A direction-aware augmented spatial keyword top- k query (DAT kQ ) returns the top- k objects based on a ranking function that considers spatial distance, textual similarity, query numeric attributes, and query direction. When a user initiates a DAT kQ , some user-desired objects (missing objects) may not appear in the query result set, and then the user wonders why they do not appear, which is called the why-not question. This paper focuses on answering why-not questions on DAT k Qs. We first discuss how to obtain the refined query direction by analyzing the position relationship between missing objects and original query direction in Polar coordinates. Then a DAPC index structure is designed, which can cut down irrelevant search space based on not only conventional distance pruning, keyword pruning, and attribute pruning but also query direction pruning. Particularly, by comparing the position relationship between the query direction and the sector (sector ring) region segmented by the DAPC-based method, the search space that does not meet the query direction is pruned. In addition, we discuss the applicability of our scheme for handling why-not questions on regional spatial keyword queries (SKQ), ordinary direction-aware top- k SKQ queries and complex scoring SKQ queries. Finally, a series of experiments are conducted on two real datasets to show the efficiency of our DAPC-based method.
|頁（從 - 到）||4932-4947|
|期刊||IEEE Transactions on Intelligent Transportation Systems|
|出版狀態||Published - 2023 5月 1|
All Science Journal Classification (ASJC) codes