Answering why-not questions on top-k augmented spatial keyword queries

Highlights：

• We design an efficient hybrid index called AkC. In AkC, the keywords, numerical attributes, and spatial information of objects are smartly organized. In addition, several lemmas are proposed to prune huge amounts of irrelevant objects for WTkASKQ queries. Based on AkC, an efficient query processing algorithm is also proposed.

• We also discuss how to extend our method to handle Why-not Questions on regional queries, ordinary SkQ queries, and complex scoring queries.

• Extensive experimental results on two real datasets show that the proposed method performs much better than its competitors, and can obtain the best refined query with the lowest modification cost.

摘要

•We introduce numerical attributes in the form of boolean expressions into the why-not questions of SKQ queries, which is closer to the real application scenario and can better answer why-not questions in TkASKQ queries (WTkASKQ for short).•We design an efficient hybrid index called AkC. In AkC, the keywords, numerical attributes, and spatial information of objects are smartly organized. In addition, several lemmas are proposed to prune huge amounts of irrelevant objects for WTkASKQ queries. Based on AkC, an efficient query processing algorithm is also proposed.•We also discuss how to extend our method to handle Why-not Questions on regional queries, ordinary SkQ queries, and complex scoring queries.•Extensive experimental results on two real datasets show that the proposed method performs much better than its competitors, and can obtain the best refined query with the lowest modification cost.