Why Questions on Time-Aware Spatial Keyword Queries in Traffic Networks

Time-aware spatial keyword queries in traffic networks ( $TSKQT$ ) aim to retrieve top- $k$ result objects based on the ranking score considering spatial proximity, textual relevance, and temporal similarity simultaneously. However, due to inappropriate query parameter settings, the query result set may contain one or more objects not expected by the user. To improve the usability of query results, we propose and study the why question on time-aware spatial keyword queries in the traffic network ( $WhyTSKQT$ ) for the first time. Specifically, a hybrid index structure, $TTG$ - $tree$ , is proposed to effectively organize the traffic network information and the positional, textual, and temporal information of objects. Moreover, several pruning strategies are presented to filter out massive objects irrelevant to the query. By analyzing the keywords contained by original result objects and studying the relationship between the time intervals of why objects and the query time interval, we can reasonably construct the candidate query keyword set and query time set to form the candidate refined queries, so as to minimize the refined queries to be evaluated. In addition, several optimization techniques are proposed to further speed up the acquisition of the lowest-cost refined query. Finally, extensive experiments are carried out on two traffic networks to verify the efficiency of the proposed method.