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BinRank: scaling dynamic authority-based search using materialized subgraphs
Abstract Dynamic authority-based keyword search algDynamic authority-based keyword search algorithms, such as ObjectRank and personalized PageRank, leverage semantic link information to provide high quality, high recall search in databases, and the Web. Conceptually, these algorithms require a query-time PageRank-style iterative computation over the full graph. This computation is too expensive for large graphs, and not feasible at query time. Alternatively, building an index of precomputed results for some or all keywords involves very expensive preprocessing. We introduce BinRank, a system that approximates ObjectRank results by utilizing a hybrid approach inspired by materialized views in traditional query processing. We materialize a number of relatively small subsets of the data graph in such a way that any keyword query can be answered by running ObjectRank on only one of the subgraphs. BinRank generates the subgraphs by partitioning all the terms in the corpus based on their co-occurrence, executing ObjectRank for each partition using the terms to generate a set of random walk starting points, and keeping only those objects that receive non-negligible scores. The intuition is that a subgraph that contains all objects and links relevant to a set of related terms should have all the information needed to rank objects with respect to one of these terms. We demonstrate that BinRank can achieve subsecond query execution time on the English Wikipedia data set, while producing high-quality search results that closely approximate the results of ObjectRank on the original graph. The Wikipedia link graph contains about 108 edges, which is at least two orders of magnitude larger than what prior state of the art dynamic authority-based search systems have been able to demonstrate. Our experimental evaluation investigates the trade-off between query execution time, quality of the results, and storage requirements of BinRank.ults, and storage requirements of BinRank.
Added by wikilit team Added on initial load  +
Collected data time dimension Cross-sectional  +
Conclusion In this paper, we proposed BinRank as a prIn this paper, we proposed BinRank as a practical solution for scalable dynamic authority-based ranking. It is based on partitioning and approximation using a number of materialized subgraphs. We showed that our tunable system offers a nice trade-off between query time and preprocessing cost. We introduce a greedy algorithm that groups co-occurring terms into a number of bins for which we compute materialized subgraphs. Note that the number of bins is much less than the number of terms. The materialized subgraphs are computed offline by using ObjectRank itself. The intuition behind the approach is that a subgraph that contains all objects and links relevant to a set of related terms should have all the information needed to rank objects with respect to one of these terms. Our extensive experimental evaluation confirms this intuition.mental evaluation confirms this intuition.
Data source Experiment responses  + , Wikipedia pages  +
Doi 10.1109/TKDE.2010.85 +
Google scholar url http://scholar.google.com/scholar?ie=UTF-8&q=%22BinRank%3A%2Bscaling%2Bdynamic%2Bauthority-based%2Bsearch%2Busing%2Bmaterialized%2Bsubgraphs%22  +
Has author Heasoo Hwang + , Andrey Balmin + , Berthold Reinwald + , Erik Nijkamp +
Has domain Computer science +
Has topic Query processing + , Ranking and clustering systems +
Issue 8  +
Pages 1176-1190  +
Peer reviewed Yes  +
Publication type Journal article  +
Published in IEEE Transactions on Knowledge and Data Engineering +
Research design Experiment  +
Research questions In this paper,we introduce a BinRank systeIn this paper,we introduce a BinRank system that employs a hybrid approach where query time can be traded off for preprocessing time and storage. BinRank closely approximates ObjectRank scores by running the same ObjectRank algorithm on a small subgraph, instead of the full data graph. The subgraphs are precomputed offline. The precomputation can be parallelized with linear scalability. For example, on the full Wikipedia data set, BinRank can answer any query in less than 1 second, by precomputing about a thousand subgraphs, which takes only about 12 hours on a single CPU.takes only about 12 hours on a single CPU.
Revid 10,685  +
Theories Undetermined
Theory type Design and action  +
Title BinRank: scaling dynamic authority-based search using materialized subgraphs
Unit of analysis N/A  +
Url http://dx.doi.org/10.1109/TKDE.2010.85  +
Volume 22  +
Wikipedia coverage Sample data  +
Wikipedia data extraction Dump  +
Wikipedia language English  +
Wikipedia page type Article  +
Year 2010  +
Creation dateThis property is a special property in this wiki. 15 March 2012 20:24:27  +
Categories Query processing  + , Ranking and clustering systems  + , Computer science  + , Publications with missing comments  + , Publications  +
Modification dateThis property is a special property in this wiki. 30 January 2014 20:21:12  +
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