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This work addresses a novel spatial keyword query called the m-closest keywords (mCK) query. Given a database of spatial objects, each tuple is associated with some descriptive information represented in the form of keywords. The mCK query aims to find the spatially closest tuples which match m user-specified keywords
Keyword search provides a simple yet effective way for the users to query and explore the underlying documents. In the recent years, there have been a great deal of research and development activities on extending keyword search capabilities to handle relational data, the dominant form in which business data are
A previously proposed keyword search paradigm produces, as a query result, a ranked list of object summaries (OSs); each OS summarizes all data held in a relational database about a particular data subject (DS). This paper further investigates the ranking of OSs and their tuples as to facilitate (1) the top-k ranking
Google Scholar is one of the major academic search engines but its ranking algorithm for academic articles is unknown. In a recent study we partly reverse-engineered the algorithm. This paper presents the results of our second study. While the previous study provided a broad overview, the current study focused on analyzing the correlation of an article's citation count and its ranking in Google Scholar...
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...
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