A Metadata Search Approach with Branch and Bound Algorithm to Keyword Query in Relational Databases

Saelee, Jarunee; Boonjing, Veera

doi:10.1109/iccit.2009.145

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Of these, only [7,8] considered combining some of the above factors with the IR-style ranking functions. In Mragyati [3], the ranking function can be based on user-specified criteria but the default ranking is based on the number of foreign-key constraints and [4] used this function but they do not handle queries with more than 2 solution paths. In contrast, we compute scores to the resulted tuples which contain all keywords.…”

Section: Related Workmentioning

confidence: 99%

Keyword searching and browsing system over relational databases

Khine

Win

Tun

2011

2011 Sixth International Conference on Digital Information Management

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Keyword search is the dominant information discovery method in Information Retrieval (IR) systems and search engines on the Web. Nowadays, there is an increase amount of data stored in structured databases (Relational Databases). Searching on traditional database management system is done through customized applications which are closely tied to the database schema. Traditional database management systems do not support keyword-based search. Keyword search techniques on the Web cannot directly be applied to databases because the data on the internet and database are in different forms. Therefore, a keyword-based search system for relational database is proposed. The goal is to provide the users, who are not familiar with query language and the schema of the database, to search the relational database with ease. The main contributions of this system compose of indexing of records in database, keyword matching, scoring and filtering of the relevant answer. As long as the database table records can be extended, this system can be easily extendable for further searching records from tables. The experimental results on a real database demonstrate that our method results in improvement in terms of retrieval efficiency as compared to previous strategies.

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Section: Related Workmentioning

confidence: 99%