Radio Frequency Identification (RFID) technology is fast becoming a prevalent tool in tracking commodities in supply chain management applications. The movement of commodities through the supply chain forms a gigantic workflow that can be mined for the discovery of trends, flow correlations and outlier paths, that in turn can be valuable in understanding and optimizing business processes. In this paper, we propose a method to construct compressed probabilistic workflows that capture the movement trends and significant exceptions of the overall data sets, but with a size that is substantially smaller than that of the complete RFID workflow. Compression is achieved based on the following observations: (1) only a relatively small minority of items deviate from the general trend, (2) only truly non-redundant deviations, i.e., those that substantially deviate from the previously recorded ones, are interesting, and (3) although RFID data is registered at the primitive level, data analysis usually takes place at a higher abstraction level. Techniques for workflow compression based on non-redundant transition and emission probabilities are derived; and an algorithm for computing approximate path probabilities is developed. Our experiments demonstrate the utility and feasibility of our design, data structure, and algorithms.
A method has been offered in REF (3) although RFID data are registered at the primitive level, data analysis usually takes place at a higher abstraction level.
Mining compressed commodity workflows from massive RFID data sets
CIKM '06
Computer Science