Towards a Scientific Model Management System

Computational models of biological systems aim at accurately simulating in vivo phenomena. They have become a very powerful tool enabling scientists to study complex behavior. A side effect of their success Unfortunately exists and is observed as an increasing difficulty in managing data, metadata and a myriad of programs and tools used and produced during a research task. In this work we aim at supporting scientists during a research endeavour by using Scientific Models as a main guiding element for describing, searching and running computational models, as well as managing the corresponding results. We assume a data-oriented perspective for scientific model representation materialized into a data model with which users describe scientific models and corresponding computational models, and a query language with which a scientist specifies simulation queries. The model is grounded in XML and tightly related to domain ontologies, which provide formal domain descriptions and uniform terminology. Scientists may search for scientific models and run simulations that automatically invoke the underlying programs on provided inputs. The results of a simulation may generate complex data that can be queried in the context of the scientific model. Higher-level models can be specified through views that export a unified representation of underlying scientific models

Um modelo de execução de fluxos de trabalho científico utilizando técnicas de planejamento automático

Resumo: Experimentos científicos produzem grande quantidade de informações que necessitam de processamento para uma posterior análise. Um cientista, que não é da área da computação, nem sempre possui as habilidades para desenvolver seu próprio ambiente de testes. Por isso a utilização de executores de fluxos de trabalhos científicos vêm sido largamente estudada. Uma das principais vantagens de se utilizar um processador de fluxo de trabalho científico é a transparência oferecida para o cientista em relação a maneira com que os experimentos serão organizados, distribuídos e processados. Este trabalho propõe um modelo para criação de um ambiente que seja capaz de processar esses fluxos de trabalho. A ênfase está em um escalonamento inteligente que utiliza técnicas para resolução de problemas de planejamento da área de inteligência artificial

QEF – Supporting Complex Query Applications

This paper describes QEF a query evaluation framework designed to support complex applications on the grid. QEF has been extended to support querying within a number of different applications, including supporting scientific visualization and implementing a web service semantic search engine. Application requests take a form of a workflow in which tasks are represented as algebraic operators and specific data types are enveloped into a common tuple structure. The implemented system is automatically deployed into schedule grid nodes and autonomously manages query evaluation according to grid environment conditions. The generality of our approach has been tested with a number of applications leading to a full grid web service implementation available at http://codims.epfl.c