Search Interfaces for Mathematicians

Access to mathematical knowledge has changed dramatically in recent years, therefore changing mathematical search practices. Our aim with this study is to scrutinize professional mathematicians' search behavior. With this understanding we want to be able to reason why mathematicians use which tool for what search problem in what phase of the search process. To gain these insights we conducted 24 repertory grid interviews with mathematically inclined people (ranging from senior professional mathematicians to non-mathematicians). From the interview data we elicited patterns for the user group "mathematicians" that can be applied when understanding design issues or creating new designs for mathematical search interfaces.Comment: conference article "CICM'14: International Conference on Computer Mathematics 2014", DML-Track: Digital Math Libraries 17 page

Supplier Portfolio Selection and Optimum Volume Allocation: A Knowledge Based Method

Selection of suppliers and allocation of optimum volumes to suppliers is a strategic business decision. This paper presents a decision support method for supplier selection and the optimal allocation of volumes in a supplier portfolio. The requirements for the method were gathered during a case study that was conducted within the logistics unit of Shell Chemicals Europe. The proposed method is based on the classical view by Sprague and Carlson of sequence and interaction of the different phases of decision making in a decision support system and supports Kraljic’s portfolio approach for supplier management. This method aims to help the managers in making decisions on the allocation of volumes to suppliers while simultaneously trying to satisfy conflicting objectives of improvement in benefit and reduction in risk. A mathematical model to struc-ture the problem is presented, knowledge elicited from the managers is used to parameterize the mathemati-cal model and a multi-objective, hierarchical optimization procedure produces ‘trade-off’ outputs. The man-agers can also conduct interactive post optimization ‘what-if’ analysi

Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types

Predicting and Evaluating Software Model Growth in the Automotive Industry

The size of a software artifact influences the software quality and impacts the development process. In industry, when software size exceeds certain thresholds, memory errors accumulate and development tools might not be able to cope anymore, resulting in a lengthy program start up times, failing builds, or memory problems at unpredictable times. Thus, foreseeing critical growth in software modules meets a high demand in industrial practice. Predicting the time when the size grows to the level where maintenance is needed prevents unexpected efforts and helps to spot problematic artifacts before they become critical. Although the amount of prediction approaches in literature is vast, it is unclear how well they fit with prerequisites and expectations from practice. In this paper, we perform an industrial case study at an automotive manufacturer to explore applicability and usability of prediction approaches in practice. In a first step, we collect the most relevant prediction approaches from literature, including both, approaches using statistics and machine learning. Furthermore, we elicit expectations towards predictions from practitioners using a survey and stakeholder workshops. At the same time, we measure software size of 48 software artifacts by mining four years of revision history, resulting in 4,547 data points. In the last step, we assess the applicability of state-of-the-art prediction approaches using the collected data by systematically analyzing how well they fulfill the practitioners' expectations. Our main contribution is a comparison of commonly used prediction approaches in a real world industrial setting while considering stakeholder expectations. We show that the approaches provide significantly different results regarding prediction accuracy and that the statistical approaches fit our data best

Exploring sustainable technical alternatives for Dutch dairy systems by integrating agro-economic modelling and public preferences assessment

Theoretical discussions on the joint consideration of multiple (economic, social and environmental) functions when assessing the sustainability of human actions are increasing. However few studies exist that integrate the social demand for multifunctional agriculture in the evaluation of the sustainability and the global welfare of society. This paper presents a methodology to answer to these questions: Which are the social demands for the multiple functions of agriculture and how can they be quantified?; Which are the feasible technical alternatives of land management to satisfy these demands?; What is the value of the land use alternatives according to social preferences and which alternatives optimally satisfy the social preferences?. The net utility of alternatives for society, and therefore their sustainability, will be measured as the sum of market and non-market net changes compared to the current situation. The proposed methodology combines economic valuation, integrated modelling, stakeholder analysis, and multi-criteria evaluation. In particular, different multi-criteria methods (QFD/ANP) and agro-economic modelling and optimizing tools (Landscape IMAGES) were used. The methodology will be fully illustrated through the case study of dairy farming landscapes in the Northern Friesian Woodlands, The Netherlands. Results show that for the case study it is possible to change current farming techniques and achieve more sustainable farming systems. The more sustainable alternatives are beneficial for farmers, obtaining higher gross margin, and for government, decreasing the current levels of subsidies in agri-environmental programs. Even current environmental restrictions can be slightly relaxed without compromising social demands to the analysed Dutch dairy farming systems.Land-use planning, public preferences, agro-economic models, Environmental Economics and Policy, Livestock Production/Industries,

A knowledge-based decision support system for roofing materials selection

Varieties of materials are available for roof housing construction but selecting the appropriate material is a complex and ponderous task. In order to choose the right material, a multitude of performance criteria would need to be considered. This research aims to develop a knowledge-based decision support system for material selection (KDSMS) to facilitate the selection of optimal material for different sub elements of roof design. This model consists of a knowledge base and databases to store different types of roofing materials with their corresponding performance characteristics. Knowledge is elicited from domain experts and extensive literature review. The proposed system employs the use of TOPSIS (Technique of ranking Preferences by Similarity to the Ideal Solution) multiple criteria decision making method, to solve the materials selection and optimisation problem where initial cost, maintenance cost, thermal performance and sustainability criteria are considered among others. The proposed system is currently being developed for the housing sector in Northern Ireland. This paper presents and explains the framework of the proposed system

A Utility-Theoretic Approach to Privacy in Online Services

Online offerings such as web search, news portals, and e-commerce applications face the challenge of providing high-quality service to a large, heterogeneous user base. Recent efforts have highlighted the potential to improve performance by introducing methods to personalize services based on special knowledge about users and their context. For example, a user's demographics, location, and past search and browsing may be useful in enhancing the results offered in response to web search queries. However, reasonable concerns about privacy by both users, providers, and government agencies acting on behalf of citizens, may limit access by services to such information. We introduce and explore an economics of privacy in personalization, where people can opt to share personal information, in a standing or on-demand manner, in return for expected enhancements in the quality of an online service. We focus on the example of web search and formulate realistic objective functions for search efficacy and privacy. We demonstrate how we can find a provably near-optimal optimization of the utility-privacy tradeoff in an efficient manner. We evaluate our methodology on data drawn from a log of the search activity of volunteer participants. We separately assess users’ preferences about privacy and utility via a large-scale survey, aimed at eliciting preferences about peoples’ willingness to trade the sharing of personal data in returns for gains in search efficiency. We show that a significant level of personalization can be achieved using a relatively small amount of information about users

Driving and sustaining culture change in professional sport performance teams: A grounded theory

Objectives As part of the recent upsurge of work on management and organizational factors in elite sports teams, researchers have focused on the team management-led creation and regulation of high performing cultures. The purpose of this study was to therefore add to a recently developed model of culture change best practice in Olympic sports teams, as led and perceived by incoming performance directors, and conceptualize culture change best practice in professional sports teams, as led and perceived by incoming team managers. Design and method A pragmatic research philosophy and corresponding grounded theory methodology were used to generate a practically-meaningful model of this culture change process from the perspective of UK-based professional team managers. Results Perceived best practice in team manager-led culture change was found to involve a finite phase of initial evaluation, planning, and impact adjoined to the enduring management of a holistic, integrated, and dynamic social system. With the former process acting as the catalyst for successful change, this model revealed that optimal change was felt to primarily rely on the constant acquisition, negotiation, and alignment of internal and external stakeholder perceptions. Conclusions Based on the model's principles, the optimization of professional team culture is defined by a manager's initial actions and never definitively achieved but rather constantly constructed and re-constructed in complex social and power dynamics. Beyond providing a conceptual backdrop for continued research in this area, the model is also a tool on which the practice of professional team managers and their supporting sport psychologists can be based