Issue No. 06 - June (2014 vol. 26)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.140
Shiwen Cheng , Univ. of California, Riverside, Riverside, CA, USA
Arash Termehchy , Oregon State Univ., Corvallis, OR, USA
Vagelis Hristidis , Univ. of California, Riverside, Riverside, CA, USA
Keyword queries on databases provide easy access to data, but often suffer from low ranking quality, i.e., low precision and/or recall, as shown in recent benchmarks. It would be useful to identify queries that are likely to have low ranking quality to improve the user satisfaction. For instance, the system may suggest to the user alternative queries for such hard queries. In this paper, we analyze the characteristics of hard queries and propose a novel framework to measure the degree of difficulty for a keyword query over a database, considering both the structure and the content of the database and the query results. We evaluate our query difficulty prediction model against two effectiveness benchmarks for popular keyword search ranking methods. Our empirical results show that our model predicts the hard queries with high accuracy. Further, we present a suite of optimizations to minimize the incurred time overhead.
query processing, keyword search ranking methods, difficult keyword queries, query identification, user satisfaction, alternative queries, database content, query difficulty prediction model,Databases, Motion pictures, Robustness, Noise, Benchmark testing, Noise measurement, Probability distribution,Search process, Relational databases,databases, Query performance, query effectiveness, keyword query, robustness
Shiwen Cheng, Arash Termehchy, Vagelis Hristidis, "Efficient Prediction of Difficult Keyword Queries over Databases", IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. , pp. 1507-1520, June 2014, doi:10.1109/TKDE.2013.140