{"id":157418,"date":"2009-04-01T00:00:00","date_gmt":"2009-04-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/aggregation-aware-top-k-computation-for-full-text-search\/"},"modified":"2018-10-16T21:56:13","modified_gmt":"2018-10-17T04:56:13","slug":"aggregation-aware-top-k-computation-for-full-text-search","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/aggregation-aware-top-k-computation-for-full-text-search\/","title":{"rendered":"Aggregation-Aware Top-k Computation for Full-Text Search"},"content":{"rendered":"<div class=\"asset-content\">\n<p>A typical scenario in information retrieval and web search is to index a given type of items (e.g., web pages, images) and provide search functionality for them. In such a scenario, the basic units of indexing and retrieval are the same. Extensive study has been done for efficient top-k computation in such settings. This paper studies top-k processing for many emerging scenarios: efficiently retrieving top-k items of one type based on the inverted index of another type of items. It would be very inefficient by directly utilizing traditional top-k approaches. Here we follow TA (the Threshold Algorithm) in this scenario. We present an aggregation-aware top-k computation framework with three pruning principles upon the conventional inverted index and a novel inverted index type HybridRank, which employs the item information of both types. Experimental results show that our proposed new index structure and the aggregation-aware top-k strategy provide an efficient solution for this aggregation-aware top-k problem.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A typical scenario in information retrieval and web search is to index a given type of items (e.g., web pages, images) and provide search functionality for them. In such a scenario, the basic units of indexing and retrieval are the same. Extensive study has been done for efficient top-k computation in such settings. 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