Abstract. In many data-driven applications, analysis needs to be performed on scientific information obtained from several sources and generated by computations on distributed resources. Systematic analysis of this scientific information unleashes a growing need for automated data-driven applications that also can keep track of the provenance of the data and processes with little user interaction and overhead. Such data analysis can be facilitated by the recent advancements in scientific workflow systems. A major profit when using scientific workflow systems is the ability to make provenance collection a part of the workflow. Specifically, provenance should include not only the standard data lineage information but also information about the context in which the workflow was used, execution that processed the data, and the evolution of the workflow design. In this paper we describe a complete framework for data and process provenance in the Kepler Scientific Workflow System. We outline the requirements and issues related to data and workflow provenance in a multidisciplinary workflow system and introduce how generic provenance capture can be facilitated in Kepler's actor-oriented workflow environment. We also describe the usage of the stored provenance information for efficient rerun of scientific workflows.

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. *

Abstract. Emerging Grid technologies enable solving scientific problems that involve large datasets and complex analyses. Coordinating distributed Grid resources and computational processes requires adaptable interfaces and tools that provide a modularized and configurable environment for accessing Grid clusters and executing high performance computational tasks. In addition, it is beneficial to make these tools available to the community in a unified framework through a shared cyberinfrastructure, or a portal, so scientists can focus on their scientific work and not be concerned with the implementation of the underlying infrastructure. In this paper we describe a scientific workflow approach to coordinate various resources as data analysis pipelines. We present a three tier architecture for LiDAR interpolation and analysis, a high performance processing of point intensive datasets, utilizing a portal, a scientific workflow engine and Grid technologies. Our proposed solution is available through the GEON portal and, though focused on LiDAR processing, is applicable to other domains as well.