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Adaptive cleaning for rfid data streams
, 2006
"... ABSTRACT To compensate for the inherent unreliability of RFID data streams, most RFID middleware systems employ a "smoothing filter", a sliding-window aggregate that interpolates for lost readings. In this paper, we propose SMURF, the first declarative, adaptive smoothing filter for RFID ..."
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ABSTRACT To compensate for the inherent unreliability of RFID data streams, most RFID middleware systems employ a "smoothing filter", a sliding-window aggregate that interpolates for lost readings. In this paper, we propose SMURF, the first declarative, adaptive smoothing filter for RFID data cleaning. SMURF models the unreliability of RFID readings by viewing RFID streams as a statistical sample of tags in the physical world, and exploits techniques grounded in sampling theory to drive its cleaning processes. Through the use of tools such as binomial sampling and π-estimators, SMURF continuously adapts the smoothing window size in a principled manner to provide accurate RFID data to applications.
Efficient Data Interpretation and Compression over RFID Streams
"... 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 ..."
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Cited by 8 (2 self)
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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.
1 SPIRE: Efficient Data Inference and Compression over RFID Streams
"... challenges, including incomplete data, lack of location and containment information, and very high volumes. In this work, we present a novel data inference and compression substrate over RFID streams to address these challenges. Our substrate employs a time-varying graph model to efficiently capture ..."
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Cited by 3 (0 self)
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challenges, including incomplete data, lack of location and containment information, and very high volumes. In this work, we present a novel data inference and compression substrate over RFID streams to address these challenges. Our substrate employs a time-varying graph model to efficiently capture possible object locations and inter-object relationships such as containment from raw RFID streams. It then employs a probabilistic algorithm to estimate the most likely location and containment for each object. By performing such online inference, it enables online compression that recognizes and removes redundant information from the output stream of this substrate. We have implemented a prototype of our inference and compression substrate and evaluated it using both real traces from a laboratory warehouse setup and synthetic traces emulating enterprise supply chains. Results of a detailed performance study show that our data inference techniques provide high accuracy while retaining efficiency over RFID data streams, and our compression algorithm yields significant reduction in output data volume. Index Terms—RFID, data streams, data cleaning, compression, supply-chain management I.
Estimating Data Stream Quality for Object-Detection Applications
"... Object-detection applications rely on streams of data gathered from sensors, RFID readers, and image recognition systems, among others. These raw data streams tend to be noisy, including both false positives (erroneous readings) and false negatives (missed readings). Techniques exist for general-pur ..."
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Object-detection applications rely on streams of data gathered from sensors, RFID readers, and image recognition systems, among others. These raw data streams tend to be noisy, including both false positives (erroneous readings) and false negatives (missed readings). Techniques exist for general-purpose cleaning of these types of data streams, based on temporal and/or spatial correlations, as well as properties of the physical world. Cleaning is effective at improving the quality of the data, however no cleaning procedures can eliminate all errors. In this paper we identify and address the problem of quality estimation as object-detection data streams are cleaned. We provide techniques for estimating both confidence and coverage as streams are processed by cleaning modules. Detailed experimental results based on an RFID application demonstrate the accuracy and effectiveness of our approach.
THE INSTITUTIONAL FACETS OF INNOVATION DIFFUSION INITIATING: THE CASE OF WAL-MART’S RFID CAMPAIGN
"... Les facettes institutionnelles de l’initialisation de la diffusion d’une innovation: le cas de la campagne RFID de Wal-Mart ..."
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Les facettes institutionnelles de l’initialisation de la diffusion d’une innovation: le cas de la campagne RFID de Wal-Mart
