K-Nearest Neighbor Temporal Aggregate Queries

We study a new type of queries called the k-nearest neighbor temporal aggregate (kNNTA) query. Given a query point and a time interval, it returns the top-k locations that have the smallest weighted sums of (i) the spatial distance to the query point and (ii) a temporal aggregate on a certain attribute over the time interval. For example, find a nearby club that has the largest number of people visiting in the last hour. This type of queries has emerging applications in location-based social networks, location-based mobile advertising and social event recommendation. It is a great challenge to efficiently answer the query due to the highly dynamic nature and the large volume of the data and queries. To address this challenge, we propose an index named TAR-tree, which organizes locations by integrating the spatial and temporal aggregate information. We perform a detailed analysis on the cost of processing kNNTA queries using the TAR-tree. The analysis shows that the TAR-tree results in much fewer node accesses than alternatives. Furthermore, we propose two enhancements for the kNNTA query: (i) an algorithm suggesting the least amount of weights to be adjusted to explore different query results and (ii) a collective processing scheme to share index traversal among a batch of queries. We conduct extensive experiments using real-world data sets. The results validate the accuracy of the cost analysis and show that the TAR-tree outperforms alternatives by up to ten times in node accesses. The results also show that the weight adjustment algorithm and collective processing scheme outperform their baselines by significant margins.