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Information, Knowledge, Systems Management
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   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1389-1995 - ISSN (Online) 1875-8762
   This journal is no longer being updated because:
    The journal ceased publication
  • Views on human-machine-systems science and engineering
    • Abstract: Human-Machine-Systems Science and Engineering is viewed as a multidisciplinary discipline. After a personal dedication of this essay to Andrew P. Sage, the discipline is briefly introduced with applications in all areas across the whole society in which humans interact and collaborate with technical artifacts. Different perspectives of the human-machine-systems discipline are explained which are interrelated with each other. These are the systems perspective, the human factors and ergonomics perspective, the information and the knowledge perspective, the control perspective, the cognition perspective, and the management perspective. The concluding remarks describe the difference between the two disciplines of Human-Machine-Systems Science and Engineering and Human-Computer Interaction and refer to the main international conferences in both fields.
      Content Type Journal Article
      Pages 187-195
      DOI 10.3233/IKS-150232
      Authors
      Gunnar Johannsen, Hamburg and Schwalmstadt, Univ.-Professor of Systems Engineering and Human-Machine Systems, Department of Mechanical Engineering, University of Kassel, Germany. E-mail: g.johannsen@uni-kassel.de
      Journal Information, Knowledge, Systems Management
      Online ISSN 1389-1995
      Journal Volume Volume 12
      Journal Issue Volume 12, Number 3-4 / 2013/2015
      PubDate: Thu, 16 Apr 2015 14:38:24 GMT
       
  • Reverse engineering: Mining structural system models from system data
    • Abstract:
      Content Type Journal Article
      Pages 205-208
      DOI 10.3233/IKS-150233
      Authors
      Nong Ye, School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe, AZ 85287-8809, USA. E-mail: nongye@gmail.com
      Journal Information, Knowledge, Systems Management
      Online ISSN 1389-1995
      Journal Volume Volume 12
      Journal Issue Volume 12, Number 3-4 / 2013/2015
      PubDate: Thu, 16 Apr 2015 14:38:24 GMT
       
  • Author Index Volume 12 (2013/2015)
    • Abstract:
      Content Type Journal Article
      Pages 283-284
      Journal Information, Knowledge, Systems Management
      Online ISSN 1389-1995
      Journal Volume Volume 12
      Journal Issue Volume 12, Number 3-4 / 2013/2015
      PubDate: Thu, 16 Apr 2015 14:38:24 GMT
       
  • Tribute to Andy Sage, and a new taxonomy of model attributes
    • Abstract:
      Content Type Journal Article
      Pages 175-181
      DOI 10.3233/IKS-150230
      Authors
      Thomas B. Sheridan, Massachusetts Institute of Technology. E-mail: sheridan@mit.edu
      Journal Information, Knowledge, Systems Management
      Online ISSN 1389-1995
      Journal Volume Volume 12
      Journal Issue Volume 12, Number 3-4 / 2013/2015
      PubDate: Thu, 16 Apr 2015 14:38:24 GMT
       
  • A dynamic network analytic perspective on the state of computational
           social science
    • Abstract: Computational Social Science has evolved remarkably since its inception. The growing ability to mine, analyze, and visualize big data and the growing interdisciplinarity of core methodologies, suggests that there is an even greater evolutionary step to come. This is particularly true from a dynamic network perspective. Herein, the state of the field in terms of great advances and core challenges is described. Although there have been great strides, the core challenges today are modern variants of challenges that have perennial impacted this field.
      Content Type Journal Article
      Pages 197-203
      DOI 10.3233/IKS-150231
      Authors
      Kathleen M. Carley, Institute for Software Research, Engineering and Public Policy, Center for Computational Analysis of Social and Organizational Science, Pittsburgh, PA, USA. E-mail: kathleen.carley@cs.cmu.edu
      Journal Information, Knowledge, Systems Management
      Online ISSN 1389-1995
      Journal Volume Volume 12
      Journal Issue Volume 12, Number 3-4 / 2013/2015
      PubDate: Thu, 16 Apr 2015 14:38:24 GMT
       
  • Multi-formalism modeling for the enterprise
    • Abstract:
      Content Type Journal Article
      Pages 183-185
      DOI 10.3233/IKS-150229
      Authors
      Alexander H. Levis, George Mason University, Fairfax County, VA, USA. E-mail: alevis@gmu.edu
      Journal Information, Knowledge, Systems Management
      Online ISSN 1389-1995
      Journal Volume Volume 12
      Journal Issue Volume 12, Number 3-4 / 2013/2015
      PubDate: Thu, 16 Apr 2015 14:38:24 GMT
       
  • Thy destroyers &ldots; shall go forth of thee (Isaiah 49:17)
    • Abstract:
      Content Type Journal Article
      Pages 173-174
      DOI 10.3233/IKS-150228
      Authors
      Arye R. Ephrath, Mythology, Inc., Fairfax, Virginia, USA. E-mail: a.ephrath@ieee.org
      Journal Information, Knowledge, Systems Management
      Online ISSN 1389-1995
      Journal Volume Volume 12
      Journal Issue Volume 12, Number 3-4 / 2013/2015
      PubDate: Thu, 16 Apr 2015 14:38:24 GMT
       
  • A transformational contributor to the theory and practice of systems
           engineering and systems management
    • Abstract:
      Content Type Journal Article
      Pages 167-168
      DOI 10.3233/IKS-150227
      Authors
      William B. Rouse, E-mail: rouse@stevens.edu
      Journal Information, Knowledge, Systems Management
      Online ISSN 1389-1995
      Journal Volume Volume 12
      Journal Issue Volume 12, Number 3-4 / 2013/2015
      PubDate: Thu, 16 Apr 2015 14:38:24 GMT
       
  • A perspective on the changing nature of R&D investment and
           collaboration in the Asian-Pacific
    • Abstract: The global leadership of science and technology appears to be pivoting towards the Asian Pacific as measured by available funding, scientific output, and growth of R&D infrastructure and professional workforce. The emerging dynamics of this shift and its implications of this for future strategic investment and collaboration between the US and Asian fundamental science communities are addressed. In particular, this paper examines the impact on sponsorship of foreign fundamental R&D by agencies of the US government which has been of historic value to advancing the global state of knowledge and technologies in a number of domains.
      Content Type Journal Article
      Pages 223-231
      DOI 10.3233/IKS-150225
      Authors
      Kenneth R. Boff, Socio-Technical Sciences, USA. E-mail: kboff@me.com
      Journal Information, Knowledge, Systems Management
      Online ISSN 1389-1995
      Journal Volume Volume 12
      Journal Issue Volume 12, Number 3-4 / 2013/2015
      PubDate: Thu, 16 Apr 2015 14:38:23 GMT
       
  • A systems approach to a research university's R&D management strategy
    • Abstract: The Georgia Institute of Technology (Georgia Tech), a major research university in the United States of America, has a legacy culture of embedding innovation throughout its education and research programs and thus an important role in facilitating economic development within its region. A new strategic vision introduced in 2010 to guide Georgia Tech's role nationally and internationally, included a systems approach to support faculty-led research concurrently with a focus on maximizing industry and societal benefit. The systems approach guides research infrastructure investments into core research areas and institute-wide support for discovery, application, and deployment functions. It also provides a venue for students to discover and explore disruptive innovations. This paper explores the similarities and differences between systems engineering in an industry setting and in the implementation of a university-based research strategy. Examples are cited from ongoing research in robotics, energy systems, health care technology, and advanced manufacturing.
      Content Type Journal Article
      Pages 209-221
      DOI 10.3233/IKS-150226
      Authors
      Stephen E. Cross, H. Milton School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, GA, USA. Tel.: +1 404 894 8885; Fax: +1 404 894 7035; E-mail: cross@gatech.edu
      Journal Information, Knowledge, Systems Management
      Online ISSN 1389-1995
      Journal Volume Volume 12
      Journal Issue Volume 12, Number 3-4 / 2013/2015
      PubDate: Thu, 16 Apr 2015 14:38:23 GMT
       
  • An integrated cost and performance model to inform capability selection
           during early-phase systems engineering: Case analysis and multi-objective
           optimization
    • Abstract: Functionality and performance have been key elements of requirements generation for major systems, especially within the Department of Defense (DoD). However, rising cost growth rates and decreasing funding have led to legislation requiring DoD to factor cost into requirements selection and to keep within cost estimates much earlier in a system's life cycle. This paper presents a framework that supports a combined cost and performance estimation model. It provides a greater emphasis on cost to assess the impact of specific requirements selection for major hardware/software systems. This model is used within a synthesized process derived from new cases within DoD. It also uses Multi-Objective Optimization (MOO), specifically physical programming, as a means to better define and assess the optimal selection of capabilities in the requirements generation phase rather than the design phase. The result is a repeatable, analytical process and model that can be used throughout a system's life cycle.
      Content Type Journal Article
      Pages 255-282
      DOI 10.3233/IKS-140224
      Authors
      Edward M. DeVilliers, Primary Correspondent, 25 Innsbrook Ct, Stafford, VA, USA
      Shahram Sarkani, Department of Engineering Management and Systems Engineering, School of Engineering and Applied Science, The George Washington University, Washington, DC, USA
      Thomas Mazzuchi, Department of Engineering Management and Systems Engineering, School of Engineering and Applied Science, The George Washington University, Washington, DC, USA
      Journal Information, Knowledge, Systems Management
      Online ISSN 1389-1995
      Journal Volume Volume 12
      Journal Issue Volume 12, Number 3-4 / 2013/2015
      PubDate: Thu, 16 Apr 2015 14:38:23 GMT
       
  • Identifying requirement attributes for materiel and non-materiel solution
           sets utilizing discrete choice models
    • Abstract: In 2008, the Government Accountability Office (GAO) performed a study on 11 Department of Defense (DoD) programs and compared how requirements were developed between DoD and private industry. The study found that the DoD did not follow good Systems Engineering practices when developing requirements, resulting in program cost and schedule overruns. With 2011 Defense spending at approximately $711B, it is undeniable that significant portions of these resources are spent on programs that meet their demise due to poorly developed requirements documentation. Such requirements are poorly written, lack clear traceability and threaten the viability of the programs. The question arises whether these issues are due to poor training during the requirements definition process, lack of sufficient information and expertise available at program initiation, or a decrease in emphasis on establishing quality attributes for requirements. This study evaluates requirement attributes for materiel and non-materiel solution sets, and whether these attributes are the same or different. A case study example is presented identifying a selected set of requirement attributes and, with the aid of expert practitioner knowledge, these attributes are ranked in order of preference for materiel and non-materiel solutions sets.
      Content Type Journal Article
      Pages 233-254
      DOI 10.3233/IKS-140223
      Authors
      Justin M. Rettaliata, School of Engineering and Applied Science, George Washington University, Washington, DC, USA
      Thomas A. Mazzuchi, Operations Research and Engineering Management, School of Engineering and Applied Science, George Washington University, Washington, DC, USA
      Shahram Sarkani, Engineering Management and Systems Engineering, School of Engineering and Applied Science, George Washington University, Washington, DC, USA
      Journal Information, Knowledge, Systems Management
      Online ISSN 1389-1995
      Journal Volume Volume 12
      Journal Issue Volume 12, Number 3-4 / 2013/2015
      PubDate: Thu, 16 Apr 2015 14:38:23 GMT
       
  • Design of multi-disciplinary publications in both traditional and in
           online forms
    • Abstract:
      Content Type Journal Article
      Pages 169-171
      DOI 10.3233/IKS-130219
      Authors
      William B. Rouse
      Andrew P. Sage
      Journal Information, Knowledge, Systems Management
      Online ISSN 1389-1995
      Journal Volume Volume 12
      Journal Issue Volume 12, Number 3-4 / 2013/2015
      PubDate: Thu, 16 Apr 2015 14:38:23 GMT
       
  • Effects of geographic and demographic dispersion on the performance of
           systems engineering teams
    • Abstract: 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.
      Content Type Journal Article
      Pages 149-166
      DOI 10.3233/IKS-130222
      Authors
      Marco Segura, The George Washington University, Washington, DC, USA
      Shahram Sarkani, The George Washington University, Washington, DC, USA
      Thomas Mazzuchi, The George Washington University, Washington, DC, USA
      Journal Information, Knowledge, Systems Management
      Online ISSN 1875-8762
      Print
      ISSN 1389-1995
      Journal Volume Volume 12
      Journal Issue Volume 12, Number 2 / 2013
      PubDate: Fri, 28 Feb 2014 18:20:56 GMT
       
  • Knowledge based data center capacity reduction using sensitivity analysis
           on causal Bayesian belief network
    • Abstract: 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.
      Content Type Journal Article
      Pages 135-148
      DOI 10.3233/IKS-130221
      Authors
      Jayneel Patel, Vanguard Group Inc., Malvern, PA, USA
      Shahram Sarkani, Engineering Management and Systems Engineering of Decision Sciences, George Washington University, Washington, DC, USA
      Thomas A. Mazzuchi, Engineering Management and Systems Engineering, George Washington University, Washington, DC, USA
      Journal Information, Knowledge, Systems Management
      Online ISSN 1875-8762
      Print
      ISSN 1389-1995
      Journal Volume Volume 12
      Journal Issue Volume 12, Number 2 / 2013
      PubDate: Fri, 28 Feb 2014 18:20:33 GMT
       
  • Tacit knowledge mobilization effect due to information structure
    • Abstract: 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.
      Content Type Journal Article
      Pages 115-133
      DOI 10.3233/IKS-130220
      Authors
      Kenneth R. Shelby, The George Washington University, Washington, DC, USA
      Thomas A. Mazzuchi, The George Washington University, Washington, DC, USA
      Shahram Sarkani, The George Washington University, Washington, DC, USA
      Journal Information, Knowledge, Systems Management
      Online ISSN 1875-8762
      Print
      ISSN 1389-1995
      Journal Volume Volume 12
      Journal Issue Volume 12, Number 2 / 2013
      PubDate: Fri, 28 Feb 2014 18:20:30 GMT
       
  • Exploring systems engineering patterns in government acquisition of
           complex information systems
    • Abstract: 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.
      Content Type Journal Article
      Pages 97-114
      DOI 10.3233/IKS-130218
      Authors
      Steven Doskey, Department of Engineering Management and Systems Engineering, The George Washington University, Washington, DC, USA
      Thomas Mazzuchi, Department of Engineering Management and Systems Engineering, The George Washington University, Washington, DC, USA
      Shahram Sarkani, Department of Engineering Management and Systems Engineering, The George Washington University, Washington, DC, USA
      Journal Information, Knowledge, Systems Management
      Online ISSN 1875-8762
      Print
      ISSN 1389-1995
      Journal Volume Volume 12
      Journal Issue Volume 12, Number 2 / 2013
      PubDate: Fri, 28 Feb 2014 18:20:30 GMT
       
 
 
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