EFFICIENT APPROACH FOR DETECTING HARD KEYWORD QUERIES WITH MULTI-LEVEL NOISE GENERATION
P. Marikkannu, Shweta Rani, S. Suganya · 2015
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 the existing work, analyzes 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. However, in this system numbers of issues are there to address. They are, searching quality is lower than the other system and reliability rate of the system is lowest. In order to overcome these drawbacks, to perform the noise generation in three levels includes attribute level, attribute value level and entity set level in the database. This proposed system is well enhancing the reliability rate of the difficult query prediction system. In other words, this work is support these operators for efficient result. From the experimentation result, the proposed system is well effective than the existing system in terms of accuracy rate, quality of result