Efficient processing of all neighboring object group queries with budget range constraint in road networks

Abstract

We present a new type of location-based queries, namely the Budget Range-based All Neighboring Object Group Query (BR-ANOGQ for short), to offer spatial object information while respecting distance and budget range constraints. This query type finds utility in numerous practical scenarios, such as assisting travelers in selecting fitting destinations for their journeys. To support the BR-ANOGQ, we develop data structures for efficient representation of road networks and employ two index structures, the \(R^{cC}\)-tree and the grid index, for managing spatial objects based on their locations and costs. We introduce two pruning criteria to filter out object sets that do not meet the specified distance d and budget range \([bgt_m, bgt_M]\) constraints. We also devise a road network traversal method that selectively accesses a small fraction of objects while generating the query result. The BR-ANOGQ algorithm effectively utilizes index structures and pruning criteria for query processing. Through a series of comprehensive experiments, we demonstrate its efficiency in terms of CPU time and index node accesses, providing valuable insights for location-based queries with constraints.