Abstract: | We are given a large database of logistic data containing information about shipments and their states in time. We introduce the problem of sequential data mining over such database collected in shipment handling process. We present three approaches on handling such data, which are, respectively, simple sequential data mining tool, advanced sequential data mining tool and transition probability model. We evaluate the differences between the approaches and between the process documentation provided and the output of the approaches. The simple tool can provide most of the common subsequences and sequences, the advanced tool can provide complex overview and using the probability model is a fast, but not very exact method to describe the input dataset.
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