The term Linked Data refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions- the Web of Data. In this article we present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. We describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward.

Provenance in the context of workflows, both for the data they derive and for their specification, is an essential component to allow for result reproducibility, sharing, and knowledge re-use in the scientific community. Several workshops have been held on the topic, and it has been the focus of many research projects and prototype systems. This tutorial provides an overview of research issues in provenance for scientific workflows, with a focus on recent literature and technology in this area. It is aimed at a general database research audience and at people who work with scientific data and workflows. We will (1) provide a general overview of scientific workflows, (2) describe research on provenance for scientific workflows and show in detail how provenance is supported in existing systems; (3) discuss emerging applications that are enabled by provenance; and (4) outline open problems and new directions for database-related research.

The openness of the Web and the ease to combine linked data from different sources creates new challenges. Systems that consume linked data must evaluate quality and trustworthiness of the data. A common approach for data quality assessment is the analysis of provenance information. For this reason, this paper discusses provenance of data on the Web and proposes a suitable provenance model. While traditional provenance research usually addresses the creation of data, our provenance model also represents data access, a dimension of provenance that is particularly relevant in the context of Web data. Based on our model we identify options to obtain provenance information and we raise open questions concerning the publication of provenance-related metadata for linked data on the Web.

Scientific workflows play an important role in today’s science. Many disciplines rely on workflow technologies to orchestrate the execution of thousands of computational tasks. Much research to-date focuses on efficient, scalable, and robust workflow execution, especially in distributed environments. However, many challenges remain in the area of data management related to workflow creation, execution, and result management. In this paper we examine some of these issues in the context of the entire workflow lifecycle. 1.

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Abstract. The complexity of scientific workflows for analyzing biological data creates a number of challenges for current workflow and provenance systems. This complexity is due in part to the nature of scientific data (e.g., heterogeneous, nested data collections) and the programming constructs required for automation (e.g., nested workflows, looping, pipeline parallelism). We present an extended version of the Kepler scientific workflow system to address these challenges, tailored for the systematics community. Our system combines novel approaches for representing scientific data, modeling and automating complex analyses, and recording and browsing associated provenance information. 1

Abstract. Data on the Semantic Web currently does not have any standardized or any de-facto agreed upon way to exhibit provenance information, yet provenance is the foundation for any reasonable model of privacy and trust. Yet, currently every RDF triple does not have any coherent way of storing provenance information on the Semantic Web. We present the hypothesis that provenance is by far the most important data needed on the Semantic Web for privacy and trust, and review previous work in database systems on provenance. We put forward the concept that the three main provenances operators (insertion, deletion, and copy) from provenance work in database systems can be used on the Semantic Web. Furthermore, we hypothesize that such information naturally should be stored in or using the name URI of named graphs. We show that such an approach can help solve practical issues of privacy and trust in social networks using a real-world example.

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In this paper we propose a new provenance model which is tailored to a class of workflow-based applications. We motivate the approach with use cases from the astronomy community. We generalize the class of applications the approach is relevant to and propose a pipeline-centric provenance model. Finally, we evaluate the benefits in terms of storage needed by the approach when applied to an astronomy application.

Abstract. The Open Provenance Model (OPM) has recently been proposed as an exchange framework for workflow provenance information. In this paper we show how the NRC data model for workflow repositories can be mapped to the OPM. Our mapping includes such features as complex data flow in an execution of a workflow; different workflows in the repository that call each other; and the tracking of subvalues of complex data structures in the provenance information. Because the NRC dataflow model has been formally specified, also our mapping can be formally specified; in particular, it can be automated. To facilitate this specification, we present an adapted set-theoretic formalization of the basic OPM. 1