Machine learning techniques to make computers easier to use
Artificial Intelligence - Special issue: artificial intelligence 40 years later
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Fast phrase querying with combined indexes
ACM Transactions on Information Systems (TOIS)
Type less, find more: fast autocompletion search with a succinct index
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
ESTER: efficient search on text, entities, and relations
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficient interactive fuzzy keyword search
Proceedings of the 18th international conference on World wide web
Efficient top-k algorithms for fuzzy search in string collections
Proceedings of the First International Workshop on Keyword Search on Structured Data
Extending autocompletion to tolerate errors
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Output-Sensitive autocompletion search
SPIRE'06 Proceedings of the 13th international conference on String Processing and Information Retrieval
Exploiting available memory and disk for scalable instant overview search
WISE'11 Proceedings of the 12th international conference on Web information system engineering
| Hi-index | 0.00 |
Traditional information systems usually return few answers if a user submits an incomplete query. Users often feel "left in the dark" when they have limited knowledge about the underlying data. They have to use a try-and-see approach to modify queries and find answers. In this paper we propose a novel approach to keyword search which can provide predicted keywords when a user submits a few characters of the underlying data in order. We study research challenges in this framework for large amounts of data. Since each keystroke of the user could invoke a query on the backend, we need efficient algorithms to process each query within milliseconds. We develop an incremental-search algorithm using previously computed and cached results in order to achieve an interactive speed. Some experiments have been conducted to prove the practicality of this new computing paradigm.