SUMMARYThe first Provenance Challenge was set up in order to provide a forum for the community to understand the capabilities of different provenance systems and the expressiveness of their provenance representations. To this end, a functional magnetic resonance imaging workflow was defined, which participants had to either simulate or run in order to produce some provenance representation, from which a set of identified queries had to be implemented and executed. Sixteen teams responded to the challenge, and submitted their inputs. In this paper, we present the challenge workflow and queries, and summarize the participants' contributions.
Scientific collaboration is largely focused on the sharing and joint analysis of scientific data and results. Today, a movement is afoot within the scientific computing community to shift "collaboratory" development from traditional tool-centric approaches to more data-centric ones. Yet, to effectively support data sharing means more than providing a common repository for storing and retrieving shared data sets. In order to reasonably comprehend and apply another researcher's data set, the scientist must grasp the various contexts of the data as it relates to the overall data space, applications, experiments, projects, and the scientific community.Under development at the Pacific Northwest National Laboratory, the Biological Sciences Collaboratory (BSC) enables the sharing of biological data and analyses through diverse capabilities such as metadata capture, electronic laboratory notebooks, data organization views, data provenance tracking, analysis notes, task management, and scientific workflow management. Overall, BSC strives to identify and capture the various social and scientific contexts in which data sharing collaborations in biology take place and to provide collaboration tools and capabilities that can effectively support and facilitate these important data sharing contexts.
Many real world networks are considered temporal networks, in which the chronological ordering of the edges has importance to the meaning of the data. Performing temporal subgraph matching on such graphs requires the edges in the subgraphs to match the order of the temporal graph motif we are searching for. Previous methods for solving this rely on the use of static subgraph matching to find potential matches first, before filtering them based on edge order to find the true temporal matches. We present a new algorithm for temporal subgraph isomorphism that performs the subgraph matching directly on the chronologically sorted edges. By restricting our search to only the subgraphs with chronologically correct edges, we can improve the performance of the algorithm significantly. We present experimental timing results to show significant performance improvements on publicly available datasets for a number of different temporal query graph motifs with four or more nodes. We also demonstrate a practical example of how temporal subgraph isomorphism can produce more meaningful results than traditional static subgraph searches.
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