Continuous Query Processing for Actual and Predicted Object Flow in Symbolic Space

Monitoring the actual and predicted flow of receptor-based (e.g. RFID) moving objects can be useful in a variety of applications, e.g., to predict congestion in an airport bag handling system or reason about the location of a lost bag. In this paper, we propose the Flow Representation Graph (FRG) model, which, unlike earlier work, captures both the actual and predicted flow of moving objects in a symbolic space covering both indoor and outdoor space. Further, the FRG supports time- and value-bound semi-constraints on the object flow, which are useful for modeling important real-world conditions. The paper further introduces FlowPredictor, a Continuous Query Processing Framework (CQPF) that supports continuous spatio-temporal selection, aggregate, and nested queries on FRG objects. A range of update policies allows tuning the tradeoff between performance and accuracy. FlowPredictor employs carefully selected data structures to efficiently handle both insertion and lookup for the actual and predicted flow of objects. The experimental study shows that FlowPredictor can handle a high number of receptor readings while simultaneously processing a large number of continuous queries. Furthermore, the proposed optimizations of data insertion and retrieval are shown to yield significant performance and memory advantages.