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The Open Provenance Model is a model of provenance that is designed to meet the following requirements: (1) Allow provenance information to be exchanged between systems, by means of a compatibility layer based on a shared provenance model. (2) Allow developers to build and share tools that operate on such a provenance model. (3) Define provenance in a(More)
Scientists are now faced with an incredible volume of data to analyze. To successfully analyze and validate various hypothesis, it is necessary to pose several queries, correlate disparate data, and create insightful visualizations of both the simulated processes and observed phenomena. Often, insight comes from comparing the results of multiple(More)
The problem of systematically capturing and managing provenance for computational tasks has recently received significant attention because of its relevance to a wide range of domains and applications. The authors give an overview of important concepts related to provenance management, so that potential users can make informed decisions when selecting or(More)
Provenance is well understood in the context of art or digital libaries, where it respectively refers to the documented history of an art object, or the documentation of processes in a digital object's life cycle. Interest for provenance in the " e-science community " [12] is also growing, since provenance is perceived as a crucial component of workflow(More)
XML has become an important medium for data representation, particularly when that data is exchanged over or browsed on the Internet. As the volume of XML data increases, there is a growing interest in storing XML in relational databases so that the well-developed features of these systems (e.g., concurrency control, crash recovery, query processors) can be(More)
In this paper, we study the problem of automating the retrieval of data hidden behind simple search interfaces that accept keyword-based queries. Our goal is to automatically retrieve all available results (or, as many as possible). We propose a new approach to siphon hidden data that automatically generates a small set of representative keywords and builds(More)
The use of relational database management systems (RDBMSs) to store and query XML data has attracted considerable interest with a view to leveraging their powerful and reliable data management services. Due to the mismatch between the relational and XML data models, it is necessary to first shred and load the XML data into relational tables, and then(More)