Anomaly digging approach based on massive RFID data in transportation logistics. (1st January 2014)

• Record Type: Journal Article
• Authors: Cao, Xiaohua; Zhang, Xiejun
• Is Part Of: International journal of big data intelligence. Volume 1:Number 3(2014)
• Page Start: 166
• Page End: 171
• Publication Date: 2014-01-01
• Subjects: anomaly digging -- RFID -- deviation model -- clustering -- logistics; Big data -- Periodicals; 005.705
• Languages: English
• ISSNs: 2053-1389

Abstract

In modern transportation logistics, anomaly significantly lowers the efficiency of production and the quality of service. Massive RFID data is produced to record the states of materials in transportation logistics. The data is of multi-attribute, randomness and various dimensions so that it is difficult to find out anomalies from these data. A deviation-based clustering approach is proposed to dig anomalies. Firstly, the features of RFID data are discussed from multi-attribute perspectives including time, location, data, sequence and path. Next, against the randomness and various dimensions of state data, a clustering approach is presented to unify the dimensions of state data and dig anomalies from random state data. The results show that the proposed approach can efficiently find more than 91.2% of anomalies among transportation logistics.