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Keyword search is a useful tool for exploring large <mathgraphic graphicformat="GIF" fileref="li-ieq1-2302294.gif"/> data sets. Existing techniques either rely on constructing a distance matrix for pruning the search space or building
Keyword search on (semi)structured databases is an increasingly popular research topic. But existing techniques do not deal well with the problems presented by the queries that are ambiguous. Recent approaches for RDF databases try to improve the quality of results by introducing an explicit top-k
Keyword queries enjoy widespread usage as they represent an intuitive way of specifying information needs. Recently, answering keyword queries on graph-structured data has emerged as an important research topic. The prevalent approaches build on dedicated indexing techniques as well as search algorithms aiming at
Keyword-Driven Analytical Processing (KDAP) integrates the simplicity of keyword search with the aggregation power in OLAP (Online-Analytical Processing), which provides an easy-to-use solution to organize the data in a way that a business analyst needs for thinking about the data. For any user query, the system
Complex ad hoc join queries over enterprise databases are commonly used by business data analysts to understand and analyze a variety of enterprise-wide processes. However, effectively formulating such queries is a challenging task for human users, especially over databases that have large, heterogeneous schemas. In this paper, we propose a novel approach to automatically create join query recommendations...
) information content due to occurrence of a property with respect to all the properties in a description base ii) unpredictability of an association due to participation of its properties in multiple domains iii) the extent of match between user specified keywords and properties and iv) the popularity of nodes involved in a
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