Aggregate keyword queries on hierarchy relational databases

作者：Hu Hao^*; He Zhen Ying

来源：Chinese Journal of Computers, 2011, 34(10): 1986-1996.

DOI：10.3724/SP.J.1016.2011.01986

• Empirical evaluations
• Existing systems
• Hierarchical structures
• Hierarchy
• Keyword queries
• Keyword search
• Minimum coverage
• Real data sets
• Relational Database
• Relative information
• Responding time
• SQL query
• Summarization
• Synthetic datasets
• User experience

Keyword search (KWS) has been well accepted as a proven, user-friendly way to retrieve information, and recently applied successfully on relational databases. Today, this technique allows users to find pieces of information without having to compose complicated SQL queries. However, almost all the existing approaches focus on finding joined tuples matching a set of keywords and return the results as joining networks of tuples. In order to feed back the user more relative information, this paper formulates an expanding version of existing system to answer aggregate keyword queries over hierarchical relational databases in which the value of a specific attribute is organized in hierarchical structure. This version retrieves information in the form of MaxLMC(Max-Lowest hierarchy Minimum Coverage aggregate, which consists of tuples more similar and closer to each other) under the conduct of the above hierarchical structure. A Nai¨ve algorithm is proposed to obtain MaxLMC and its enhancement is designed to reduce the system's responding time. Meanwhile, recognized that the number of returned answer might be extremely large in practical, we defined and studied the problem of effective exploration of large sets of aggregating tuples: summarization, a technique which has been applied to help the user find diverse aggregating tuples, thus can be used to improve the user experience. An extensive empirical evaluation using both real data sets and synthetic data sets is reported to verify the effectiveness and the efficiency of our methods.

• 出版日期2011
• 单位复旦大学