Searchlight: Context-aware predictive Continuous Querying of moving objects in symbolic space

Increasingly, streaming positions from moving objects in blended indoor/outdoor spaces are used to deliver new types of real-time location-based services. To support such scenarios, this paper presents the Searchlight Graph (SLG) model and the associated Searchlight Continuous Query Processing Framework (CQPF) for (predictive) Continuous Query Processing (CQP) in symbolic indoor/outdoor spaces. The model captures both actual and predicted object movement, object-specific edge costs, and location/object context annotation with keywords, enabling context-aware (predictive) querying of both locations and objects. Furthermore, the paper proposes several types of continuous spatio-temporal queries, expressed in the declarative Searchlight Query Language (SLQL), along with novel query processing algorithms, and describes their implementation in the Searchlight CQPF. Finally, a novel location prediction algorithm is proposed. Extensive experimental studies show that Searchlight is scalable, efficient, and outperforms its main competitor.