Query Recommendation in Digital Libraries Using OLAP
Query suggestion is well-known to enhance the user's search for relevant documents. In this paper, the authors propose a novel technique that emulates a human skill when searching or exploring digital collections. In general, a user begins searching by providing a naive query and then analyzes the retrieved documents in order to refine the query search. They decided to emulate this behavior by generating alternative queries using OLAP. Such queries are the result of performing multiple data summarizations on digital libraries, and then generating cuboids depending on the correlation between the keywords of the collection and the subset of keywords belonging to the previous search.