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The data model generated by battlefield information is data streams. Because of the rapid data arriving speed and huge size of data set in stream model, novel one-pass algorithms are devised to support data aggregation on demand. VFDT is one of the most successful algorithms for data streams mining, which uses Hoeffding inequality to achieve a probabilistic bound on the accuracy of the tree constructed; we revisit this problem and propose an efficient algorithm for handling battlefield information streaming data. In order to examine this algorithm, we study its performance with different data noise level, number of battlefield information nodes and number of data. Overall, the techniques introduced here can handle battlefield information data efficiently.