Indoor spatial keyword path query method based on time awareness and exclusion preference

Abstract Existing indoor spatial keyword path queries have not yet addressed user exclusion preferences, and research that simultaneously considers time constraints and exclusion preferences remains unexplored. To address this issue, this paper proposes an indoor spatial keyword path query method based on time awareness and exclusion preference (TEISKPQ). The method introduces a novel and purpose-built Time-awareness and Exclusion-preference Indoor Spatial Keyword tree (TEISK-tree) index structure, specifically designed to organize and manage indoor spatial keyword objects and their associated information. Based on the TEISK-tree index, an index-driven pruning algorithm is developed to rapidly eliminate nodes that do not meet user requirements, significantly reducing computational complexity and enhancing query efficiency. Furthermore, a greedy-based initial path generation algorithm is proposed, which employs a weighted evaluation function to comprehensively assess spatial, textual, and temporal factors between nodes, thereby generating multiple locally optimal initial paths. Finally, an improved genetic algorithm is designed to perform global search-based optimization of the initial paths, thereby better satisfying user requirements by helping to escape local optima and increasing the likelihood of finding high-quality solutions. Experimental results demonstrate that the proposed method exhibits high efficiency and good convergence speed in complex indoor environments, which can improve the efficiency and practicality of indoor path query in urban smart environments and provide support for efficient and intelligent spatial information retrieval.