TY - GEN
T1 - Individual Query Cardinality Estimation using Multiple Query Combinations on a Search Engine's Corpus
AU - Islam, Fahad
AU - Hassaine, Abdelaali
AU - Jaoua, Ali
AU - Das, Gautam
AU - Zhang, Nan
N1 - Funding Information:
ACKNOWLEDGMENT This contribution was made possible by NPRP grant #07-794-1-145 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/20
Y1 - 2017/10/20
N2 - Most modern search engines feature keyword based search interfaces. These interfaces are usually found on websites belonging to enterprises or governments or sites related to news articles, blogs and social media that contain a large corpus of documents. These collections of documents are not easily indexed by web search engines, and are considered as hidden web databases. These databases provide opportunities for data analysis for many third-parties through their keyword search interfaces. A significant amount of research has already been carried out on analyzing and extracting aggregate information about these hidden document corpora. But most of these research focus on the high level big-picture information of the database. Not enough focus has been done on extracting analytical information which is specific to individual queries. This paper focuses on that analysis gap and takes ideas from other existing research to formulate a query cardinality estimation technique i.e. The count of documents matching a query in the document corpus of a search engine. We experimentally assess the effectiveness of our method by building a search engine on the Reuters-21578 document corpus. For a given keyword the corresponding documents' count is estimated only by sending search queries using the interface.
AB - Most modern search engines feature keyword based search interfaces. These interfaces are usually found on websites belonging to enterprises or governments or sites related to news articles, blogs and social media that contain a large corpus of documents. These collections of documents are not easily indexed by web search engines, and are considered as hidden web databases. These databases provide opportunities for data analysis for many third-parties through their keyword search interfaces. A significant amount of research has already been carried out on analyzing and extracting aggregate information about these hidden document corpora. But most of these research focus on the high level big-picture information of the database. Not enough focus has been done on extracting analytical information which is specific to individual queries. This paper focuses on that analysis gap and takes ideas from other existing research to formulate a query cardinality estimation technique i.e. The count of documents matching a query in the document corpus of a search engine. We experimentally assess the effectiveness of our method by building a search engine on the Reuters-21578 document corpus. For a given keyword the corresponding documents' count is estimated only by sending search queries using the interface.
UR - http://www.scopus.com/inward/record.url?scp=85040020837&partnerID=8YFLogxK
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U2 - 10.1109/COMAPP.2017.8079753
DO - 10.1109/COMAPP.2017.8079753
M3 - Conference contribution
AN - SCOPUS:85040020837
T3 - 2017 International Conference on Computer and Applications, ICCA 2017
SP - 312
EP - 316
BT - 2017 International Conference on Computer and Applications, ICCA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Conference on Computer and Applications, ICCA 2017
Y2 - 6 September 2017 through 7 September 2017
ER -