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Abstract-Recent innovations in RFID technology are enabling large-scale cost-effective deployments in retail, healthcare, pharmaceuticals and supply chain management. The advent of mobile or handheld readers adds significant new challenges to RFID stream processing due to the inherent reader mobility, increased noise, and incomplete data. In this paper, we address the problem of translating noisy, incomplete raw streams from mobile RFID readers into clean, precise event streams with location information. Specifically we propose a probabilistic model to capture the mobility of the reader, object dynamics, and noisy readings. Our model can self-calibrate by automatically estimating key parameters from observed data. Based on this model, we employ a sampling-based technique called particle filtering to infer clean, precise information about object locations from raw streams from mobile RFID readers. Since inference based on standard particle filtering is neither scalable nor efficient in our settings, we propose three enhancementsparticle factorization, spatial indexing, and belief compressionfor scalable inference over large numbers of objects and highvolume streams. Our experiments show that our approach can offer 54% error reduction over a state-of-the-art data cleaning approach such as SMURF while also being scalable and efficient.

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Abstract-Despite its promise, RFID technology presents numerous challenges, including incomplete data, lack of location and containment information, and very high volumes. In this work, we present a novel data interpretation and compression substrate over RFID streams to address these challenges in enterprise supply-chain environments. Our results show that our inference techniques provide good accuracy while retaining efficiency, and our compression algorithm yields significant reduction in data volume.I. INTRODUCTION RFID is a promising electronic identification technology that enables a real-time information infrastructure to provide timely, high-value content to monitoring and tracking applications. However, RFID data-a triplet in its most basic form-raises new challenges since it may be insufficient, incomplete, and voluminous.Insufficient information: Since RFID is inherently an identification technology designed to identify individual objects, a stream of RFID readings does not capture inter-object relationships such as co-location and containment.Incomplete data: Despite technological advances, RFID readings are inherently noisy with observed read rates below 100% in actual deployments. Missed readings result in a lack of information about an object's location, significantly impairing the tasks of determining both location and containment.High volume streams: Perhaps the key distinguishing characteristic of RFID streams are their high data volumes which can easily overwhelm a data stream system. Hence, it is imperative that data be filtered and compressed close to the hardware while preserving all useful information.In this paper, we present SPIRE, a system that addresses the above challenges. SPIRE departs from the prior work by building an interpretation and compression substrate over RFID data streams which employs three key techniques: (1) a time-varying graph model that captures possible object locations and containment relationships with its stream-driven construction, (2) a probabilistic algorithm that infers the mostlikely locations and containment relationships of objects, and (3) an output algorithm that transforms an input stream to a compressed, yet informative output stream. We have implemented our interpretation and compression substrate and have evaluated it using synthetic RFID streams emulating an enterprise supply-chain environment. Our results show that our inference techniques provide good accuracy while retaining efficiency, and our compression algorithm yields significant reduction in data volume.

Uncertain data streams are increasingly common in real-world deployments and monitoring applications require the evaluation of complex queries on such streams. In this paper, we consider complex queries involving conditioning (e.g., selections and group by's) and aggregation operations on uncertain data streams. To characterize the uncertainty of answers to these queries, one generally has to compute the full probability distribution of each operation used in the query. Computing distributions of aggregates given conditioned tuple distributions is a hard, unsolved problem. Our work employs a new evaluation framework that includes a general data model, approximation metrics, and approximate representations. Within this framework we design fast data-stream algorithms, both deterministic and randomized, for returning approximate distributions with bounded errors as answers to those complex queries. Our experimental results demonstrate the accuracy and efficiency of our approximation techniques and offer insights into the strengths and limitations of deterministic and randomized algorithms.