An exploration of enterprise technology selection and evaluation

The evaluation-and-selection of enterprise technologies by firms has been said to be largely rational and deterministic. This paper challenges this notion, and puts forward the argument that substantial ceremonial aspects also play an important role. An in-depth, exploratory longitudinal case study of a bank selecting a ubiquitous and pervasive e-mail system was conducted using grounded theory and a hermeneutic [pre] understanding of institutional and decision making theories. Intuition, symbols, rituals, and ceremony all figured prominently in the decision process. However, rather than being in conflict with the rational processes, we found them to be in tension, leading to a more holistic social construction of decision processes. For researchers, this suggests that a focus on process rationality, not outcomes, might lead to a fuller understanding of these critical decisions. For managers, it underscores the importance of understanding the past in order to create the future.     

PASES: An energy-aware design space exploration framework for wireless sensor networks

Energy consumption is one of the most constraining requirements for the development and implementation of wireless sensor networks. Many design aspects affect energy consumption, ranging from the hardware components, operations of the sensors, the communication protocols, the application algorithms, and the application duty cycle. A full design space exploration solution is therefore required to estimate the contribution to energy consumption of all of these factors, and significantly decrease the effort and time spent to choose the right architecture that fits best to a particular application. In this paper we present a flexible and extensible simulation and design space exploration framework called “PASES” for accurate power consumption analysis of wireless sensor networks. PASES performs both performance and energy analysis, including the application, the communication and the platform layers, providing an extensible and customizable environment. The framework assists the designers in the selection of an optimal hardware solution and software implementation for the specific project of interest ranging from standalone to large scale networked systems. Experimental and simulation results demonstrate the framework accuracy and utility.     

An experimental design approach using TOPSIS method for the selection of computer-integrated manufacturing technologies

The selection of Computer-Integrated Manufacturing (CIM) technologies becomes more complex as the decision makers in the manufacturing organization have to assess a wide range of alternatives based on a set of attributes. Although, a lot of Multi-Attribute Decision-Making (MADM) methods are available to deal with selection applications, this paper aims to explore the applicability of an integrated TOPSIS and DoE method to solve different CIM selection problems in real-time industrial applications. Four CIM selection problems, which include selection of (a) an industrial robot, (b) a rapid prototyping process, (c) a CNC machine tool and (d) plant layout design, are considered in this paper. TOPSIS method and Design of Experiment (DoE) are used together to identify critical selection attributes and their interactions of all these cases by fitting a polynomial to the experimental data in a multiple linear regression analysis. This mathematical model development process involves TOPSIS experiments with the model. The regression meta-model greatly reduced the cost, time and amount of the calculation step in application the TOPSIS model. Application results were validated and shown that they provide good approximations to four decision making problem's results in the literature.     

Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting

Weighted voting is the commonly used strategy for combining predictions in pairwise classification. Even though it shows good classification performance in practice, it is often criticized for lacking a sound theoretical justification. In this paper, we study the problem of combining predictions within a formal framework of label ranking and, under some model assumptions, derive a generalized voting strategy in which predictions are properly adapted according to the strengths of the corresponding base classifiers. We call this strategy adaptive voting and show that it is optimal in the sense of yielding a MAP prediction of the class label of a test instance. Moreover, we offer a theoretical justification for weighted voting by showing that it yields a good approximation of the optimal adaptive voting prediction. This result is further corroborated by empirical evidence from experiments with real and synthetic data sets showing that, even though adaptive voting is sometimes able to achieve consistent improvements, weighted voting is in general quite competitive, all the more in cases where the aforementioned model assumptions underlying adaptive voting are not met. In this sense, weighted voting appears to be a more robust aggregation strategy.     

Ranking alternatives in a preferential voting system using fuzzy concepts and data envelopment analysis

In this paper, a new method for aggregating the opinions of experts in a preferential voting system is proposed. The method, which uses fuzzy concept in handling crisp data, is computationally efficient and is able to completely rank the alternatives. Through this method, the number of votes for certain rank position that each alternative receives are first grouped together to form fuzzy numbers. The nearest point to a fuzzy number concept is then used to introduce an artificial ideal alternative. Data envelopment analysis is next used to find the efficiency scores of the alternatives in a pair-wise comparison with the artificial ideal alternative. Alternatives are rank based on these efficiency scores. If the alternatives are not completely ranked, a weight restriction method also based on fuzzy concept is used on the un-discriminated alternatives until they are completely ranked. Two examples are given for illustration of the method.     

Neurophysiological correlates in interface design: An HCI perspective

Objective: The current study examines the changes in functional connectivity that occurs when expert users adapt to an alternate mapping. Background: Research has indicated that interfaces that are similar will result in more errors and may contribute to confusion. Methods: Six volunteers were recruited to determine the neurophysiological changes that occur when users are exposed to an alternate mapping once an internal mental model is formed. Results: The results indicated a change in synchronization after alterations to the button mappings occurred. By altering the layout or order of the task, a difference in the activation pattern was observed. New areas became synchronized while synchronized activity that was present in the developed internal model became desynchronized. Altering the complexity of the task resulted in different patterns of activation recorded on the quantitative electroencephalogram (QEEG). Conclusion: Users often form a schema when learning a device and subsequent interactions are compared to the mental model formed during the initial learning phase. If the newer interface differs significantly a new schema is formed, resulting in a different pattern of synchronization recorded on the QEEG. Application: The use of this knowledge can assist in the development of new interfaces. If the intent is to create a similar interface design, the activation pattern should remain the same indicating that the old schema can be applied. An interface that displays a different cognitive pattern will indicate that a new schema was developed.     

Comparison of customer balking and reneging behavior to queueing theory predictions: An experimental study

In this paper, we study customer decision-making while in a queuing situation. Customers can either join a queue or balk and return at a later time. Customers who join can renege and also return later. Our objective is to determine whether people seem to follow the benchmarks provided by queuing theory or whether psychological costs and perceptions of time invalidate these benchmarks. We use a computerized experiment where participants face explicit financial rewards and penalties for their decisions in a between subjects, fully crossed design with two experimental factors—clock data, and information about expected waiting time, each at two levels, presence and absence. Evaluated against the queuing theory benchmark, decision-making is quite good. Reneging is very rare, as queuing theory requires. Most participants follow a consistent rule for balking. They balk at every line longer than some critical value, as prescribed by queueing theory. But, even when corrected for heterogeneity in time perception, this critical value is greater than the one that minimizes expected waiting time. The large critical value may be due to risk-aversion or participants overestimating the switching cost. The results are supported by a second experiment using different parameters. Information improved decisions for most participants by increasing the precision of waiting time estimates. In addition, information helps participants who underestimate waiting time to correctly leave the line and those who over-estimate to stay. Providing clock time had almost no impact on decision-making.     

Model-driven design space exploration for multi-robot systems in simulation

Multi-robot systems are increasingly deployed to provide services and accomplish missions whose complexity or cost is too high for a single robot to achieve on its own. Although multi-robot systems offer increased reliability via redundancy and enable the execution of more challenging missions, engineering these systems is very complex. This complexity affects not only the architecture modelling of the robotic team but also the modelling and analysis of the collaborative intelligence enabling the team to complete its mission. Existing approaches for the development of multi-robot applications do not provide a systematic mechanism for capturing these aspects and assessing the robustness of multi-robot systems. We address this gap by introducing ATLAS, a novel model-driven approach supporting the systematic design space exploration and robustness analysis of multi-robot systems in simulation. The ATLAS domain-specific language enables modelling the architecture of the robotic team and its mission and facilitates the specification of the team’s intelligence. We evaluate ATLAS and demonstrate its effectiveness in three simulated case studies: a healthcare Turtlebot-based mission and two unmanned underwater vehicle missions developed using the Gazebo/ROS and MOOS-IvP robotic platforms, respectively.

     

Interdigitation for effective design space exploration using iSIGHT

Optimization studies for nonlinear constrained problems (i.e. most complex engineering design problems) have repeatedly shown that (i) no single optimization technique performs best for all design problems, and (ii) in most cases, a mix of techniques perform better than a single technique for a given design problem. iSIGHT^TM is a generic software framework for integration, automation, and optimization of design processes that has been developed on the foundation of interdigitation: the strategy of combining multiple optimization algorithms to exploit their desirable aspects for solving complex problems. With the recent paradigm shift from traditional optimization to design space exploration for evaluating “what-if” scenarios and trade-off studies, iSIGHT has grown from an optimization software system to a complete design exploration environment, providing a suite of design exploration tools including a collection of optimization techniques, design of experiments techniques, approximation methods, and probabilistic quality engineering methods. Likewise, the interdigitation design methodology embodied in iSIGHT has grown to support the interdigitation of all design exploration tools for effective design space exploration. In this paper we present an overview of iSIGHT, past and present, of the interdigitation design methodology and its implementation for multiple design exploration tools, and of an industrial case study for which elements of this methodology have been applied. Received December 30, 2000     

STATS: A framework for microprocessor and system-level design space exploration

As microprocessor-based systems grow in complexity, and the processor-memory speed gap widens further, more emphasis needs to be placed on early design space exploration in order to produce the highest performance systems with minimal schedule impact. We discuss the critical issues associated with architectural evaluation of complex microprocessor-based systems, and present a methodology for the comprehensive and semiautomatic evaluation of processor, cache hierarchy, system interconnect, and main memory architectural and technological alternatives. We discuss the implementation of the methodology, and describe how it can be used in early design space exploration. The unique aspects of the methodology are further illustrated through two architectural investigations performed using the toolset.     

Sensitivity analysis for simulation-based decision making: Application to a hospital emergency service design

An increasing concern of decision makers when dealing with system design is preparation for a wide range of potentially uncertain operating conditions. This paper provides a novel multiobjective approach for simulation-driven decision making that accounts for not only the conventional average system performance indices, but also (i) upper-tail, or extreme, values of these indices, and (ii) measures of their sensitivity to uncertainty in model parameters. The proposed approach is applied to a hospital emergency department service design case study wherein different design alternatives are compared using total time-in-system performance metric under multiple uncertain operating conditions.     

Values in socio-environmental modelling: Persuasion for action or excuse for inaction

Science in general and modelling in particular provide in-depth understanding of environmental processes and clearly demonstrate the present unsustainable use of resources on a global scale. The latest report by the Intergovernmental Panel on Climate Change (IPCC), for instance, shows that climate is changing and with a 95% certainty it is the humans have caused the change. The future climatic conditions are shown to be largely adversely affecting human wellbeing on this planet. Yet we see in numerous examples that societies are very slow in reacting to this rapid depletion of natural resources. What still seems lacking is the translation of scientific reports and the results of analysis and modelling into corrective actions. We argue that one of the reasons for this is the traditional workflow of environmental modelling, which starts with the purpose, the goal formulation, and ends with problem solutions or decision support tools. Instead, modelling, and applied science in general, has to enhance its scope beyond the problem solving stage, to do more on the problem definition and solution implementation phases. Modelling can be also used for identification of societal values and for setting purposes by appropriate communication of the modelling process and results. We believe this new approach for modelling can impact and bring the social values to the forefront of socio-environmental debate and hence turn scientific results into actions sooner rather than later. Instead of being separated from the modelling process, the translation of results should be an intrinsic part of it. We discuss several challenges for recent socio-environmental modelling and conclude with ten propositions that modellers and scientists in general can follow to improve their communication with the society and produce results that can be understood and used to improve awareness and education and spur action.     

Designing for creativity: Considerations for DSS development

Decision support systems (DSS) can be designed to support the creative and intuitive aspects of decision making. Our purpose is to provide a new perspective for the design of DSS by focusing on the important external factors that have been shown to influence creative activity. Design guidelines can then be developed by viewing a DSS as a special environment that incorporates these factors.     

CADSE: communication aware design space exploration for efficient run-time MPSoC management

Real-time multi-media applications are increasingly mapped on modern embedded systems based on multiprocessor systems-on-chip (MPSoC). Tasks of the applications need to be mapped on the MPSoC resources efficiently in order to satisfy their performance constraints. Exploring all the possible mappings, i.e., tasks to resources combinations exhaustively may take days or weeks. Additionally, the exploration is performed at design-time, which cannot handle dynamism in applications and resources’ status. A runtime mapping technique can cater for the dynamism but cannot guarantee for strict timing deadlines due to large computations involved at run-time. Thus, an approach performing feasible compute intensive exploration at design-time and using the explored results at run-time is required. This paper presents a solution in the same direction. Communicationaware design space exploration (CADSE) techniques have been proposed to explore different mapping options to be selected at run-time subject to desired performance and available MPSoC resources. Experiments show that the proposed techniques for exploration are faster over an exhaustive exploration and provides almost the same quality of results.     

An approximate approach for ranking fuzzy numbers based on left and right dominance

This study presents an approximate approach for ranking fuzzy numbers based on the left and right dominance. The proposed approach only requires a few left and right spreads at some -levels of fuzzy numbers to determine the respective dominance of one fuzzy number over the other. The total dominance is then determined by combining the left and right dominance based on a decision maker's optimistic perspectives. Such a dominance is useful in ranking the fuzzy numbers when membership functions cannot be acquired. The approach proposed herein is relatively simple in terms of computational efforts and is efficient when ranking a large quantity of fuzzy numbers. By using a few left and right spreads, two groups of examples demonstrate the accuracy and applicability of the proposed approach.     

Splitting methods for decision tree induction: An exploration of the relative performance of two entropy-based families

Decision tree (DT) induction is among the more popular of the data mining techniques. An important component of DT induction algorithms is the splitting method, with the most commonly used method being based on the Conditional Entropy (CE) family. However, it is well known that there is no single splitting method that will give the best performance for all problem instances. In this paper we explore the relative performance of the Conditional Entropy family and another family that is based on the Class-Attribute Mutual Information (CAMI) measure. Our results suggest that while some datasets are insensitive to the choice of splitting methods, other datasets are very sensitive to the choice of splitting methods. For example, some of the CAMI family methods may be more appropriate than the popular Gain Ratio (GR) method for datasets which have nominal predictor attributes, and are competitive with the GR method for those datasets where all predictor attributes are numeric. Given that it is never known beforehand which splitting method will lead to the best DT for a given dataset, and given the relatively good performance of the CAMI methods, it seems appropriate to suggest that splitting methods from the CAMI family should be included in data mining toolsets. Kweku-Mauta Osei-Bryson is Professor of Information Systems at Virginia Commonwealth University, where he also served as the Coordinator of the Ph.D. program in Information Systems during 2001–2003. Previously he was Professor of Information Systems and Decision Analysis in the School of Business at Howard University, Washington, DC, U.S.A. He has also worked as an Information Systems practitioner in both industry and government. He holds a Ph.D. in Applied Mathematics (Management Science & Information Systems) from the University of Maryland at College Park, a M.S. in Systems Engineering from Howard University, and a B.Sc. in Natural Sciences from the University of the West Indies at Mona. He currently does research in various areas including: Data Mining, Expert Systems, Decision Support Systems, Group Support Systems, Information Systems Outsourcing, Multi-Criteria Decision Analysis. His papers have been published in various journals including: Information & Management, Information Systems Journal, Information Systems Frontiers, Business Process Management Journal, International Journal of Intelligent Systems, IEEE Transactions on Knowledge & Data Engineering, Data & Knowledge Engineering, Information & Software Technology, Decision Support Systems, Information Processing and Management, Computers & Operations Research, European Journal of Operational Research, Journal of the Operational Research Society, Journal of the Association for Information Systems, Journal of Multi-Criteria Decision Analysis, Applications of Management Science. Currently he serves an Associate Editor of the INFORMS Journal on Computing, and is a member of the Editorial Board of the Computers & Operations Research journal. Kendall E. Giles received the BS degree in Electrical Engineering from Virginia Tech in 1991, the MS degree in Electrical Engineering from Purdue University in 1993, the MS degree in Information Systems from Virginia Commonwealth University in 2002, and the MS degree in Computer Science from Johns Hopkins University in 2004. Currently he is a PhD student (ABD) in Computer Science at Johns Hopkins, and is a Research Assistant in the Applied Mathematics and Statistics department. He has over 15 years of work experience in industry, government, and academic institutions. His research interests can be partially summarized by the following keywords: network security, mathematical modeling, pattern classification, and high dimensional data analysis.