63 research outputs found
Updates by Reasoning about States
It has been argued that some sort of control must be introduced in order to perform update operations in deductive databases. Indeed, many approaches rely on a procedural semantics of rule based languages and often perform updates as side-effects. Depending on the evaluation procedure, updates are generally performed in the body (top-down evaluation) or in the head of rules (bottom-up evaluation). We demonstrate that updates can be specified in a purely declarative manner using standard model based semantics without relying on procedural aspects of program evaluation. The key idea is to incorporate states as first-class objects into the language. This is the source of the additional expressiveness needed to define updates. We introduce the update language Statelog+-, discuss various domains of application and outline how to implement computation of the perfect model semantics for Statelog+- programs
Capturing the "Whole Tale" of Computational Research: Reproducibility in Computing Environments
We present an overview of the recently funded "Merging Science and
Cyberinfrastructure Pathways: The Whole Tale" project (NSF award #1541450). Our
approach has two nested goals: 1) deliver an environment that enables
researchers to create a complete narrative of the research process including
exposure of the data-to-publication lifecycle, and 2) systematically and
persistently link research publications to their associated digital scholarly
objects such as the data, code, and workflows. To enable this, Whole Tale will
create an environment where researchers can collaborate on data, workspaces,
and workflows and then publish them for future adoption or modification.
Published data and applications will be consumed either directly by users using
the Whole Tale environment or can be integrated into existing or future domain
Science Gateways
Reasoning over Taxonomic Change: Exploring Alignments for the Perelleschus Use Case
Classifications and phylogenetic inferences of organismal groups change in
light of new insights. Over time these changes can result in an imperfect
tracking of taxonomic perspectives through the re-/use of Code-compliant or
informal names. To mitigate these limitations, we introduce a novel approach
for aligning taxonomies through the interaction of human experts and logic
reasoners. We explore the performance of this approach with the Perelleschus
use case of Franz & Cardona-Duque (2013). The use case includes six taxonomies
published from 1936 to 2013, 54 taxonomic concepts (i.e., circumscriptions of
names individuated according to their respective source publications), and 75
expert-asserted Region Connection Calculus articulations (e.g., congruence,
proper inclusion, overlap, or exclusion). An Open Source reasoning toolkit is
used to analyze 13 paired Perelleschus taxonomy alignments under heterogeneous
constraints and interpretations. The reasoning workflow optimizes the logical
consistency and expressiveness of the input and infers the set of maximally
informative relations among the entailed taxonomic concepts. The latter are
then used to produce merge visualizations that represent all congruent and
non-congruent taxonomic elements among the aligned input trees. In this small
use case with 6-53 input concepts per alignment, the information gained through
the reasoning process is on average one order of magnitude greater than in the
input. The approach offers scalable solutions for tracking provenance among
succeeding taxonomic perspectives that may have differential biases in naming
conventions, phylogenetic resolution, ingroup and outgroup sampling, or
ostensive (member-referencing) versus intensional (property-referencing)
concepts and articulations.Comment: 30 pages, 16 figure
The First Provenance Challenge
The first Provenance Challenge was set up in order to provide a forum for the community to help 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 summarise the participants contributions
Scientific Workflows: Catalyzing the Grid ? Semantic Web Reaction
Scientific workflows allow scientists to automate repetitive data management, analysis, and visualization tasks, and to document the provenance of analysis results. Scientific workflows are composed of interlinked computational components (sometimes called actors), and the datasets that are consumed and produced by those components. Scientific workflow systems are problem-solving environments to design, reuse, share, execute, monitor, and archive scientific workflows. As such, they are the primary tool that end user scientists use when interacting with the emerging e-Science cyberinfrastucture. Scientific workflow systems can often benefit from both, Grid and Semantic Web capabilities. Thus, scientific
workflows can bring together these otherwise loosely connected technologies and "catalyze the reaction" between them
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Scientific Data Management Integrated Software Infrastructure Center (SDM/ISIC): Scientific Process Automation (SPA), FINAL REPORT
This is the final report from SDSC and UC Davis on DE-FC02-01ER25486, Scientific Data Management Integrated Software Infrastructure Center (SDM/ISIC): Scientific Process Automation (SPA)
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