1,614 research outputs found
bdbms -- A Database Management System for Biological Data
Biologists are increasingly using databases for storing and managing their
data. Biological databases typically consist of a mixture of raw data,
metadata, sequences, annotations, and related data obtained from various
sources. Current database technology lacks several functionalities that are
needed by biological databases. In this paper, we introduce bdbms, an
extensible prototype database management system for supporting biological data.
bdbms extends the functionalities of current DBMSs to include: (1) Annotation
and provenance management including storage, indexing, manipulation, and
querying of annotation and provenance as first class objects in bdbms, (2)
Local dependency tracking to track the dependencies and derivations among data
items, (3) Update authorization to support data curation via content-based
authorization, in contrast to identity-based authorization, and (4) New access
methods and their supporting operators that support pattern matching on various
types of compressed biological data types. This paper presents the design of
bdbms along with the techniques proposed to support these functionalities
including an extension to SQL. We also outline some open issues in building
bdbms.Comment: This article is published under a Creative Commons License Agreement
(http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute,
display, and perform the work, make derivative works and make commercial use
of the work, but, you must attribute the work to the author and CIDR 2007.
3rd Biennial Conference on Innovative Data Systems Research (CIDR) January
710, 2007, Asilomar, California, US
Dynamic Provenance for SPARQL Update
While the Semantic Web currently can exhibit provenance information by using
the W3C PROV standards, there is a "missing link" in connecting PROV to storing
and querying for dynamic changes to RDF graphs using SPARQL. Solving this
problem would be required for such clear use-cases as the creation of version
control systems for RDF. While some provenance models and annotation techniques
for storing and querying provenance data originally developed with databases or
workflows in mind transfer readily to RDF and SPARQL, these techniques do not
readily adapt to describing changes in dynamic RDF datasets over time. In this
paper we explore how to adapt the dynamic copy-paste provenance model of
Buneman et al. [2] to RDF datasets that change over time in response to SPARQL
updates, how to represent the resulting provenance records themselves as RDF in
a manner compatible with W3C PROV, and how the provenance information can be
defined by reinterpreting SPARQL updates. The primary contribution of this
paper is a semantic framework that enables the semantics of SPARQL Update to be
used as the basis for a 'cut-and-paste' provenance model in a principled
manner.Comment: Pre-publication version of ISWC 2014 pape
Towards structured sharing of raw and derived neuroimaging data across existing resources
Data sharing efforts increasingly contribute to the acceleration of
scientific discovery. Neuroimaging data is accumulating in distributed
domain-specific databases and there is currently no integrated access mechanism
nor an accepted format for the critically important meta-data that is necessary
for making use of the combined, available neuroimaging data. In this
manuscript, we present work from the Derived Data Working Group, an open-access
group sponsored by the Biomedical Informatics Research Network (BIRN) and the
International Neuroimaging Coordinating Facility (INCF) focused on practical
tools for distributed access to neuroimaging data. The working group develops
models and tools facilitating the structured interchange of neuroimaging
meta-data and is making progress towards a unified set of tools for such data
and meta-data exchange. We report on the key components required for integrated
access to raw and derived neuroimaging data as well as associated meta-data and
provenance across neuroimaging resources. The components include (1) a
structured terminology that provides semantic context to data, (2) a formal
data model for neuroimaging with robust tracking of data provenance, (3) a web
service-based application programming interface (API) that provides a
consistent mechanism to access and query the data model, and (4) a provenance
library that can be used for the extraction of provenance data by image
analysts and imaging software developers. We believe that the framework and set
of tools outlined in this manuscript have great potential for solving many of
the issues the neuroimaging community faces when sharing raw and derived
neuroimaging data across the various existing database systems for the purpose
of accelerating scientific discovery
Using Provenance to support Good Laboratory Practice in Grid Environments
Conducting experiments and documenting results is daily business of
scientists. Good and traceable documentation enables other scientists to
confirm procedures and results for increased credibility. Documentation and
scientific conduct are regulated and termed as "good laboratory practice."
Laboratory notebooks are used to record each step in conducting an experiment
and processing data. Originally, these notebooks were paper based. Due to
computerised research systems, acquired data became more elaborate, thus
increasing the need for electronic notebooks with data storage, computational
features and reliable electronic documentation. As a new approach to this, a
scientific data management system (DataFinder) is enhanced with features for
traceable documentation. Provenance recording is used to meet requirements of
traceability, and this information can later be queried for further analysis.
DataFinder has further important features for scientific documentation: It
employs a heterogeneous and distributed data storage concept. This enables
access to different types of data storage systems (e. g. Grid data
infrastructure, file servers). In this chapter we describe a number of building
blocks that are available or close to finished development. These components
are intended for assembling an electronic laboratory notebook for use in Grid
environments, while retaining maximal flexibility on usage scenarios as well as
maximal compatibility overlap towards each other. Through the usage of such a
system, provenance can successfully be used to trace the scientific workflow of
preparation, execution, evaluation, interpretation and archiving of research
data. The reliability of research results increases and the research process
remains transparent to remote research partners.Comment: Book Chapter for "Data Provenance and Data Management for eScience,"
of Studies in Computational Intelligence series, Springer. 25 pages, 8
figure
Provision of an integrated data analysis platform for computational neuroscience experiments
Ā© Emerald Group Publishing Limited. Purpose ā The purpose of this paper is to provide an integrated analysis base to facilitate computational neuroscience experiments, following a user-led approach to provide access to the integrated neuroscience data and to enable the analyses demanded by the biomedical research community. Design/methodology/approach ā The design and development of the N4U analysis base and related information services addresses the existing research and practical challenges by offering an integrated medical data analysis environment with the necessary building blocks for neuroscientists to optimally exploit neuroscience workflows, large image data sets and algorithms to conduct analyses. Findings ā The provision of an integrated e-science environment of computational neuroimaging can enhance the prospects, speed and utility of the data analysis process for neurodegenerative diseases. Originality/value ā The N4U analysis base enables conducting biomedical data analyses by indexing and interlinking the neuroimaging and clinical study data sets stored on the grid infrastructure, algorithms and scientific workflow definitions along with their associated provenance information
An Architecture for Provenance Systems
This document covers the logical and process architectures of provenance systems. The logical architecture identifies key roles and their interactions, whereas the process architecture discusses distribution and security. A fundamental aspect of our presentation is its technology-independent nature, which makes it reusable: the principles that are exposed in this document may be applied to different technologies
trackr: A Framework for Enhancing Discoverability and Reproducibility of Data Visualizations and Other Artifacts in R
Research is an incremental, iterative process, with new results relying and
building upon previous ones. Scientists need to find, retrieve, understand, and
verify results in order to confidently extend them, even when the results are
their own. We present the trackr framework for organizing, automatically
annotating, discovering, and retrieving results. We identify sources of
automatically extractable metadata for computational results, and we define an
extensible system for organizing, annotating, and searching for results based
on these and other metadata. We present an open-source implementation of these
concepts for plots, computational artifacts, and woven dynamic reports
generated in the R statistical computing language
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