Today, many scientific disciplines heavily rely on computer systems for in-silico experimentation or data management and analysis. The employed computer hard-and software is heterogeneous and complies to different standards, interfaces and protocols for interoperation. Grid middleware systems like UNICORE 6 try to hide some of the complexity of the underlying systems by offering high-level, uniform interfaces for executing computational jobs or storing, moving, and searching through data. Via UNICORE 6 computer resources can be accessed securely with different software clients, e. g. the UNICORE Commandline Client (UCC) or the graphical UNICORE Rich Client (URC) which is based on Eclipse. In this paper, we describe the design and features of the URC, and highlight its role as a flexible and extensible Grid client framework using the QSAR field as an example.
The UNICORE Grid system provides a seamless, secure and intuitive access to distributed Grid resources. In recent years, UNICORE 5 is used as a well-tested Grid middleware system in production Grids (e.g. DEISA, D-Grid) and at many supercomputer centers world-wide.
Beyond this production usage, UNICORE serves as a solid basis in many European and International research projects and business scenarios from T-Systems, Philips Research, Intel, Fujitsu and others. To foster ongoing developments in multiple projects, UNICORE is open source under BSD license at SourceForge. More recently, the new Web services-based UNICORE 6 has become available that is based on open standards such as the Web Services Addressing (WS-A) and the Web Services Resource Framework (WS-RF) and thus conforms to the Open Grid Services Architecture (OGSA) of the Open Grid Forum (OGF). In this paper we present the evolution from production UNICORE 5 to the open standards-based UNICORE 6 and its various Web servicesbased interfaces. It describes the interface integration ofemerging open standards such as OGSA-BES and OGSA-RUS and thus provides an overview of UNICORE 6.
Abstract. This article presents a genetic learning algorithm to derive discrete patterns that can be used for classification and retrieval of 3D motion capture data. Based on boolean motion features, the idea is to learn motion class patterns in an evolutionary process with the objective to discriminate a given set of positive from a given set of negative training motions. Here, the fitness of a pattern is measured with respect to precision and recall in a retrieval scenario, where the pattern is used as a motion query. Our experiments show that motion class patterns can automate query specification without loss of retrieval quality.
In the last decade, life science applications have become more and more integrated into e-Science environments, hence they are typically very demanding, both in terms of computational capabilities and data capacities. Especially the access to life science applications, embedded in such environments via Grid clients still constitutes a major hurdle for scientists that do not have an IT background. Life science applications often comprise a whole set of small programs instead of a single executable. Many of the graphical Grid clients are not perfectly suited for these types of applications, as they often assume that Grid jobs will run a single executable instead of a set of chained executions (i.e. sequences). This means that in order to execute a sequence of multiple programs on a single Grid resource, piping data from one program to the next, the user would have to run a hand-written shell script. Otherwise each program is independently scheduled as a Grid job, which causes unnecessary file transfers between the jobs, even if they are scheduled on the same resource.We present a generic solution to this problem and provide a reference implementation, which seamlessly integrates with the Grid middleware UNICORE. Our approach focuses on a comfortable user interface for the creation of such program sequences, validated in UNICORE-driven HPC-based Grids. Thus, we applied our approach in order to provide support for the usage of the AMBER package (a widely-used collection of programs for molecular dynamics simulations) within Grid workflows. We finally provide a scientific use case of our approach leveraging the interoperability of two different scientific infrastructures that represents an instance of the infrastructure interoperability reference model.
In the last decade, computational biological applications have become very well integrated into e-Science infrastructures. These distributed resources, containing computing and data sources, provide a reasonable environment for computing and data demanding applications. The access to e-Science infrastructures is mostly enstablished via Grids, where Grid clients support scientists using different types of resources. This paper extends an instance of the infrastructure interoperability reference model to remove the lack by adding centralized access to distributed computational and database resources via a graphical Grid client.
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