Data Interpolating over RFID Data Streams for Missed Readings

While tracing objects or analyzing human activities with RFID data sets, the quality of RFID data is a crucial aspect. The raw RFID data streams, however, tend to be noisy, including missed readings and unreliable readings. Traditional data cleaning tends to focus on a small set of well-defined tasks, including transformation, matching, and duplicate elimination. In this paper, we focus on exploring efficient methods for interpolating missed readings. We propose a novel probabilistic interpolating method and three novel deterministic interpolating methods based on time interval, containment relationship and inertia of objects, respectively. We conduct extensive experiments and the experimental results demonstrate the feasibility and effectiveness of our methods.