A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure

Behavior change interventions: the potential of ontologies for advancing science and practice

A central goal of behavioral medicine is the creation of evidence-based interventions for promoting behavior change. Scientific knowledge about behavior change could be more effectively accumulated using "ontologies." In information science, an ontology is a systematic method for articulating a "controlled vocabulary" of agreed-upon terms and their inter-relationships. It involves three core elements: (1) a controlled vocabulary specifying and defining existing classes; (2) specification of the inter-relationships between classes; and (3) codification in a computer-readable format to enable knowledge generation, organization, reuse, integration, and analysis. This paper introduces ontologies, provides a review of current efforts to create ontologies related to behavior change interventions and suggests future work. This paper was written by behavioral medicine and information science experts and was developed in partnership between the Society of Behavioral Medicine's Technology Special Interest Group (SIG) and the Theories and Techniques of Behavior Change Interventions SIG. In recent years significant progress has been made in the foundational work needed to develop ontologies of behavior change. Ontologies of behavior change could facilitate a transformation of behavioral science from a field in which data from different experiments are siloed into one in which data across experiments could be compared and/or integrated. This could facilitate new approaches to hypothesis generation and knowledge discovery in behavioral science

Quality assurance for digital learning object repositories: issues for the metadata creation process

Metadata enables users to find the resources they require, therefore it is an important component of any digital learning object repository. Much work has already been done within the learning technology community to assure metadata quality, focused on the development of metadata standards, specifications and vocabularies and their implementation within repositories. The metadata creation process has thus far been largely overlooked. There has been an assumption that metadata creation will be straightforward and that where machines cannot generate metadata effectively, authors of learning materials will be the most appropriate metadata creators. However, repositories are reporting difficulties in obtaining good quality metadata from their contributors, and it is becoming apparent that the issue of metadata creation warrants attention. This paper surveys the growing body of evidence, including three UK-based case studies, scopes the issues surrounding human-generated metadata creation and identifies questions for further investigation. Collaborative creation of metadata by resource authors and metadata specialists, and the design of tools and processes, are emerging as key areas for deeper research. Research is also needed into how end users will search learning object repositories

Ontology of core data mining entities

In this article, we present OntoDM-core, an ontology of core data mining entities. OntoDM-core defines themost essential datamining entities in a three-layered ontological structure comprising of a specification, an implementation and an application layer. It provides a representational framework for the description of mining structured data, and in addition provides taxonomies of datasets, data mining tasks, generalizations, data mining algorithms and constraints, based on the type of data. OntoDM-core is designed to support a wide range of applications/use cases, such as semantic annotation of data mining algorithms, datasets and results; annotation of QSAR studies in the context of drug discovery investigations; and disambiguation of terms in text mining. The ontology has been thoroughly assessed following the practices in ontology engineering, is fully interoperable with many domain resources and is easy to extend

MIREOT: the Minimum Information to Reference an External Ontology Term

While the Web Ontology Language (OWL) provides a mechanism to import ontologies, this mechanism is not always suitable. First, given the current state of editing tools and the issues they have working with large ontologies, direct OWL imports have sometimes proven impractical for day-to-day development. Second, ontologies chosen for integration may be under active development and not aligned with the chosen design principles. Importing heterogeneous ontologies in their entirety may lead to inconsistencies or unintended inferences. In this paper we propose a set of guidelines for importing required terms from an external resource into a target ontology. We describe the guidelines, their implementation, present some examples of application, and outline future work and extensions

Behind the Scenes with OpenLearn: the Challenges of Researching the Provision of Open Educational Resources

Open educational resources are defined as technology-enabled educational resources that are openly available for consultation, use and adaptation by users for non-commercial purposes (UNESCO, 2002). OpenLearn is one of the largest of such initiatives and is committed to the provision of open educational resources for all. It is being developed by The Open University and is primarily sponsored by the William and Flora Hewlett Foundation. It provides users with over 4 200 hours of higher educational material drawn from Open University courses. Other learning tools such as discussion forums, video conferencing, and knowledge mapping software are also available to the user. In this paper we introduce OpenLearn and outline some of the main research issues surrounding such an initiative. We seek to explore theoretical and practical approaches that can provide suitable tools for analysis. Activity theory is seen as a suitable approach for macro analysis and its use is illustrated in terms of the complexity of large scale research. Activity theory, besides informing research perspectives, can be turned in upon the research process itself allowing us to consider the challenges and context of the research. By using activity theory in this way and illustrating from a range of practical approaches we demonstrate and illustrate a useful research approach

An information retrieval approach to ontology mapping

In this paper, we present a heuristic mapping method and a prototype mapping system that support the process of semi-automatic ontology mapping for the purpose of improving semantic interoperability in heterogeneous systems. The approach is based on the idea of semantic enrichment, i.e., using instance information of the ontology to enrich the original ontology and calculate similarities between concepts in two ontologies. The functional settings for the mapping system are discussed and the evaluation of the prototype implementation of the approach is reported. \ud \u