32811638
Timing of Software Replacement
Analogous to the replacement of an old machine, such as a car, replacing an aged softwitre may contain its escalating cost of maintenance. Prior research has assumed that increasing maintenance cost is due to the deterioration of the system maintainability. However, cost of maintenance depends also on the number of incoming maintenance requests. While software maintainability is determined by its complexity and development environment, number of maintenance requests is affected by the business environment. This distinction is significant in analyzing economic tradeoffs in software maintenance because replacement improves system maintainability but will not affect the number of maintenance requests. Unlike replacement of hardware, rewriting software takes an extended length of time, Thus, the old software must still be maintained before the new software is ready. We develop an economic model that considers the number of maintenance requests and the rewriting period explicitly. The model is an extension of Gode, Barua, and Mukhopadhyay (1990), which assumes a constant number of maintenance requests and instantaneous replacement. Our model allows us to draw some additional policy implications about software inaintenatice aild replacement. For instance, we show that in certain situations delaying a system replacement can be inore cost effective when the user environment changes more rapidly, contrary to our intuition. Moreover, it is shown that rewriting should begin earlier when the instantane(,us replacement assumption is relaxed.
10011305
Group decision support systems: the cultural factor
Information technologies are socio-technical in that they consist of people (the human or "socio" side), non-human resources (the technical side), and the interaction between these two. They cannot be culture-free because the cultural factor associated with the human component will always be present. An information technology that is appropriate in one national culture is not necessarily appropriate in another. This paper reports an empirical study of group decision support systems (GDSS) conducted in Singapore. It describes the cultural differences between the USA and Singapore and discusses how these differences may affect the application of the existing body of GDSS findings to Singapore. This cross-cultural analysis of GDSS research findings is based on two studies carried out in the USA and Singapore. By adopting a very similar research design and employing the same GDSS software and research task, the authors are able to eliminate most of the contextual variables and the situational factors that might possibly account for the differences in research findings and explain these differences in terms of cultural factors. The key findings of the cross-cultural analysis are: 1. The anonymity feature of a GDSS allowed dominant members in Singaporean GDSS groups to openly express negative opinions about other group members' contributions, a behavior that would otherwise be culturally unacceptable. This, in turn, led to dissatisfaction and lower post-meeting consensus among group members. This phenomenon was not obvious in the American GDSS groups. 2. While structure facilitated expression of agreement or conflict in the American groups, it did not help Singaporean groups. Structure forced group members to be direct and open, a feature that is undesirable in Singaporean culture in which members prefer to express disagreement in an indirect manner so as to preserve harmony. 3. Use of a GDSS led to more even member influence in American groups but resulted in less even member influence in Singaporean groups. The anonymity feature of a GDSS allowed more equal member participation in both cultures. However, it also allowed a dominant member in a Singaporean group to gain influence without direct confrontation with other group members. This resulted in lower equality of influence in the Singaporean GDSS groups.
3251340
A Knowledge Discovery System with Support for Model Selection and Visualization
The process of knowledge discovery in databases consists of several steps that are iterative and interactive. In each application, to go through this process the user has to exploit different algorithms and their settings that usually yield multiple models. Model selection, that is, the selection of appropriate models or algorithms to achieve such models, requires meta-knowledge of algorithm/model and model performance metrics. Therefore, model selection is usually a difficult task for the user. We believe that simplifying the process of model selection for the user is crucial to the success of real-life knowledge discovery activities. As opposed to most related work that aims to automate model selection, in our view model selection is a semiautomatic process, requiring an effective collaboration between the user and the discovery system. For such a collaboration, our solution is to give the user the ability to try various alternatives and to compare competing models quantitatively by performance metrics, and qualitatively by effective visualization. This paper presents our research on model selection and visualization in the development of a knowledge discovery system called D2MS. The paper addresses the motivation of model selection in knowledge discovery and related work, gives an overview of D2MS, and describes its solution to model selection and visualization. It then presents the usefulness of D2MS model selection in two case studies of discovering medical knowledge in hospital data—on meningitis and stomach cancer—using three data mining methods of decision trees, conceptual clustering, and rule induction.
14896427
Multilevel full-chip routing for the X-based architecture
As technology advances into the nanometer territory, the interconnect delay has become a first-order effect on chip performance. To handle this effect, the X-architecture has been proposed for high-performance integrated circuits. The X-architecture presents a new way of orienting a chip's microscopic interconnect wires with the pervasive use of diagonal routes. It can reduce the wire-length and via count, and thus improve performance and routability. Furthermore, the continuous increase of the problem size of IC routing is also a great challenge to existing routing algorithms. In this paper, we present the first multilevel framework for full-chip routing using the X-architecture. To take full advantage of the X-architecture, we explore the optimal routing for three-terminal nets on the X-architecture and develop a general X-Steiner tree algorithm based on the Delaunay triangulation approach for the X-architecture. The multilevel routing framework adopts a two-stage technique of coarsening followed by uncoarsening, with a trapezoid-shaped track assignment embedded between the two stages to assign long, straight diagonal segments for wirelength reduction. Compared with the state-of-the-art multilevel routing for the Manhattan architecture, experimental results show that our approach reduced wirelength by 18.7% and average delay by 8.8% with similar routing completion rates and via counts.
211733
Using Case-Based Reasoning in Interpreting Unsupervised Inductive Learning Results
The objective of this work is to interpret inductive results obtained by the unsupervised learning method OSHAM. We briefly introduce the learning process of OSHAM, that extracts concept hierarchies from unlabelled data, based on a representation combining the classical, prototype and exemplar views on concepts. The interpretive process is considered as an intrinsic part in OSHAM and is carried out by a combination of case-based reasoning with matching approaches in inductive learning. An experimental comparative study of some learning methods in terms of knowledge description and prediction is given.
1324170
A Random Linear Network Coding Approach to Multicast
We present a distributed random linear network coding approach for transmission and compression of information in general multisource multicast networks. Network nodes independently and randomly select linear mappings from inputs onto output links over some field. We show that this achieves capacity with probability exponentially approaching 1 with the code length. We also demonstrate that random linear coding performs compression when necessary in a network, generalizing error exponents for linear Slepian-Wolf coding in a natural way. Benefits of this approach are decentralized operation and robustness to network changes or link failures. We show that this approach can take advantage of redundant network capacity for improved success probability and robustness. We illustrate some potential advantages of random linear network coding over routing in two examples of practical scenarios: distributed network operation and networks with dynamically varying connections. Our derivation of these results also yields a new bound on required field size for centralized network coding on general multicast networks
7929717
Chance discovery and learning minority classes
Chances are viewed in chance discovery as events/situations with significant impact on human decision making. In this research context we are particularly interested in a subset of chances that are unexpected or contradictory with human common knowledge, and the human role that we consider as an essential factor in finding such chances. We first introduce the method LUPC that can learn minority classes from large unbalanced datasets. With its visualization tools as well its exclusive and inclusive constraints, LUPC allows the user to actively participate in and to incorporate background knowledge in the chance discovery process. We then present case studies in which LUPC is used to support the user in discovering significant unexpected chances from stomach cancer and hepatitis databases.