Parameswaran, Aditya and Teh, Ming Han and Garcia-Molina, Hector and Widom, Jennifer DataSift: A Crowd-Powered Search Toolkit. Technical Report. Stanford InfoLab.
Traditional search engines are unable to support a large number of potential queries issued by users, for instance, queries containing non-textual fragments such as images or videos, queries that are very long, ambiguous, or those that require subjective judgment, or semantically-rich queries over non-textual corpora. We demonstrate DataSift, a crowd-powered search toolkit that can be instrumented over any corpus supporting a keyword search API, and supports efficient and accurate querying for a rich general class of queries, including those described previously. Our demonstration will allow conference attendees to issue live queries for image, video, and product search, as well as “play back” the results of a wide variety of prior queries issued on DataSift. Attendees will also be able to perform a side-by-side comparison between DataSift and traditional retrieval schemes.
|Item Type:||Techreport (Technical Report)|
|Deposited By:||Aditya Parameswaran|
|Deposited On:||02 Mar 2014 13:21|
|Last Modified:||02 Mar 2014 13:21|
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