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Generic Inverted Index on the GPU

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dc.contributor.author ZHOU, Jingbo en_US
dc.contributor.author GUO, Qi en_US
dc.contributor.author JAGADISH, H. V. en_US
dc.contributor.author LUAN, Wenhao en_US
dc.contributor.author TUNG, Anthony K. H. en_US
dc.contributor.author ZHENG, Yuxin en_US
dc.date.accessioned 2015-12-03T01:08:18Z en_US
dc.date.accessioned 2017-01-23T06:59:41Z
dc.date.available 2015-12-03T01:08:18Z en_US
dc.date.available 2017-01-23T06:59:41Z
dc.date.issued 2015-11-23 en_US
dc.identifier.uri http://hdl.handle.net/1900.100/5332 en_US
dc.description.abstract Data variety, as one of the three Vs of the Big Data, is manifested by a growing number of complex data types such as documents, sequences, trees, graphs and high dimensional vectors. To perform similarity search on these data, existing works mainly choose to create customized indexes for different data types. Due to the diversity of customized indexes, it is hard to devise a general parallelization strategy to speed up the search. In this paper, we propose a generic inverted index on the GPU (called GENIE), which can support similarity search of multiple queries on various data types. GENIE can effectively support the approximate nearest neighbor search in different similarity measures through exerting Locality Sensitive Hashing schemes, as well as similarity search on original data such as short document data and relational data. Extensive experiments on different reallife datasets demonstrate the efficiency and effectiveness of our system. en_US
dc.language.iso en en_US
dc.relation.ispartofseries ;TR11/15 en_US
dc.title Generic Inverted Index on the GPU en_US
dc.type Technical Report en_US

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