he LUPOSDATE SPARQL system supports various approaches to manage RDF data and process SPARQL queries: Index, RDF3X, Stream, Jena and Sesame. Jena  and Sesame  refer to third-party SPARQL engines. Index is our in-memory Engine presented in . Stream is our stream-based implementation (see ). RDF3X is a re-implementation of , but is further enhanced with additional optimization strategies. ·
This section contains paper formatting instructions for publishing with IEEE Computer Society conference publishing services. CPS collects from our entire conference author PDFs of their papers to be used in the Conference Publication chosen format & for use in the Computer Society Digital Library and IEEE Xplore. ·
To print Livescribe dot paper documents on sheets of regular printer paper, you will need a color laser printer that is Adobe® PostScript® compatible and can print at 600dpi or higher. The pages you print will work with your Sky wifi smartpen in exactly the same way as any other sheet of Livescribe dot paper. You’ll be able to record audio and link it to what you are writing, tap your notes to play back what you recorded, and then transfer it, store it and play it back in Evernote®. ·
As with many interventions intended to prevent ill health, the effectiveness of parachutes has not been subjected to rigorous evaluation by using randomised controlled trials. Advocates of evidence based medicine have criticised the adoption of interventions evaluated by using only observational data. We think that everyone might benefit if the most radical protagonists of evidence based medicine organised and participated in a double blind, randomised, placebo controlled, crossover trial of the parachute. ·
Scientific Literature Digital Library incorporating autonomous citation indexing, awareness and tracking, citation context, related document retrieval, similar document identification, citation graph analysis, and query-sensitive document summaries. Advantages in terms of availability, coverage, timeliness, and efficiency. Isaac Councill and C. Lee Giles. ·
Updating an index of the web as documents are crawled requires continuously transforming a large repository of existing documents as new documents arrive. This task is one example of a class of data processing tasks that transform a large repository of data via small, independent mutations. These tasks lie in a gap between the capabilities of existing infrastructure. Databases do not meet the storage or throughput requirements of these tasks: Google's indexing system stores tens of petabytes of data and processes billions of updates per day on thousands of machines. MapReduce and other batch-processing systems cannot process small updates individually as they rely on creating large batches for efficiency.
We have built Percolator, a system for incrementally processing updates to a large data set, and deployed it to create the Google web search index. By replacing a batch-based indexing system with an indexing system based on incremental processing using Percolator, we process the same number of documents per day, while reducing the average age of documents in Google search results by 50%. ·