21,269 research outputs found
NeuroProv: Provenance data visualisation for neuroimaging analyses
© 2019 Elsevier Ltd Visualisation underpins the understanding of scientific data both through exploration and explanation of analysed data. Provenance strengthens the understanding of data by showing the process of how a result has been achieved. With the significant increase in data volumes and algorithm complexity, clinical researchers are struggling with information tracking, analysis reproducibility and the verification of scientific output. In addition, data coming from various heterogeneous sources with varying levels of trust in a collaborative environment adds to the uncertainty of the scientific outputs. This provides the motivation for provenance data capture and visualisation support for analyses. In this paper a system, NeuroProv is presented, to visualise provenance data in order to aid in the process of verification of scientific outputs, comparison of analyses, progression and evolution of results for neuroimaging analyses. The experimental results show the effectiveness of visualising provenance data for neuroimaging analyses
Watchword-Oriented and Time-Stamped Algorithms for Tamper-Proof Cloud Provenance Cognition
Provenance is derivative journal information about the origin and activities
of system data and processes. For a highly dynamic system like the cloud,
provenance can be accurately detected and securely used in cloud digital
forensic investigation activities. This paper proposes watchword oriented
provenance cognition algorithm for the cloud environment. Additionally
time-stamp based buffer verifying algorithm is proposed for securing the access
to the detected cloud provenance. Performance analysis of the novel algorithms
proposed here yields a desirable detection rate of 89.33% and miss rate of
8.66%. The securing algorithm successfully rejects 64% of malicious requests,
yielding a cumulative frequency of 21.43 for MR
Using a Model-driven Approach in Building a Provenance Framework for Tracking Policy-making Processes in Smart Cities
The significance of provenance in various settings has emphasised its
potential in the policy-making process for analytics in Smart Cities. At
present, there exists no framework that can capture the provenance in a
policy-making setting. This research therefore aims at defining a novel
framework, namely, the Policy Cycle Provenance (PCP) Framework, to capture the
provenance of the policy-making process. However, it is not straightforward to
design the provenance framework due to a number of associated policy design
challenges. The design challenges revealed the need for an adaptive system for
tracking policies therefore a model-driven approach has been considered in
designing the PCP framework. Also, suitability of a networking approach is
proposed for designing workflows for tracking the policy-making process.Comment: 15 pages, 5 figures, 2 tables, Proc of the 21st International
Database Engineering & Applications Symposium (IDEAS 2017
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