A Framework for Processing Uncertain RFID Data in Supply Chain Management

Dong Xie, Quan Z. Sheng, Jiangang Ma, Yun Cheng, Yongrui Qin, Rui Zeng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Radio Frequency Identification (RFID) is widely used to track and trace objects in supply chain management. However, massive uncertain data produced by RFID readers are not suitable for directly use in RFID applications. Following our thorough analysis of key features of RFID objects, this paper proposes a new framework for effectively and efficiently processing uncertain RFID data, and supporting a variety of queries for tracking and tracing RFID objects. In particular, we propose an adaptive cleaning method by adjusting size of smoothing window according to various rates of uncertain data, employing different strategies to process uncertain readings, and distinguishing different types of uncertain data according to their appearing positions. We propose a comprehensive data model, which is suitable for a wide range of application scenarios. In addition, a path coding scheme is proposed to significantly compress massive data by aggregating the path sequences, the positions and the time intervals. Experimental evaluations show that our approach is effective and efficient in terms of the compression and traceability queries.

Original languageEnglish
Title of host publicationWeb Information Systems Engineering, WISE 2013
Subtitle of host publication14th International Conference, Proceedings
EditorsXuemin Lin, Yannis Manolopoulos, Divesh Srivastava, Guangyan Huang
PublisherSpringer Berlin Heidelberg
Pages396-409
Number of pages14
Edition1
ISBN (Electronic)9783642412301
ISBN (Print)9783642412295
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event14th International Workshop on Web Information Systems Engineering - Nanjing, China
Duration: 13 Oct 201315 Oct 2013
Conference number: 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8180 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Workshop

Workshop14th International Workshop on Web Information Systems Engineering
Abbreviated titleWISE 2013
CountryChina
CityNanjing
Period13/10/1315/10/13

Fingerprint

Supply Chain Management
Supply chain management
Radio Frequency Identification
Radio frequency identification (RFID)
Uncertain Data
Processing
Query
Path
Traceability
Cleaning
Tracing
Experimental Evaluation
Data Model
Data structures
Framework
Smoothing
Compression
Coding
Trace
Scenarios

Cite this

Xie, D., Sheng, Q. Z., Ma, J., Cheng, Y., Qin, Y., & Zeng, R. (2013). A Framework for Processing Uncertain RFID Data in Supply Chain Management. In X. Lin, Y. Manolopoulos, D. Srivastava, & G. Huang (Eds.), Web Information Systems Engineering, WISE 2013: 14th International Conference, Proceedings (1 ed., pp. 396-409). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8180 LNCS, No. PART 1). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-41230-1_33
Xie, Dong ; Sheng, Quan Z. ; Ma, Jiangang ; Cheng, Yun ; Qin, Yongrui ; Zeng, Rui. / A Framework for Processing Uncertain RFID Data in Supply Chain Management. Web Information Systems Engineering, WISE 2013: 14th International Conference, Proceedings. editor / Xuemin Lin ; Yannis Manolopoulos ; Divesh Srivastava ; Guangyan Huang. 1. ed. Springer Berlin Heidelberg, 2013. pp. 396-409 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
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Xie, D, Sheng, QZ, Ma, J, Cheng, Y, Qin, Y & Zeng, R 2013, A Framework for Processing Uncertain RFID Data in Supply Chain Management. in X Lin, Y Manolopoulos, D Srivastava & G Huang (eds), Web Information Systems Engineering, WISE 2013: 14th International Conference, Proceedings. 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 8180 LNCS, Springer Berlin Heidelberg, pp. 396-409, 14th International Workshop on Web Information Systems Engineering, Nanjing, China, 13/10/13. https://doi.org/10.1007/978-3-642-41230-1_33

A Framework for Processing Uncertain RFID Data in Supply Chain Management. / Xie, Dong; Sheng, Quan Z.; Ma, Jiangang; Cheng, Yun; Qin, Yongrui; Zeng, Rui.

Web Information Systems Engineering, WISE 2013: 14th International Conference, Proceedings. ed. / Xuemin Lin; Yannis Manolopoulos; Divesh Srivastava; Guangyan Huang. 1. ed. Springer Berlin Heidelberg, 2013. p. 396-409 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8180 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - A Framework for Processing Uncertain RFID Data in Supply Chain Management

AU - Xie, Dong

AU - Sheng, Quan Z.

AU - Ma, Jiangang

AU - Cheng, Yun

AU - Qin, Yongrui

AU - Zeng, Rui

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N2 - Radio Frequency Identification (RFID) is widely used to track and trace objects in supply chain management. However, massive uncertain data produced by RFID readers are not suitable for directly use in RFID applications. Following our thorough analysis of key features of RFID objects, this paper proposes a new framework for effectively and efficiently processing uncertain RFID data, and supporting a variety of queries for tracking and tracing RFID objects. In particular, we propose an adaptive cleaning method by adjusting size of smoothing window according to various rates of uncertain data, employing different strategies to process uncertain readings, and distinguishing different types of uncertain data according to their appearing positions. We propose a comprehensive data model, which is suitable for a wide range of application scenarios. In addition, a path coding scheme is proposed to significantly compress massive data by aggregating the path sequences, the positions and the time intervals. Experimental evaluations show that our approach is effective and efficient in terms of the compression and traceability queries.

AB - Radio Frequency Identification (RFID) is widely used to track and trace objects in supply chain management. However, massive uncertain data produced by RFID readers are not suitable for directly use in RFID applications. Following our thorough analysis of key features of RFID objects, this paper proposes a new framework for effectively and efficiently processing uncertain RFID data, and supporting a variety of queries for tracking and tracing RFID objects. In particular, we propose an adaptive cleaning method by adjusting size of smoothing window according to various rates of uncertain data, employing different strategies to process uncertain readings, and distinguishing different types of uncertain data according to their appearing positions. We propose a comprehensive data model, which is suitable for a wide range of application scenarios. In addition, a path coding scheme is proposed to significantly compress massive data by aggregating the path sequences, the positions and the time intervals. Experimental evaluations show that our approach is effective and efficient in terms of the compression and traceability queries.

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SN - 9783642412295

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Web Information Systems Engineering, WISE 2013

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A2 - Manolopoulos, Yannis

A2 - Srivastava, Divesh

A2 - Huang, Guangyan

PB - Springer Berlin Heidelberg

ER -

Xie D, Sheng QZ, Ma J, Cheng Y, Qin Y, Zeng R. A Framework for Processing Uncertain RFID Data in Supply Chain Management. In Lin X, Manolopoulos Y, Srivastava D, Huang G, editors, Web Information Systems Engineering, WISE 2013: 14th International Conference, Proceedings. 1 ed. Springer Berlin Heidelberg. 2013. p. 396-409. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-642-41230-1_33