6 research outputs found
Delta bloom filter compression using stochastic learning-based weak estimation
Get PDFSubstantial research has been done, and sill continues, for reducing the bandwidth requirement and for reliable access to the data, stored and transmitted, in a space efficient manner. Bloom filters and their variants have achieved wide spread acceptability in various fields due to their ability to satisfy these requirements.
As this need has increased, especially, for the applications which require heavy use of the transmission bandwidth, distributed computing environment for the databases or the proxy servers, and even the applications which are sensitive to the access to the information with frequent modifications, this thesis proposes a solution in the form of compressed delta Bloom filter.
This thesis proposes delta Bloom filter compression, using stochastic learning-based weak estimation and prediction with partial matching to achieve the goal of lossless compression with high compression gain for reducing the large data transferred frequently
Efficient Query Subscription Processing for Prospective Search Engines
No full textCurrent web search engines are retrospective in that they limit users to searches against already existing pages. Prospective search engines, on the other hand, allow users to upload queries that will be applied to newly discovered pages in the future. Some examples of prospective search are the subscription features in Google News andinRSS-basedblogsearchengines. In this paper, we study the problem of efficiently processing large numbers of keyword query subscriptions against a stream of newly discovered documents, and propose several query processing optimizations for prospective search. Our experimental evaluation shows that these techniques can improve the throughput of a well known algorithm by more than a factor of 20,andallow matching hundreds or thousands of incoming documents per second against millions of subscription queries per node.
