

Studies on data center capacity planning, maintenance, and reorganization have been of interest to all its stakeholders since the data centers were first instituted. Recent study shows that data center costs contribute to nearly 25% of all information technology budgets in a company. Several methodologies have been adopted for strategic data center capacity reduction such as dynamic shutdown, virtualization, and logical partitions. The greatest challenge around data center capacity reduction is an approach that captures all data center variables and allows for strategic reduction in capacity while minimizing risks.

This paper uses causal Bayesian Belief Network to represent data center capacity planning decision process. It encapsulates three areas that influence the data center demand. These areas include market conditions, development process, and internal business decisions. The approach uses sensitivity analysis to narrow down the factors that influence the decision process the most while providing an opportunity, if one exists, to also reduce unused data center capacity. An iterative approach was applied to develop a causal Bayesian Belief Network, to carry out decisions at each stage, and to collect sensitivity values. Training data was simulated using Geometric Brownian motion generated through Monte-Carlo simulation. The Bayesian belief network itself was designed using Netica.

Globalization and the increasing complexity of systems require collaboration across multidisciplinary teams. Systems Engineering (SE) teams are often geographically and demographically dispersed; such dispersion might affect the ability of the teams to produce their desired outcomes.

The main objective of this research study was to determine how geographic and demographic dispersion affect the performance of a SE team, and which phases of the SE life cycle are more susceptible to positive or negative effects caused by team dispersion.

This research study started with an exhaustive review of the literature related to team dispersion and team performance. The next step was building a conceptual model grounded in theory, which allowed the measurement of geographic and demographic dispersion through the use of well-established indices recognized by the scientific community. The data collection process successfully gathered information about projects geographically distributed throughout 57 cities in 38 countries.

Finally, multiple linear regression (MLR) and structural equation modeling (SEM) were selected as data analysis techniques for this study. The results of MLR show that geographic and demographic dispersion factors (independent variables) statistically significantly predicted team performance along each phase (dependent variables) of the SE life cycle. The results of SEM show a moderate positive relationship between dispersion and team performance. It was also found that the SE life cycle phases of Development and Production are the higher predictors of team performance.