100 resultados para model-based reasoning processes


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Key topics: Since the birth of the Open Source movement in the mid-80's, open source software has become more and more widespread. Amongst others, the Linux operating system, the Apache web server and the Firefox internet explorer have taken substantial market shares to their proprietary competitors. Open source software is governed by particular types of licenses. As proprietary licenses only allow the software's use in exchange for a fee, open source licenses grant users more rights like the free use, free copy, free modification and free distribution of the software, as well as free access to the source code. This new phenomenon has raised many managerial questions: organizational issues related to the system of governance that underlie such open source communities (Raymond, 1999a; Lerner and Tirole, 2002; Lee and Cole 2003; Mockus et al. 2000; Tuomi, 2000; Demil and Lecocq, 2006; O'Mahony and Ferraro, 2007;Fleming and Waguespack, 2007), collaborative innovation issues (Von Hippel, 2003; Von Krogh et al., 2003; Von Hippel and Von Krogh, 2003; Dahlander, 2005; Osterloh, 2007; David, 2008), issues related to the nature as well as the motivations of developers (Lerner and Tirole, 2002; Hertel, 2003; Dahlander and McKelvey, 2005; Jeppesen and Frederiksen, 2006), public policy and innovation issues (Jullien and Zimmermann, 2005; Lee, 2006), technological competitions issues related to standard battles between proprietary and open source software (Bonaccorsi and Rossi, 2003; Bonaccorsi et al. 2004, Economides and Katsamakas, 2005; Chen, 2007), intellectual property rights and licensing issues (Laat 2005; Lerner and Tirole, 2005; Gambardella, 2006; Determann et al., 2007). A major unresolved issue concerns open source business models and revenue capture, given that open source licenses imply no fee for users. On this topic, articles show that a commercial activity based on open source software is possible, as they describe different possible ways of doing business around open source (Raymond, 1999; Dahlander, 2004; Daffara, 2007; Bonaccorsi and Merito, 2007). These studies usually look at open source-based companies. Open source-based companies encompass a wide range of firms with different categories of activities: providers of packaged open source solutions, IT Services&Software Engineering firms and open source software publishers. However, business models implications are different for each of these categories: providers of packaged solutions and IT Services&Software Engineering firms' activities are based on software developed outside their boundaries, whereas commercial software publishers sponsor the development of the open source software. This paper focuses on open source software publishers' business models as this issue is even more crucial for this category of firms which take the risk of investing in the development of the software. Literature at last identifies and depicts only two generic types of business models for open source software publishers: the business models of ''bundling'' (Pal and Madanmohan, 2002; Dahlander 2004) and the dual licensing business models (Välimäki, 2003; Comino and Manenti, 2007). Nevertheless, these business models are not applicable in all circumstances. Methodology: The objectives of this paper are: (1) to explore in which contexts the two generic business models described in literature can be implemented successfully and (2) to depict an additional business model for open source software publishers which can be used in a different context. To do so, this paper draws upon an explorative case study of IdealX, a French open source security software publisher. This case study consists in a series of 3 interviews conducted between February 2005 and April 2006 with the co-founder and the business manager. It aims at depicting the process of IdealX's search for the appropriate business model between its creation in 2000 and 2006. This software publisher has tried both generic types of open source software publishers' business models before designing its own. Consequently, through IdealX's trials and errors, I investigate the conditions under which such generic business models can be effective. Moreover, this study describes the business model finally designed and adopted by IdealX: an additional open source software publisher's business model based on the principle of ''mutualisation'', which is applicable in a different context. Results and implications: Finally, this article contributes to ongoing empirical work within entrepreneurship and strategic management on open source software publishers' business models: it provides the characteristics of three generic business models (the business model of bundling, the dual licensing business model and the business model of mutualisation) as well as conditions under which they can be successfully implemented (regarding the type of product developed and the competencies of the firm). This paper also goes further into the traditional concept of business model used by scholars in the open source related literature. In this article, a business model is not only considered as a way of generating incomes (''revenue model'' (Amit and Zott, 2001)), but rather as the necessary conjunction of value creation and value capture, according to the recent literature about business models (Amit and Zott, 2001; Chresbrough and Rosenblum, 2002; Teece, 2007). Consequently, this paper analyses the business models from these two components' point of view.

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Purpose: The aim was to construct and advise on the use of a cost-per-wear model based on contact lens replacement frequency, to form an equitable basis for cost comparison. ---------- Methods: The annual cost of professional fees, contact lenses and solutions when wearing daily, two-weekly and monthly replacement contact lenses is determined in the context of the Australian market for spherical, toric and multifocal prescription types. This annual cost is divided by the number of times lenses are worn per year, resulting in a ‘cost-per-wear’. The model is presented graphically as the cost-per-wear versus the number of times lenses are worn each week for daily replacement and reusable (two-weekly and monthly replacement) lenses.---------- Results: The cost-per-wear for two-weekly and monthly replacement spherical lenses is almost identical but decreases with increasing frequency of wear. The cost-per-wear of daily replacement spherical lenses is lower than for reusable spherical lenses, when worn from one to four days per week but higher when worn six or seven days per week. The point at which the cost-per-wear is virtually the same for all three spherical lens replacement frequencies (approximately AUD$3.00) is five days of lens wear per week. A similar but upwardly displaced (higher cost) pattern is observed for toric lenses, with the cross-over point occurring between three and four days of wear per week (AUD$4.80). Multifocal lenses have the highest price, with cross-over points for daily versus two-weekly replacement lenses at between four and five days of wear per week (AUD$5.00) and for daily versus monthly replacement lenses at three days per week (AUD$5.50).---------- Conclusions: This cost-per-wear model can be used to assist practitioners and patients in making an informed decision in relation to the cost of contact lens wear as one of many considerations that must be taken into account when deciding on the most suitable lens replacement modality.

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In information retrieval (IR) research, more and more focus has been placed on optimizing a query language model by detecting and estimating the dependencies between the query and the observed terms occurring in the selected relevance feedback documents. In this paper, we propose a novel Aspect Language Modeling framework featuring term association acquisition, document segmentation, query decomposition, and an Aspect Model (AM) for parameter optimization. Through the proposed framework, we advance the theory and practice of applying high-order and context-sensitive term relationships to IR. We first decompose a query into subsets of query terms. Then we segment the relevance feedback documents into chunks using multiple sliding windows. Finally we discover the higher order term associations, that is, the terms in these chunks with high degree of association to the subsets of the query. In this process, we adopt an approach by combining the AM with the Association Rule (AR) mining. In our approach, the AM not only considers the subsets of a query as “hidden” states and estimates their prior distributions, but also evaluates the dependencies between the subsets of a query and the observed terms extracted from the chunks of feedback documents. The AR provides a reasonable initial estimation of the high-order term associations by discovering the associated rules from the document chunks. Experimental results on various TREC collections verify the effectiveness of our approach, which significantly outperforms a baseline language model and two state-of-the-art query language models namely the Relevance Model and the Information Flow model

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Model-based testing (MBT) relies on models of a system under test and/or its environment to derive test cases for the system. This paper discusses the process of MBT and defines a taxonomy that covers the key aspects of MBT approaches. It is intended to help with understanding the characteristics, similarities and differences of those approaches, and with classifying the approach used in a particular MBT tool. To illustrate the taxonomy, a description of how three different examples of MBT tools fit into the taxonomy is provided.

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Identifying the design features that impact construction is essential to developing cost effective and constructible designs. The similarity of building components is a critical design feature that affects method selection, productivity, and ultimately construction cost and schedule performance. However, there is limited understanding of what constitutes similarity in the design of building components and limited computer-based support to identify this feature in a building product model. This paper contributes a feature-based framework for representing and reasoning about component similarity that builds on ontological modelling, model-based reasoning and cluster analysis techniques. It describes the ontology we developed to characterize component similarity in terms of the component attributes, the direction, and the degree of variation. It also describes the generic reasoning process we formalized to identify component similarity in a standard product model based on practitioners' varied preferences. The generic reasoning process evaluates the geometric, topological, and symbolic similarities between components, creates groupings of similar components, and quantifies the degree of similarity. We implemented this reasoning process in a prototype cost estimating application, which creates and maintains cost estimates based on a building product model. Validation studies of the prototype system provide evidence that the framework is general and enables a more accurate and efficient cost estimating process.

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We present a novel approach for developing summary statistics for use in approximate Bayesian computation (ABC) algorithms using indirect infer- ence. We embed this approach within a sequential Monte Carlo algorithm that is completely adaptive. This methodological development was motivated by an application involving data on macroparasite population evolution modelled with a trivariate Markov process. The main objective of the analysis is to compare inferences on the Markov process when considering two di®erent indirect mod- els. The two indirect models are based on a Beta-Binomial model and a three component mixture of Binomials, with the former providing a better ¯t to the observed data.

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Commercial legal expert systems are invariably rule based. Such systems are poor at dealing with open texture and the argumentation inherent in law. To overcome these problems we suggest supplementing rule based legal expert systems with case based reasoning or neural networks. Both case based reasoners and neural networks use cases-but in very different ways. We discuss these differences at length. In particular we examine the role of explanation in existing expert systems methodologies. Because neural networks provide poor explanation facilities, we consider the use of Toulmin argument structures to support explanation (S. Toulmin, 1958). We illustrate our ideas with regard to a number of systems built by the authors

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Fluid–Structure Interaction (FSI) problem is significant in science and engineering, which leads to challenges for computational mechanics. The coupled model of Finite Element and Smoothed Particle Hydrodynamics (FE-SPH) is a robust technique for simulation of FSI problems. However, two important steps of neighbor searching and contact searching in the coupled FE-SPH model are extremely time-consuming. Point-In-Box (PIB) searching algorithm has been developed by Swegle to improve the efficiency of searching. However, it has a shortcoming that efficiency of searching can be significantly affected by the distribution of points (nodes in FEM and particles in SPH). In this paper, in order to improve the efficiency of searching, a novel Striped-PIB (S-PIB) searching algorithm is proposed to overcome the shortcoming of PIB algorithm that caused by points distribution, and the two time-consuming steps of neighbor searching and contact searching are integrated into one searching step. The accuracy and efficiency of the newly developed searching algorithm is studied on by efficiency test and FSI problems. It has been found that the newly developed model can significantly improve the computational efficiency and it is believed to be a powerful tool for the FSI analysis.