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A Relational Approach to Novelty Detection in Data Streams

A data stream is a sequence of time-stamped data elements which arrive on-line, at consecutive time points. In this work we propose a multi-relational approach to mine complex data streams in order to identify novelty patterns which target new or unknown situations in the stream. Multi-relational data mining is motivated by the existence of several real-world data stream applications where data elements are complex data scattered in several database relations of a relational database. In our proposal a stream is mined according to a data block model, that is, the stream is segmented in a sequence of data blocks where each data block consists of complex data elements arriving in a user define period (e.g., daily or monthly). A relational pattern base is mined each time a new data block arrives in the stream and a time window is used to filter out novelty patterns. An application of the proposed algorithm to the problem of detecting anomalies in network traffic is described