Cherie Courseault Trumbach

Data Mining: Proprietary Rights, People and Proposals

This article focuses on the issue of data mining as it relates to the consumer and to the issue of whether the consumers private information has any proprietary status. A brief review of data mining is provided as a background for a better understanding of the purposes and uses of data mining. Also examined are several issues of the ethics of data mining, including a review of stakeholders, who they are and which may be most seriously affected by unethical data mining practices. Several suggestions for the improvement of data mining as it relates to the consumer are further presented: suggestions that would allow for data mining that would be beneficial to both the business community and the consumer.

A Framework for Effective Environmental Scanning and Analysis

Areas such as competitive intelligence and environmental scanning are becoming increasingly more important business practices, particularly under the intense competition of the technology industry. However, organizations are reluctant to search for information which they cannot act on immediately and are also reluctant to experiment in the absence of reliable information; conditions leading to organizational inattention. On the other hand, many organizations are spending valuable resources either on projects assessed with insufficient information or on information that they are unable to internalize. This paper provides a framework, based on environmental turbulence and organizational flexibility, to aid organizations in developing the appropriate strategy for effective environmental scanning and analysis.

Innovation and Design Process Ontology

Many domain-specific ontologies exist. These ontologies are used in text mining processes to better understand text that is available within the specific domain. Example domains include specific business areas such as marketing or functional areas such as particular types of operations within the intelligence community. This paper makes a step toward developing a broad ontology for the innovation and design process as a domain. Such an ontology can be used to better understand the discussion that takes places in the design and development of new innovations and can be used to better understand the influences on that development. In many cases, the success, failure, or final direction of a new innovation may not rest upon its technical merits but on the non-technical influences during the design and development process such as political influences. This paper uses examples within the shipbuilding domain in order to take steps toward building an Innovation and Design Process Ontology that can be applied to the Technology Delivery System (TDS) framework as a means of capturing and understanding the influences on the delivery system.

Towards optimal ship design and valuable knowledge discovery under uncertain conditions

Ship design is a complex engineering activity which requires a multidisciplinary consideration in arriving at design objectives and constraints. An optimal design of such problems require a multi-objective optimization method that is capable of finding multiple trade-off solutions, not only to choose a preferred solution for implementation, but also to have a deeper understanding of the interactions among design variables. In this paper, we consider two ship design models involving uncertainties in design variables, and demonstrate the usefulness of an evolutionary multiobjective optimization (EMO) method and subsequent data analysis procedures in arriving at valuable design principles that enhance the knowledge of a designer. The study is pedagogical yet provide key insights of ship design issues and importantly outlines the systematic procedure for applying the technology to other more complex design problems.

Visualizing technology mining results on life cycle axes: A study of server virtualization

Text data mining of research abstract databases, across time, shows how an emergent technology is progressing towards commercialization. Text data mining tools like VantagePoint can provide insights into the progress of a technology discipline showing the projection of research trends for strategic analysis and displaying the interconnections between research topics. Technology development starts with the underlying science where theoretical concepts are proposed and tested, to a more focused and technical research on devices that apply these theories. This paper demonstrates a time series text mining approach to the development of server virtualization technology as example of an emerging technology. Since there are three dimensions in the development of technology; basic science, technology development, and business process focus, this research presents the results of the text data mining of research abstracts projected in a three-dimensional virtual reality visualization.

Visualizing technology mining results on life cycle axes: A text data mining study of publication trends in server virtualization from scientific research to business process improvement

Text data mining of research abstract databases provides insight into the relationships between researchers and research topics. Text data mining tools like VantagePoint provide insights into the progress of a technology discipline showing the projection of research trends for strategic analysis and displaying the interconnections between researchers and research topics. Research in a technology typically flows from works in the underlying science where theoretical concepts are proposed and tested to a more focused and technical research on devices that apply these theories. Our study tracks the spread of ideas throughout the technology life cycle with an emphasis on technology trends rather than research clusters. The premise is that the manager seeking a strategic view of the technology, and its development over time, needs to be presented a visualization of these trends in a manner that supports effective decision-making. Since there are three dimensions in the development of technology, basic science, technology development, and business process focus, this research presents the results of the text data mining of research abstracts projected in a three-dimensional visualization.

Technology development and disasters: is there a silver lining to the dark cloud?

A significant amount of research was funded after Hurricane Katrina slammed into the Gulf Coast region. Topics range from the immediately known problems associated with flood control and weather forecasting into issues associated with social, economic and political landscapes. In an effort to understand the impact of a natural disaster on research and the implications for technology innovation and development, the current research will use tech mining approaches to analyse Science Citation Index (SCI) abstracts to determine the amount of research specifically related to Hurricane Katrina, as well as the specific subject matters studied as a result of that storm. The end result is a basis on which to analyse the impact of that storms effects on technology research. This effort is but one step in understanding the impact of such disasters on technology development.

Achieving Group Cohesion using Information Technology Tools: A Systems Perspective

This paper defines a systems theoretic model of group consensus in an information and communication technology intense environment. The model describes states of group consensus and how interventions can move the system to other states depending on the current state and the level of the intervention. The systems theoretic model is shown to be more useful in understanding why groups are influenced and how effective interventions are likely to be than the construct/influence theoretic models that dominate research in the field and can be significant tool for collaborative systems design.