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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.
Temporal management of RFID data
- the 31st VLDB Conf
, 2005
"... RFID technology can be used to significantly im-prove the efficiency of business processes by pro-viding the capability of automatic identification and data capture. This technology poses many new challenges on current data management sys-tems. RFID data are time-dependent, dynamically changing, in ..."
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Cited by 82 (0 self)
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RFID technology can be used to significantly im-prove the efficiency of business processes by pro-viding the capability of automatic identification and data capture. This technology poses many new challenges on current data management sys-tems. RFID data are time-dependent, dynamically changing, in large volumes, and carry implicit se-mantics. RFID data management systems need to effectively support such large scale temporal data created by RFID applications. These sys-tems need to have an explicit temporal data model for RFID data to support tracking and monitor-ing queries. In addition, they need to have an automatic method to transform the primitive ob-servations from RFID readers into derived data used in RFID-enabled applications. In this pa-per, we present an integrated RFID data manage-ment system – Siemens RFID Middleware – based on an expressive temporal data model for RFID data. Our system enables semantic RFID data filtering and automatic data transformation based on declarative rules, provides powerful query sup-port of RFID object tracking and monitoring, and can be adapted to different RFID-enabled applica-tions.
Complex event processing in enterprise information systems based on RFID
"... Abstract Enterprises have to be increasingly agile and responsive to address the challenges posed by the fast moving market. With the software architecture evolving into SOA, and the adoption of RFID, event processing can fit well in enterprise information systems in terms of facilitation of event a ..."
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Cited by 10 (0 self)
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Abstract Enterprises have to be increasingly agile and responsive to address the challenges posed by the fast moving market. With the software architecture evolving into SOA, and the adoption of RFID, event processing can fit well in enterprise information systems in terms of facilitation of event aggregation into high level actionable information, and event response to improve the responsiveness. To make it more applicable, the architecture of event processing in enterprise information system is proposed; event meta model and context serve as the solid basis for event processing; the rules, operators and keys of complex event processing are defined. Especially, workflow model is firstly used to extract complex event pattern. We have implemented the event processing mechanism in enterprise information systems based on RFID, including the architecture, data structures, optimization strategies and algorithm. The performance evaluations show that the method is effective in terms of scalability and the capability of event processing. Complex event processing can improve operational performance and discover more actionable information, which is justified by application. Finally, lessons learned from the application are presented.
The Internet of Things: A survey from the data-centric perspective,” in Managing and Mining Sensor Data,
, 2013
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Complex Event Processing in EPC Sensor Network Middleware for Both RFID and WSN
- In ISORC ’08: Proceedings of the 2008 11th IEEE Symposium on Object Oriented Real-Time Distributed Computing (ISORC
, 2008
"... In an integration system of RFID and wireless sensor network (WSN), RFID is used to identify objects while WSN can provide context environment information of these objects. Thus, it increases system intelligent in pervasive computing. We propose the EPC Sensor Network (ESN) architecture as an integr ..."
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Cited by 8 (0 self)
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In an integration system of RFID and wireless sensor network (WSN), RFID is used to identify objects while WSN can provide context environment information of these objects. Thus, it increases system intelligent in pervasive computing. We propose the EPC Sensor Network (ESN) architecture as an integration system of RFID and WSN. This ESN architecture is based on EPCglobal architecture, the de facto international standard for RFID. The core of ESN is the middleware part which is also implemented in our work. In this paper, complex event processing (CEP) technology is used in our ESN middleware which can handle large volume of events from distributed RFID and sensor readers in real time. Through filtering, grouping, aggregating and constructing complex event, ESN middleware provides a more meaningful report for the clients and increases system automation.
Title A Temporal RFID Data Model for Querying Physical Objects
, 2007
"... Any software made available via TIMECENTER is provided “as is ” and without any express or implied warranties, including, without limitation, the implied warranty of merchantability and fitness for a particular purpose. The TIMECENTER icon on the cover combines two “arrows. ” These “arrows ” are let ..."
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Cited by 6 (0 self)
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Any software made available via TIMECENTER is provided “as is ” and without any express or implied warranties, including, without limitation, the implied warranty of merchantability and fitness for a particular purpose. The TIMECENTER icon on the cover combines two “arrows. ” These “arrows ” are letters in the so-called Rune alphabet used one millennium ago by the Vikings, as well as by their precedessors and successors. The Rune alphabet (second phase) has 16 letters, all of which have angular shapes and lack horizontal lines because the primary storage medium was wood. Runes may also be found on jewelry, tools, and weapons and were perceived by many as having magic, hidden powers. The two Rune arrows in the icon denote “T ” and “C, ” respectively.
Temporal Management of RFID Data
"... RFID technology can be used to significantly improve the efficiency of business processes by providing the capability of automatic identification and data capture. This technology poses many new challenges on current data management systems. RFID data are time-dependent, dynamically changing, in lar ..."
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Cited by 2 (0 self)
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RFID technology can be used to significantly improve the efficiency of business processes by providing the capability of automatic identification and data capture. This technology poses many new challenges on current data management systems. RFID data are time-dependent, dynamically changing, in large volumes, and carry implicit semantics. RFID data management systems need to effectively support such large scale temporal data created by RFID applications. These systems need to have an explicit temporal data model for RFID data to support tracking and monitoring queries. In addition, they need to have an automatic method to transform the primitive observations from RFID readers into derived data used in RFID-enabled applications. In this paper, we present an integrated RFID data management system -- Siemens RFID Middleware -- based on an expressive temporal data model for RFID data. Our system enables semantic RFID data filtering and automatic data transformation based on declarative rules, provides powerful query support of RFID object tracking and monitoring, and can be adapted to different RFID-enabled applications.
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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Cited by 2 (1 self)
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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.
Data Staging for OLAP- and OLTP-Applications on RFID Data
"... The emerging trend towards seamless monitoring of all business processes via comprehensive sensor networks – in particular RFID readers – creates new data management challenges. In addition to handling the huge volume of data generated by these sensor networks the information systems must support th ..."
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The emerging trend towards seamless monitoring of all business processes via comprehensive sensor networks – in particular RFID readers – creates new data management challenges. In addition to handling the huge volume of data generated by these sensor networks the information systems must support the efficient querying and analysis of both recent data and historic sensor readings. In this paper, we devise and evaluate a data staging architecture that consists of a distributed caching layer and a data warehouse. The caches maintain the most recent RFID events (i.e., sensor readings) to facilitate very efficient OLTP processing. Aged RFID events are removed from the caches and propagated into the data warehouse where related events are aggregated to reduce storage space consumption. The data warehouse can be utilized for business intelligence applications that, e.g., analyze the supply chain quality. 1
