System engineering approach applied to Galileo system

Developing a localization system, with more precise performances than GPS that guarantees Europe autonomy is a complex challenge that ESA and a large number of European economical actors of space industry were decided to meet. To design and manage such a huge system would have been impossible without applying System Engineering best practices, thanks to fundamental activities, multidisciplinary teams and dedicated tools. This paper gives an overview of the System Engineering approach applied to design and develop Galileo, the European Satellite Radio-Navigation System. Galileo system scope is so wide that we have decided to focus on some particular steps of the System Engineering processes that are: Requirements Engineering and Architec-ture. All along this paper, examples are given to illustrate the additional difficulties that have made Systems Engineering more and more complex

A branch and bound algorithm to optimize the representation of tabular decision processe.

Decision situations have various aspects: knowledge acquisition and structuring, knowledge representation, knowledge validation and decision making. It has been recognized in literature that decision tables can play an important role in each of these stages. It is however not necessary to use only one representation formalism during the whole life cycle of an intelligent system. Likewise it is possible that different formats of the same formalism serve different purposes in the development process.Important in this respect is the search for automated and, if possible, optimized transitions between different formats of a formalism and between various formalisms. In this paper a branch and bound algorithm is presented that transforms expanded decision tables, that, because of their explicit enumeration of all decision cases primarily serve an acquisition and verification function, into optimized contracted decision tables, primarily used as target representation of a decision process. An optimal contracted decision table is a contracted decision table with a condition order which results in the minimum number of contracted decision columns.

Unsupervised String Transformation Learning for Entity Consolidation

Data integration has been a long-standing challenge in data management with many applications. A key step in data integration is entity consolidation. It takes a collection of clusters of duplicate records as input and produces a single "golden record" for each cluster, which contains the canonical value for each attribute. Truth discovery and data fusion methods, as well as Master Data Management (MDM) systems, can be used for entity consolidation. However, to achieve better results, the variant values (i.e., values that are logically the same with different formats) in the clusters need to be consolidated before applying these methods. For this purpose, we propose a data-driven method to standardize the variant values based on two observations: (1) the variant values usually can be transformed to the same representation (e.g., "Mary Lee" and "Lee, Mary") and (2) the same transformation often appears repeatedly across different clusters (e.g., transpose the first and last name). Our approach first uses an unsupervised method to generate groups of value pairs that can be transformed in the same way (i.e., they share a transformation). Then the groups are presented to a human for verification and the approved ones are used to standardize the data. In a real-world dataset with 17,497 records, our method achieved 75% recall and 99.5% precision in standardizing variant values by asking a human 100 yes/no questions, which completely outperformed a state of the art data wrangling tool

The significance of distance constraints in peasant farming systems with special reference to sub-Saharan Africa

Analysis of agricultural development potential at village level tends to neglect the factor of relative location, compared with the attention paid to physical resources and economic factors. This paper argues that, in African peasant agriculture, distance takes on increasing significance when farming populations are resettled and agglomerated, there being little intensification in evidence. The impacts of agglomeration and excessive ‘journeys to work’ are identified as affecting the quantity and the quality of agricultural labour inputs, the collection of domestic necessities (especially fuelwood), livestock husbandry, and socio-cultural and welfare conditions.\ud \ud Some simple analyses of time-distance relations, such as the ‘effective working day’, are also described, and a model of peasant decision-making with respect to optimizing farm activity location is proposed as a descriptive-explanatory tool. Response to distance problems is considered as part of rural change; and the particular position of peasant women vis-à-vis distance and transport technology is stressed. Data collection methods and descriptive statements of the spatial relationships within a village, or an agro-ecological zone, are outlined within the framework of rapid rural appraisal. Finally, a number of potential solutions to the agro-economic distance problem are briefly discussed—either as changes in farming systems, or as redistributions of the working population. Changes with the greatest potential are intensification and satellite settlements, though both face difficulties in policy and in implementation

Distribution of municiplaities´ tax incomes/revenues modelling by means of genetic programming

The Goal of this contribution is to demonstrate an illustrative example of genetic programming application in the area of regional administrations financing. The principle of genetic programming is used for the establishing of the analytical function for the calculation of the share each individual municipality has in the national shared taxes revenues in the Czech Republic. This approach is confronted with the existing municipal financing principle issuing from the effective act on Tax Assignment to sub-national Government Level

Modeling good research practices - overview: a report of the ISPOR-SMDM modeling good research practices task force - 1.

Models—mathematical frameworks that facilitate estimation of the consequences of health care decisions—have become essential tools for health technology assessment. Evolution of the methods since the first ISPOR modeling task force reported in 2003 has led to a new task force, jointly convened with the Society for Medical Decision Making, and this series of seven papers presents the updated recommendations for best practices in conceptualizing models; implementing state–transition approaches, discrete event simulations, or dynamic transmission models; dealing with uncertainty; and validating and reporting models transparently. This overview introduces the work of the task force, provides all the recommendations, and discusses some quandaries that require further elucidation. The audience for these papers includes those who build models, stakeholders who utilize their results, and, indeed, anyone concerned with the use of models to support decision making