No abstract

Abstract

No abstract

Skip Tree Graph is a novel, distributed, data structure for peer-to-peer systems that supports exact-match and order-based queries such as range queries efficiently. It is based on skip trees, which are randomised balanced search trees equivalent to skip lists and designed to provide improved concurrency. Skip tree graphs constitute an extension of skip graphs enhancing their performance in both, exact-match and range queries. Moreover, skip tree graph maintains the underlying balanced tree structures using randomisation and local operations, which provides a greater degree of concurrency and scalability.

Agile software development has steadily gained momentum and acceptability as a viable approach to software development. As software development continues to take advantage of the global market, agile methods are also being attempted in geographically distributed settings. In this paper, the authors discuss the usefulness of published research on agile global software development for the practitioner. It is contended that such published work is of minimal value to the practitioner and does not add anything to the guidance available before the existence of current agile methods. A survey of agile GSD related publications, from XP/Agile conferences between 2001 and 2005, is used to support this claim. The paper ends with a number of proposals which aim to improve the usefulness of future agile GSD research and experience.

No abstract

Abstract. The adoption of agile software development methodologies may appear to be a rather straightforward process yielding instantly improved software in less time and increasingly satisfied customers. This paper will show that such a notion is a misunderstanding and can be harmful to small software development organisations. A more reasonable approach involves a careful risk assessment and framework for introducing agile practices to address specific risks. A case study with a small software development organisation is provided to show the assessment in practice and the resulting risk mitigation strategies for process improvement.

A genetic algorithm (GA) model can be helpful in locating leaks and incorrectly closed valves in a water system in addition to calibrating for pipe roughness. This paper provides some practical suggestions to help users collect the right quality and quantity of data, manage the GA runs and interpret the results.Optimization techniques work by adjusting demands, locating leakage hotspots, changing pipe roughness or valve status so that the model matches the field observations for flow and pressure. However, all field data is inaccurate to some extent. The first key to success is that it is necessary to have head loss in the system that is significantly greater than the error in measurement. Otherwise, adjustments to the model are essentially random.In many water distribution systems, the head loss from the boundary nodes to the measurement points is small and, thus, extra head loss must be generated. The easiest way to accomplish this is by opening one or more hydrants such that the flow increases by a known amount. This magnifies the discrepancies between the model and field data and makes the model much more sensitive to changes in parameters. Even with these high flows, it is important to measure the resulting pressure and the elevation of the pressure gage to a high degree of accuracy (and remember that the elevation of the gage is not necessarily the elevation of the model node).