The Reverse 𝑘-Nearest Neighbor (R𝑘NN) query over moving objects on road networks seeks to find all moving objects that consider the specified query point as one of their 𝑘 nearest neighbors.In location based services, many users probably submit R𝑘NN queries simultaneously.However, existing methods largely overlook how to efficiently process multiple such queries together, missing opportunities to share redundant computations and thus reduce overall processing costs.To address this, this work is the first to explore batch processing of multiple R𝑘NN queries, aiming to minimize total computation by sharing duplicate calculations across queries.To tackle this issue, we propose the BR𝑘NN-Light algorithm, which uses rapid verification and pruning strategies based on geometric constraints, along with an optimized range search technique, to speed up the process of identifying the R𝑘NNs for each query.Furthermore, it proposes a dynamic distance caching mechanism to enable computation reuse when handling multiple queries, thereby significantly reducing unnecessary computations.Experiments on multiple real-world road networks demonstrate the superiority of the BR𝑘NN-Light algorithm on the processing of batch queries. CCS Concepts• Theory of computation → Database query processing and optimization (theory); Data structures and algorithms for data management.

With the rise of Large Language Models (LLMs), tourists increasingly use it for route planning by entering keywords for attractions, instead of relying on traditional manual map services. LLMs provide generally reasonable suggestions, but often fail to generate optimal plans that account for detailed user requirements, given the vast number of potential POIs and possible routes based on POI combinations within a real-world road network. In this case, a route-planning API could serve as an external tool, accepting a sequence of keywords and returning the top-k best routes tailored to user requests. To address this need, this paper introduces the Keyword-Aware Top-k Routes (KATR) query that provides a more flexible and comprehensive semantic to route planning that caters to various user's preferences including flexible POI visiting order, flexible travel distance budget, and personalized POI ratings. Subsequently, we propose an explore-and-bound paradigm to efficiently process KATR queries by eliminating redundant candidates based on estimated score bounds from global to local levels. Extensive experiments demonstrate our approach's superior performance over existing methods across different scenarios.