Improving Lucene's geographical data search performance with R-trees
This event took place on Friday 24 August 2007 at 12:00
Evgeny Shadchnev Imperial College London, Department of Computing
Lucene, a state-of-the-art open source information retrieval library, is an efficient solution for indexing and searching textual data. However, some Lucene usage scenarios require handling of geographically augmented data, that is, text documents that contain geographical coordinates (e.g. wikipedia pages about cities). This data is best searched using spacial access methods, such as R-trees, provided that the number of unique documents is large enough to benefit from this approach. An extension to Lucene that improves its speed at searching geographically augmented data is described.
This event took place on Friday 24 August 2007 at 12:00
Lucene, a state-of-the-art open source information retrieval library, is an efficient solution for indexing and searching textual data. However, some Lucene usage scenarios require handling of geographically augmented data, that is, text documents that contain geographical coordinates (e.g. wikipedia pages about cities). This data is best searched using spacial access methods, such as R-trees, provided that the number of unique documents is large enough to benefit from this approach. An extension to Lucene that improves its speed at searching geographically augmented data is described.
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We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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