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Technologically inevitable introduction of various kinds of sensors to our life resulted in the production of huge amount of data delivered as streams. An improper acquisition of information may lead to errors caused by mixing observations coming from different processes threads. Some remedy can bring a proper representation of information. Hence, this paper introduces a graph–stream structure representing performance of complex multi–threaded process. The proposed network representation can separate information describing multiple threads and allows for modeling causal relationships between them. It gives separated and segregated information opening opportunity for development of qualitatively better and simpler knowledge retrieval algorithms. Further, the paper delivers a method for this representation extraction from multivariate data stream. It would be done by a clustering algorithm particularly designed for this purpose and evaluated quantitatively and qualitatively on example sets of data.

Unsupervised Extraction of Graph-stream Structure for Purpose of Knowledge Retrieval and Information Fusion