Sensor data streams exhibit special characteristics such as inherent information uncertainty and inherent data sample correlations, both within and across streams. We introduce a new data model, called Probabilistic Stream Relational Algebra (PSRA), that models a sensor data stream as a set of probabilistic data samples, along with prediction strategies for each attributes, capturing domain kno...

We face the problem of novelty detection from stream data, that is, the identification of new or unknown situations in an ordered sequence of objects which arrive on-line, at consecutive time points. We extend previous solutions by considering the case of objects modeled by multiple database relations. Frequent relational patterns are efficiently extracted at each time point, and a time window ...

This paper presents a novel index, called I-Forest, to support data mining activities on data streams, i.e., sequences of incoming data blocks. This approach is appropriate for itemset extraction on evolving datasets such as analysis of transactional data streams from retail chains. The index is a covering structure that represents transaction blocks in a succinct form and allows different kind...

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...

Most of the recent work on adaptive processing and continuous querying of data streams assume that data objects come in the form of tuples, thus relying on the relational data model and traditional relational operators as basis for query processing techniques. Complex types of objects, such as multidimensional data sets or the vast amounts of raster image data continuously streaming down to Ear...