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With the development of keyword search over relational databases, how to improve the result quality is a popular problem. To solve it, existing work mainly are CN-based and graph-based. The CN-based approaches occupy little memory space and have high level of abstract. However, the defect of these approaches is not
With the increasing amount of text data stored in relational databases, there is a demand for RDBMS to support keyword queries over text data. As a search result is often assembled from multiple relational tables, traditional IR-style ranking and query evaluation methods cannot be applied directly. In this paper, we
In this article we will make a brief survey for keyword search in relational database, including query result definition, ranking functions, result generation and top-k query processing, performance optimization, and search quality evaluation. Two main data models and some algorithms will be discussed, we also discuss
Keyword search is considered to be an effective information discovery method for both structured and semi-structured data. In XML keyword search, query semantics is based on the concept of Lowest Common Ancestor (LCA). However, naive LCA-based semantics leads to exponential computation and result size. In the
Keyword search in XML and relational databases (RDB) has gained popularity as it provides a user-friendly way to explore structured data. Existing works on XML and RDB keyword search only rely on the structures of XML/RDB data and/or schemas, and this causes serious problems of returning incomplete answers
Information Retrieval and web search domain revolves around search and retrieve Methodologies. keyword search has been most popular and easy to used technique. Popular search Engine like Google Bing at core have this methodology in operation. Keywords are terms Extracted from document or single sentence generating
Real databases often consist of hundreds of innerlinked tables, which makes posing a complex join query a really hard task for common users. Join query recommendation is an effective technique to help users formulate better join queries and explore their information demand. In this paper, we propose a novel approach to automatically create join query recommendations based on path frequency. Our approach...
Current techniques for retrieving content and usage information from educational data are based on keywords which including string combinations. This technique raises the limitation in terms of capturing learning conceptualization associated to the results. Aims to reveal this issue, this paper present an approach of
time. Comparing the 'like' query in the standard SQL in relational databases, which can not decide the similarity according users' interests when keywords appear in several different fields, a novel similarity evaluation is given in algorithm of the personalized recommendation. Using the method, a personalized digital
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