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Outlier Detection in RFID Datasets in Supply Chain Process: A Review
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 65 - Number 25 |
| Year of Publication: 2013 |
| Authors: Meghna Sharma, Manjeet Singh |
10.5120/11277-6422
|
Meghna Sharma, Manjeet Singh . Outlier Detection in RFID Datasets in Supply Chain Process: A Review. International Journal of Computer Applications. 65, 25 ( March 2013), 47-51. DOI=10.5120/11277-6422
Abstract
Outlier detection has been a very important concept in the realm of data analysis. Most real-world databases include a certain amount of exceptional values, generally termed as "outliers". The finding of outliers is important for improving the quality of original data and for reducing the impact of outlying values in the process of knowledge discovery in databases. . Outlier detection has been researched within various application domains and knowledge disciplines. Supply Chain Process is one of the popular and important domains. The implementation of RFID leads to improved visibility in supply chains. However, as a result of the increased collection of data and data granularity, new data management challenges are faced by supply chain participants new techniques for outlier detection are experimented. In this Paper the problem of detecting outliers in RFID readings stream. is addressed and considering the stream based ,spatio-temporal nature of RFID datasets, density based outlier detection technique is concluded to be the best among all the existing approaches. for outlier detection
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