Managing scientific data: From data integration to scientific workflows<xref ref-type="fn" rid="i0-8137-2397-3-397-0-109_N101">*</xref>

Abstract

Scientists are confronted with significant datamanagement problems due to the large volume and high complexity of scientific data. In particular, the latter makes data integration a difficult technical challenge. In this paper, we describe our work on semantic mediation and scientific workflows, and discuss how these technologies address integration challenges in scientific data management. We first give an overview of the main data-integration problems that arise from heterogeneity in the syntax, structure, and semantics of data. Starting from a traditional mediator approach, we show how semantic extensions can facilitate data integration in complex, multipleworlds scenarios, where data sources cover different but related scientific domains. Such scenarios are not amenable to conventional schema-integration approaches. The core idea of semantic mediation is to augment database mediators and query evaluation algorithms with appropriate knowledge-representation techniques to exploit information from shared ontologies. Semantic mediation relies on semantic data registration, which associates existing data with semantic information from an ontology. The Kepler scientific workflow system addresses the problem of synthesizing, from existing tools and applications, reusable workflow components and analytical pipelines to automate scientific analyses. After presenting core features and example workflows in Kepler, we present a framework for adding semantic information to scientific workflows. The resulting system is aware of semantically plausible connections between workflow components as well as between data sources and workflow components. This information can be used by the scientist during workflow design, and by the workflow engineer for creating data transformation steps between semantically compatible but structurally incompatible analytical steps. *

Understanding Scientific Study via Process Modeling

“…Our qualitative process (Silberschatz et al, 2005) that capture and organize knowledge for science and research. For example, some research work organized knowledge bases into some kind of ontology to assist scientific knowledge management (e.g., Kingston, 2002), and some focuses on the data integration and detailed scientific workflows (e.g., Ludäscher et al (2006)) that are unconnected to issues in the philosophy of science. Some even include search facility (e.g., Shadbolt et al, 2004).…”

Four interacting aspects of a geological survey knowledge system

“…The framework might also locate reusable pathways, represented by scientific workflows (Ludäscher et al, 2006), describing a sequence of procedures that can be applied over a long period to ever-changing information stores and software sources. For example, the user might wish to select, retrieve, assess, analyze and visualize the most up-to-date geotechnical and associated information for a specified area using the latest display techniques, simply by invoking an existing workflow.…”

A Semantic Framework for Integrated Modeling and Simulation of Transportation Systems

“…1. Architecture of the integration framework Many application integration issues are addressed extensively using workflow technologies such as in the Kepler (Ludascher et al, 2005) and SODIUM systems, which we believe are the closest to our work. Especially, SODIUM accepts high-level domain specific specification of workflows and automatically translates them to executable workflows, which may invoke web-services, grid services or P2P services.…”