Datastreams are potentially infinite data sources that flow continuously while monitoring a physical phenomenon, like temperature levels or other kind of human activities, such as clickstreams, telephone call records, and so on. RFID technology has lead in recent years the generation of huge streams of data. Moreover, RFID based systems allow the effective management of items tagged by RFID tags, especially for supply chain management or objects tracking. In this paper we introduce SMART (Stream Monitoring enterprise Activities by RFID Tags) a system based on an outlier template definition for detecting anomalies in RFID streams. We describe SMART features and its application on a real life scenario that shows the effectiveness of the proposed method for enterprise management. Moreover, we describe an outlier detection approach we defined and effectively exploited in SMART. (C) 2012 Elsevier Inc. All rights reserved.

SMART: Stream Monitoring enterprise Activities by RFID Tags

Masciari E
2012

Abstract

Datastreams are potentially infinite data sources that flow continuously while monitoring a physical phenomenon, like temperature levels or other kind of human activities, such as clickstreams, telephone call records, and so on. RFID technology has lead in recent years the generation of huge streams of data. Moreover, RFID based systems allow the effective management of items tagged by RFID tags, especially for supply chain management or objects tracking. In this paper we introduce SMART (Stream Monitoring enterprise Activities by RFID Tags) a system based on an outlier template definition for detecting anomalies in RFID streams. We describe SMART features and its application on a real life scenario that shows the effectiveness of the proposed method for enterprise management. Moreover, we describe an outlier detection approach we defined and effectively exploited in SMART. (C) 2012 Elsevier Inc. All rights reserved.
2012
Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR
RFID tag
Object tracking
Outlier detection
Data stream
Data mining
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/261654
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