Enhancing Workflow with a Semantic Description of Scientific Intent

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Towards metadata standards for sharing simulation outputs

This extended abstract outlines a prototype metadata standard for recording outputs of social simulations, to be refined as part of a project funded through the third round of the Digging into Data challenge. This is with a view to gathering community feedback on the proposals

Um framework para composição semântica de workflows científicos

An important element in research on e-Science, which includes research in computational modeling, is the scientific workflow that, in general, is very extensive, comprising many computations, and forward to represent an experimental scientific procedure, generally collaborative. Because it is generally so extensive, a scientific workflow eventually becomes difficult to define. One way to facilitate the process that leads to the representation of a scientific workflow is using tools that make use of semantics to facilitate its composition. In this context, this paper presents a proposal whose main objective is to facilitate the composition of scientific workflows, considering projects in computational modeling, performing semantic search of semantic web services and incorporating these into the definition of the workflows.Um elemento importante para a pesquisa em e-Science, onde se inclui pesquisas em modelagem computacional, é o workflow científico que, em geral, é muito extenso, composto por muitas computações, e voltado para representar um processo experimental científico, geralmente de natureza colaborativa. Por ser, em geral, tão extenso, um workflow científico acaba se tornando de difícil definição. Uma forma de facilitar o processo que leva à representação de um workflow científico é utilizando ferramentas que fazem uso da semântica para facilitar sua composição. Neste contexto, este trabalho apresenta uma proposta que tem como principal objetivo facilitar a composição de workflows científicos, considerando projetos em modelagem computacional, realizando buscas semânticas de serviços Web semânticos e incorporando estes na definição dos workflows

Enhancing workflow with a semantic description of scientific intent

In recent years there has been a proliferation of scientific resources available through the Internet including, for example, datasets and computational modelling services.  Scientists are becoming increasingly dependent upon these resources, which are changing the way they conduct their research activities with increasing emphasis on conducting ‘in silico’ experiments as a way to test hypotheses.  Scientific workflow technologies provide researchers with a flexible problem-solving environment by facilitating the creation and execution of experiments from a pool of available services.  This thesis investigates the use of workflow tools enhanced with semantics to facilitate the design, execution, analysis and interpretation of workflow experiments and exploratory studies.  It is argued that in order to better characterise such experiments we need to go beyond low-level service composition and execution details by capturing higher-level descriptions of the scientific process.  Current workflow technologies do not incorporate any representation of such experimental constraints and goals, which is referred to in this thesis as scientist’s intent.  This thesis proposes an abstract model of scientific intent based on the concept of an Agent in the Open Provenance Model (OPM) specification.  To realise this model a framework based upon a number of Semantic Web technologies has been developed, including the OWL ontology language and the Semantic Web Rule Language (SWRL).  Through the use of social simulation case studies the thesis illustrates the benefits of using this framework in terms of workflow monitoring, workflow provenance and annotation of experimental results.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Enhancing workflow with a semantic description of scientific intent

In the e-Science context, workflow technologies provide a problem-solving environment for researchers by facilitating the creation and execution of experiments from a pool of available services. In this paper we will show how Semantic Web technologies can be used to overcome a limitation of current workflow languages by capturing experimental constraints and goals, which we term scientist's intent. We propose an ontology driven framework for capturing such intent based on workflow metadata combined with SWRL rules. Through the use of an example we will present the key benefits of the proposed framework in terms of enriching workflow output, assisting workflow execution and provenance support. We conclude with a discussion of the issues arising from application of this approach to the domain of social simulation

AVENTIS - An architecture for event data analysis

Time-stamped event data is being generated at an exponential rate from various sources (sensor networks, e-markets etc.), which are stored in event logs and made available to researchers. Despite the data deluge and evolution of a plethora of tools and technologies, science behind exploratory analysis and knowledge discovery lags. There are several reasons behind this. In conducting event data analysis, researchers typically detect a pattern or trend in the data through computation of time-series measures and apply the computed measures to several mathematical models to glean information from data. This is a complex and time-consuming process covering a range of activities from data capture (from a broad array of data sources) to interpretation and dissemination of experimental results forming a pipeline of activities. Further, data-analysis is conducted by domain-users, who are typically non-IT experts but data processing tools and applications are largely developed by application developers. End-users not only lack the critical skills to build a structured analysis pipeline, but are also perplexed by the number of different ways available to derive the necessary information. Consequently, this thesis proposes AVENTIS (Architecture for eVENT Data analysIS), a novel framework to guide the design of analytic solutions to facilitate time-series analysis of event data and is tailored to the needs of domain users. The framework comprises three components; a knowledge base, a model-driven analytic methodology and an accompanying software architecture that provides the necessary technical and operational requirements. Specifically, the research contribution lies in the ability of the framework to enable expressing analysis requirements at a level of abstraction consistent with the domain users and readily make available the information sought without the users having to build the analysis process themselves. Secondly, the framework also facilitates an abstract design space for the domain experts to enable them to build conceptual models of their experiment as a sequence of structured tasks in a technology neutral manner and transparently translate these abstract process models to executable implementations. To evaluate the AVENTIS framework, a prototype based on AVENTIS is implemented and tested with case studies taken from the financial research domain