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Journal of Software (JSW) is a scholarly peer-reviewed international scientific journal focusing on theories, methods, and applications in software. It provide a high profile, leading edge forum for academic researchers, industrial professionals, engineers, consultants, managers, educators and policy makers working in the field to contribute and disseminate innovative new work on software.

When orchestrating data-centric workflows as are commonly found in the sciences, centralised servers can become a bottleneck to the performance of a workflow; output from service invocations are always transferred via a centralised orchestration engine, when they should be passed directly to where they are needed at the next service in the workflow. To address this performance bottleneck, this paper presents a lightweight Web services architecture and concrete API, based on a centralised control flow, distributed data flow model. Our architecture maintains the robustness and simplicity of centralised orchestration, but facilitates choreography by allowing services to exchange data directly with one another, reducing data that needs to be transferred through a centralised server. Furthermore, our architecture is a flexible, non-intrusive solution, as existing service definitions do not have to be altered prior to enactment.

Graph vertex colouring can be defined in such a way where colour assignments are substituted by vertex contractions. We present various hypergraph representations for the graph colouring problem all based on the approach where vertices are merged into groups. In this paper, we explain this approach and identify three reasons that make it useful. First, generally, this approach provides a potential decrease in computational complexity. Second, it provides a uniform and compact way in which algorithms, be it of a complete or a heuristic nature, can be defined and progress toward a colouring. Last, it opens the way to novel applications that extract information useful to guide algorithms during their search. These approaches can be useful in the design of an algorithm.

The wavelet transform has been shown to be a powerful tool for characterising network traffic. However, the resulting decomposition of a wavelet transform typically forms a high-dimension space. This is obviously problematic on compact representations, visualizations, and modeling approaches that are based on these high-dimensional data. In this study, we show how data projection techniques can represent the high- dimensional wavelet decomposition in a low dimensional space to facilitate visual analysis. A low-dimensional representation can significantly reduce the model complexity. Hence, features in the data can be presented with a small number of parameters. We demonstrate these projections in the context of network traffic pattern analysis. The experimental results show that the proposed method can effectively discriminate between different application flows, such as originating from FTP and P2P.

Combinatorial optimisation is a ubiquitous discipline whose usefulness spans vast applications domains. The intrinsic complexity of most combinatorial optimisation problems makes classical methods unaffordable in many cases. To acquire practical solutions to these problems requires the use of metaheuristic approaches that trade completeness for pragmatic effectiveness. Such approaches are able to provide optimal or quasi-optimal solutions to a plethora of difficult combinatorial optimisation problems. The application of metaheuristics to combinatorial optimisation is an active field in which new theoretical developments, new algorithmic models, and new application areas are continuously emerging. This volume presents recent advances in the area of metaheuristic combinatorial optimisation, with a special focus on evolutionary computation methods. Moreover, it addresses local search methods and hybrid approaches. In this sense, the book includes cutting-edge theoretical, methodological, algorithmic and applied developments in the field, from respected experts and with a sound perspective.

Efficiently executing large-scale, data-intensive workflows such as Montage must take into account the volume and pattern of communication. When orchestrating data-centric workflows, centralised servers common to standard workflow systems can become a bottleneck to performance. However, standards-based workflow systems that rely on centralisation, e.g., Web service based frameworks, have many other benefits such as a wide user base and sustained support. This paper presents and evaluates a light-weight hybrid architecture which maintains the robustness and simplicity of centralised orchestration, but facilitates choreography by allowing services to exchange data directly with one another. Furthermore our architecture is standards compliment, flexible and is a non-disruptive solution; service definitions do not have to be altered prior to enactment. Our architecture could be realised within any existing workflow framework, in this paper, we focus on a Web service based framework. Taking inspiration from Montage, a number of common workflow patterns (sequence, fan-in and fan-out), input to output data size relationships and network configurations are identified and evaluated. The performance analysis concludes that a substantial reduction in communication overhead results in a 2-4 fold performance benefit across all patterns. An end-to-end pattern through the Montage workflow results in an 8 fold performance benefit and demonstrates how the advantage of using our hybrid architecture increases as the complexity of a workflow grows.

The Edinburgh Mouse Atlas aims to capture in-situ gene expression patterns in a common spatial framework. In this study, we construct a grammar to define spatial regions by combinations of these patterns. Combinations are formed by applying operators to curated gene expression patterns from the atlas, thereby resembling gene interactions in a spatial context. The space of combinations is searched using an evolutionary algorithm with the objective of finding the best match to a given target pattern. We evaluate the method by testing its robustness and the statistical significance of the results it finds.

Workflow technologies are emerging as the dominant approach to coordinate groups of distributed services. However with a space filled with competing specifications, standards and frameworks from multiple domains, choosing the right tool for the job is not always a straightforward task. Researchers are often unaware of the range of technology that already exists and focus on implementing yet another proprietary workflow system. As an antidote to this common problem, this paper presents a concise survey of existing workflow technology from the business and scientific domain and makes a number of key suggestions towards the future development of scientific workflow systems.

Evolutionary computation involves the study of problem-solving and optimization techniques inspired by principles of evolution and genetics. As any other scientific field, its success relies on the continuity provided by new researchers joining the field to help it progress. One of the most important sources for new researchers is the next generation of PhD students that are actively studying a topic relevant to this field. It is from this main observation the idea arose of providing a platform exclusively for PhD students.

Metaheuristics have shown to be effective for difficult combinatorial optimization problems appearing in various industrial, economical, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, tabu search, simulated annealing, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, ant colony optimization and estimation of distribution algorithms. Problems solved successfully include scheduling, timetabling, network design, transportation and distribution, vehicle routing, the travelling salesman problem, packing and cutting, satisfiability and general mixed integer programming. EvoCOP began in 2001 and has been held annually since then. It is the first event specifically dedicated to the application of evolutionary computation and related methods to combinatorial optimization problems. Originally held as a workshop, EvoCOP became a conference in 2004. The events gave researchers an excellent opportunity to present their latest research and to discuss current developments and applications. Following the general trend of hybrid metaheuristics and diminishing boundaries between the different classes of metaheuristics, EvoCOP has broadened its scope over the last years and invited submissions on any kind of metaheuristic for combinatorial optimization.

Graph vertex colouring can be defined in such a way where colour assignments are substituted by vertex contractions. We present various hyper-graph representations for the graph colouring problem all based on the approach where vertices are merged into groups. In this paper, we show this provides a uniform and compact way to define algorithms, both of a complete or a heuristic nature. Moreover, the representation provides information useful to guide algorithms during their search. In this paper we focus on the quality of solutions obtained by graph colouring heuristics that make use of higher order properties derived during the search. An evolutionary algorithm is used to search permutations of possible merge orderings.

You have stumbled upon my hyperhome, welcome! Me being a scientist, this page is focused on my work. I currently lead a research group at the National e-Science Centre of the University of Edinburgh. If you are not into science, you might enjoy some of my photographs or you could take a look at some of my current or past projects.