Fig. 1. Our novel propagation workflow makes it easy to propagate visual designs to numerous datasets. Reference visualizations are created for data streams, which are associated with several keywords in our ontology. A search and activate process is used to propagate the reference visualisation to other appropriate data streams. (1) Ontology keywords are used to construct a query in our search UI for suitable data stream combinations. (2) Search results consist of ranked data stream combinations that match query parameters, although some results may not be suitable for propagation. (3) A quality assurance step carried out by an expert ensures the visual design is only propagated to suitable data, resulting in new visualizations that are immediately deployed as web pages.

No abstract

No abstract

This paper presents an analysis on performance of logarithmic degree structured P2P (peer-to-peer) overlay networks. P2P network consist of highly transient peers, where peers join and leave the network randomly also known as dynamic environment. It is, therefore difficult to measure the performance of the parameters in real environment. The design of structured overlay networks is fragmented and due to various designs few simulations have been conducted to compare the protocols in dynamic environment. The outcome of the analysis helps the decision in choosing and designing better structured overlay protocol for P2P network. In order to evaluate the routing performance, this work simulates logarithmic-hop overlays -Chord, Pastry and Kademlia. The result shows that among Chord, Pastry and Kademlia protocols, performance of Kademlia is better than Chord and Pastry with 94.2 -99% routing efficiency. Hence, Kademlia architecture is better choice to implement structured P2P network.

How to cite:Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

The material cannot be used for any other purpose without further permission of the publisher and is for private use only.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.

In many large engineering enterprises, searching for files is a high-volume routine activity. Visualization-assisted search facilities can significantly reduce the cost of such activities. In this paper, we introduce the concept of Search Provenance Graph (SPG), and present a technique for mapping out the search results and externalizing the provenance of a search process. This enables users to be aware of collaborative search activities within a project, and to be able to reason about potential missing files (i.e., false negatives) more effectively. We describe multiple ontologies that enable the computation of SPGs while supporting an enterprise search engine. We demonstrate the novelty and application of this technique through an industrial case study, where a large engineering enterprise needs to make a long-term technological plan for large-scale document search, and has found the visualization-assisted approach to be more cost-effective than alternative approaches being studied.

We report on an ongoing collaboration between epidemiological modellers and visualization researchers by documenting and reflecting upon knowledge constructs—a series of ideas, approaches and methods taken from existing visualization research and practice—deployed and developed to support modelling of the COVID-19 pandemic. Structured independent commentary on these efforts is synthesized through iterative reflection to develop: evidence of the effectiveness and value of visualization in this context; open problems upon which the research communities may focus; guidance for future activity of this type and recommendations to safeguard the achievements and promote, advance, secure and prepare for future collaborations of this kind. In describing and comparing a series of related projects that were undertaken in unprecedented conditions, our hope is that this unique report, and its rich interactive supplementary materials, will guide the scientific community in embracing visualization in its observation, analysis and modelling of data as well as in disseminating findings. Equally we hope to encourage the visualization community to engage with impactful science in addressing its emerging data challenges. If we are successful, this showcase of activity may stimulate mutually beneficial engagement between communities with complementary expertise to address problems of significance in epidemiology and beyond. See https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/ . This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.