more template updates for i18n
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<#assign shortVisualizationURLRoot ="/vis">
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<#assign refreshCacheURL = "${urls.base}${shortVisualizationURLRoot}/refresh-cache">
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<h2>Visualization Tools</h2>
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<h2>${i18n().visualization_tools}</h2>
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<a href="${refreshCacheURL}">Refresh Cached Models for Visualization</a>
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<a href="${refreshCacheURL}">${i18n().refresh_cached_vis_model}</a>
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<section class="visualizationTools">
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<h3>Why is it needed?</h3>
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<p>Large-scale visualizations like the Temporal Graph or the Map of Science involve calculating total counts of publications or
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of grants for some entity. Since this also means checking through all of its sub-entities, the underlying queries can be both
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memory-intensive and time-consuming. For a faster user experience, we wish to save the results of these queries for later re-use.</p>
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<h3>${i18n().why_needed}</h3>
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<p>${i18n().vis_tools_note_one}</p>
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<h3>What's involved in the caching process?</h3>
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<p>To this end we have devised a caching solution which will retain information about the hierarchy of organizations -- namely,
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which publications are attributed to which organizations -- by storing the RDF model.</p>
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<h3>${i18n().vis_caching_process}</h3>
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<p>${i18n().vis_tools_note_two}</p>
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<p>We're currently caching these models in memory. The cache is built (only once) on the first user request after a server restart.
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Because of this, the same model will be served until the next restart. This means that the data in these models may become stale
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depending upon when it was last created. This works well enough for now. In future releases we will improve this solution so that
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models are stored on disk and periodically updated.</p>
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<p>${i18n().vis_tools_note_three}</p>
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<p>The models are refreshed each time the server restarts. Since this is not generally practical on production instances,
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administrators can instead use the "refresh cache" link above to do this without a restart.</p>
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<p>${i18n().vis_tools_note_four}</p>
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</section>
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