Publications of Jérôme Darmont
R. Salem, J. Darmont, O. Boussaïd, "Efficient Incremental Breadth-Depth XML Event Mining", 15th International Database Engineering and Applications Symposium (IDEAS 11), Lisbon, Portugal, September 2011, 197-203.
Many applications log a large amount of events as data streams every day. Extracting interesting knowledge from logged events is an emerging active research area in data mining. In this context, we propose an approach for mining association rules from logged events in XML format. This approach is composed of two-main phases: I) constructing a novel tree structure called Frequency XML-based Tree (FXT), which contains the frequency of events to be mined; II) querying the constructed FXT using XQuery to extract frequent itemsets and association rules. The FXT is constructed with a single-pass over logged data. We implement the proposed algorithm and study various performance issues. The performance study shows that the algorithm is efficient, for both constructing the FXT and discovering association rules.
Event stream mining, logged events, XML mining, frequent itemsets, association rules
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