Many systems exist without us knowing system models that drive system behaviors. Although having complete, accurate system models is critical to understand system behaviors and to perform system engineering, we often do not have pre-defined system models due to the interconnectivity and interdependencies of many engineered system factors and non-engineered system factors (e.g., natural system factors). Energy consumption and supply systems at multi-tiers (equipment, occupants and other building contents, building, building cluster, and community) are such systems [1,2]. We need adequate, accurate energy system models to predict/project energy demand and to closely align energy production with energy demand for energy use and production efficiency. Yet, we do not have adequate, accurate system models of energy consumption and supply because energy consumption and supply at multi-tiers involve not only engineered system factors (e.g., equipment whose energy consumption is well defined from the engineering design of equipment) but also social/behavioral factors (e.g., occupants and their activities whose energy consumption is not pre-defined) and environmental factors (e.g., climate). The neuronal system in our brain drives our behaviors, but we do not have a clear understanding of how a large number of neurons work together to store, process and select information [3]. A network system of interacting genes regulates functions of biological systems, but we do not have adequate and accurate models of the gene regulatory network system [4,5] which will help us understand diseases and develop cures. Current sensing and information technologies have allowed us to collect massive amounts of data about systems in many fields. With available system data, it is highly desirable to perform reverse engineering which is to mine system data in order to discover white-box, structural system models that explicitly explain system behaviors. A white-box, structural system model presents explicit relations of multiple system variables in a structural form (e.g., a network of variables and their relations). Figure 1 gives an example of whitebox, structural system models capturing causal relations of nine system variables, x1, . . . , x9, where xi represents the presence or absence of system factor i by the value of 1 or 0, respectively. Each directed link in Fig. 1 represents a causal relation. For example, x1 → x5 in Fig. 1 represents that the presence of system factor 1 causes the presence of system factor 5, that is, x1 = 1 causes x5 = 1. Suppose that we do not know the structural system model in Fig. 1 but have 10 instances of data observations with the presence/absence of system factors under 10 system conditions, respectively, as shown in Table 1. For example, data in instance 1 in Table 1 are observed when the system is under the condition of system
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.
Information •Knowledge •Systems Managementwas founded by IOS Press in 1999with the two of us as Co-Editors-in-Chief. At the end of 2013, the journal will cease publication, having published 12 volumes in 15 years, including five special issues, four of which constituted whole volumes in themselves. IKSM was an experiment in multidisciplinary, interdisciplinary, and transdisciplinary publishing for an audience ranging from academic researchers to industry and government practitioners. From the perspective of the business of publishing – which primarily depends on the number of paying subscribers – this experiment was not a success. Nevertheless, it is worth relating several of the lessons learned in the process of establishing and operating the journal.
Although having structural system models which determine system behaviours is critical to plan, control and manage many complex systems (e.g. manufacturing and production systems), we often do not have pre-defined structural system models. We need to perform reverse engineering which is to collect and mine observable system data in order to discover structural system models. This paper presents a reverse engineering algorithm that can be used to discover a causal system model which is one kind of structural system model and represents causal relations of system factors. In a causal relation, the presence of one system factor causes the presence of another system factor. The paper also shows the computational complexity of the algorithm. The paper presents the application and performance of the reverse engineering algorithms to data in two application fields.
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.
First let me render some recollections about Andy Sage, a wonderful human being to whom those of us calling ourselves system engineers owe a great deal. My first connection with Andy was in regard to IEEE Transactions. I had been Editor of IEEE Transactions on Human Factors in Electronics, starting around 1966. That publication changed its name in 1970 to IEEE Transactions on Man-Machine Systems. There was also an IEEE Transactions of Systems Science and Cybernetics existing from about 1964. In the early 1970s it was decided to combine the two IEEE Groups and hence their transactions as well, the combined publication being IEEE Transactions on Systems Man and Cybernetics. Andy Sage was chosen Editor of that new Transactions and served in that role for many years, doing an outstanding job. As is no doubt mentioned in other papers in this volume Andy took on a great many other editing and book-writing tasks, in addition to becoming the founding Dean of the School of Engineering at George Mason University. I always held Andy in the greatest esteem in terms of his energy, organizational skills and fairness. The results of his efforts were always of the highest quality.
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 suf- ficient 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.
No single model can capture the complexities of modern enterprises. Machinery and plants are being instrumented generating large amounts of data. Plants and administrative locations are all interconnected through a variety of networks. These networks have also enabled the creation of very diverse and active social networks within organizations. None of the individual components are new. What is new is their diversity and the speed with which they evolve. When multiple models are used, often expressed in different modeling languages, to capture and support different aspects of the enterprise, it often becomes necessary to have these models interoperate. However, each modeling language, while it offers unique insights, also makes specific assumptions about the domain being modeled. For example, social networks [1] describe the interactions (and linkages) among group members but say little about the underlying organization and/or functional structure of the enterprise. Similarly, organization models [3] focus on the structure of the organization and the prescribed interactions based on authorities and responsibilities but say little about the social/behavioral aspects of the members of the organization. Timed Influence net models [6,7] a variant of Bayesian net models, describe cause-and-effect relationships and can be used to assess the decisions made and alternative courses of action that may be executed by management but say little about the decision makers and operators themselves. In order to address the modeling and simulation issues that arise when multiple models are to interoperate, four layers need to be addressed. The lowest layer, the Physical one, i.e., hardware and software, is a platform that enables the concurrent execution of multiple models expressed in different modeling languages and provides the ability to exchange data and also to schedule the events across the different models. The second layer is the Syntactic layer which ascertains that the right data are exchanged among the models. The Physical and Syntactic layers have been addressed through such developments as the C2 Wind Tunnel (C2WT) [2] by Vanderbilt University in collaboration with UC-Berkeley and George Mason University. The C2WT is an integrated, multi-modeling simulation environment. Its framework uses a discrete event model of computation as the common semantic framework for the precise integration of an extensible range of simulation engines, using the Run-Time Infrastructure (RTI) of the High Level Architecture (HLA) platform. Once the technical means to execute concurrently inter-operating models expressed in different modeling languages is achieved, a third problem needs to be addressed at the Semantic layer, where the interoperation of different models is examined to ensure that conflicting assumptions in different modeling languages are recognized and form constraints to the exchange of data. Finally, at the top layer, the Workflow layer, valid combinations of interoperating models are considered to address specific issues. Different issues require different workflows and may also require domain-specific workflow modeling languages [4]. The use of multiple interoperating models is referred to as multi-modeling (or multiformalism modeling) while the analysis of the validity of model interoperation is referred to as metamodeling.
Resource allocation in cloud computing is an optimization problem that determines the allocation of computer and network resources of service providers to requested services of users for meeting user service requirements. A distributed cloud computing environment for IT services requires resource allocation in a decentralized manner through the coordination of service providers and service users. Achieving the optimal solution of service planning through the decentralized service provider-user coordination remains as a challenge. This paper looks into elements of service provider-user coordination first in the formulation of the resource allocation problem in a centralized manner and then in the formulation of the problem in a decentralized manner for various problem cases. By examining differences between the centralized, optimal solutions and the decentralized solutions for those problem cases, the analysis of how the decentralized service provider-user coordination breaks down the optimal solutions is performed. Based on this analysis, strategies of decentralized service provider-user coordination are developed.
Three types of activities may run on computer and network systems at the same time: services, security mechanisms, and attacks. Computer and network systems should sustain legitimate cyber services even under attacks. In this study, system impacts of services, security mechanisms and attacks are investigated and used to develop strategies for system survivability. Experiments are conducted to collect system dynamics data under two services of voice communication and motion detection, two security mechanisms of data encryption and intrusion detection, and five cyber attacks. Statistical analyses are performed on the experimental data to identify system-wide impacts of services, security mechanisms and attacks on system activities, state and performance. The analytical results reveal the system impact characteristics of these services, security mechanisms, and attacks on IO and file operations and bytes, page and cache faults, memory usage, CPU usage, and network traffic. The competition for system resources by all the activities in the system manifests themselves predominantly in their competition for limited CPU time. This competition for limited CPU time can be used as a strategy to ensure system survivability by increasing the activity level of legitimate services to leave less CPU time for attacks and thus suppress the level and system impacts of attacks while sustaining CPU time for legitimate services.
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.
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.
Universal, effective, fair, and consistent methods for evaluating faculty scholarly activities have remained elusive for years. This research utilized the Analytic Hierarchy Process AHP to assess the professoriate's own perceptions of the appropriate assessment of faculty research. The paper also incorporated the Boyer model of scholarship as one of the main criteria for scholarship evaluation. We addressed the "how" and "how much" in the weighting of scholarship output based on a survey of faculty at five Canadian teaching-intensive universities. The surprising lack of weighting value assigned to the Scholarship of Teaching and Learning, and the overall structure of the AHP model present interesting results.
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.
