Results Clustering for Keyword Search over Relational Database

Keyword Search over Relational Database (KSORD) has been a hot research topic in the field of the database. The existing prototype systems present the results to user in a linear list. The user has to browse individually. Therefore, it is still very difficult to find the information users really need. To solve this problem, this study is carried out on results clustering for Keyword Search over Relational Database. Learning from the concept of vector in physics, this study proposes a new model of result tree, which is called result-tree characteristic vector. This study also proposes a new clustering strategy based on result-tree characteristic vector. It firstly gets the result-tree characteristic information, and describes the joint tuple tree using vector representation, and then classifies the retrieval results according to the corresponding vector representation. The experimental results verify the feasibility and effectiveness of the clustering strategy in this study and manifest that the method in this study can efficiently help users navigate through and improve the users’ browsing efficiency.

[1]  Ji-Jun Wen.  SEEKER: Keyword-Based Information Retrieval over Relational Databases , 2005 .

[2]  Philip S. Yu,et al.  BLINKS: ranked keyword searches on graphs , 2007, SIGMOD '07.

[3]  Hui Zhang,et al.  An Improved Cluster-Based Cooperative Spectrum Sensing Algorithm , 2013, J. Comput..

[4]  Vagelis Hristidis,et al.  DISCOVER: Keyword Search in Relational Databases , 2002, VLDB.

[5]  S. Sudarshan,et al.  Keyword searching and browsing in databases using BANKS , 2002, Proceedings 18th International Conference on Data Engineering.

[6]  Jun Zhang,et al.  NUITS: a novel user interface for efficient keyword search over databases , 2006, VLDB.

[7]  Jianyong Wang,et al.  Providing built-in keyword search capabilities in RDBMS , 2011, The VLDB Journal.

[8]  Zhao-Hui Peng.  S-CBR: Presenting Results of Keyword Search over Databases Based on Database Schema: S-CBR: Presenting Results of Keyword Search over Databases Based on Database Schema , 2008 .

[9]  Peng Zhao.  S-CBR: Presenting Results of Keyword Search over Databases Based on Database Schema , 2008 .

[10]  Ximing Li,et al.  A Solution for Privacy-Preserving Data Manipulation and Query on NoSQL Database , 2013, J. Comput..

[11]  Xuemin Lin,et al.  SPARK2: Top-k Keyword Query in Relational Databases , 2007, IEEE Transactions on Knowledge and Data Engineering.

[12]  Xiaoming Wang,et al.  A query verification scheme for dynamic outsourced databases , 2012, J. Comput..

[13]  Luis Gravano,et al.  Efficient IR-Style Keyword Search over Relational Databases , 2003, VLDB.

[14]  Surajit Chaudhuri,et al.  DBXplorer: a system for keyword-based search over relational databases , 2002, Proceedings 18th International Conference on Data Engineering.

[15]  Roy Goldman,et al.  Proximity Search in Databases , 1998, VLDB.