The full text of this article


Anomaly digging approach based on massive RFID data in transportation logistics
by Xiaohua Cao; Xiejun Zhang
International Journal of Big Data Intelligence (IJBDI), Vol. 1, No. 3, 2014


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.


is only available to individual subscribers or to users at subscribing institutions.


Existing subscribers:

Go to Inderscience Online Journals to access the full text.


Pay per view:

If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Big Data Intelligence (IJBDI):
Login with your Inderscience username and password:


    Username:        Password:         

Forgotten your password?


Want to subscribe?

A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.


If you still need assistance, please email