Knowledge discovery from data streams
Wide area sensor infrastructures, remote sensors, and wireless sensor networks yield massive volumes of disparate, dynamic, and geographically distributed data. As sensors are becoming ubiquitous, a set of broad requirements is beginning to emerge across high priority applications including disaster preparedness and management, adaptability to climate change, national or homeland security, and the management of critical infrastructures. The raw data from sensors need to be efficiently managed and transformed to usable information through data fusion, which in turn must be converted to predictive insights via knowledge discovery, ultimately facilitating automated or human induced tactical decisions or strategic policy. The challenges for the Knowledge Discovery community are immense. Sensors produce dynamic data streams or events requiring real time analysis methodologies and systems. Moreover, in most of the cases these streams are distributed in space, requiring spatio temporal knowledge discovery solutions. All these aspects are of increasing importance to the research community, as new algorithms are needed to process this continuously flow of data in reasonable time. Learning from data streams require algorithms that process examples in constant time and memory, usually scanning data once. Moreover, if the process is not strictly stationary (as most of real world applications) the target concept could gradually change over time. This is an incremental task that requires incremental learning algorithms that take drift into account. For this special issue of Intelligent Data Analysis we selected 5 papers from the accepted papers of the Fourth International Workshop on Knowledge Discovery from Data Streams, an associated workshop of the 18th European Conference on Machine Learning (ECML) and the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) co located in Warsaw, Poland, 2007 and the First ACM SIGKDD Knowledge Discovery from Sensor Data SensorKDD07, co located with the Knowledge Discovery and Data Mining (KDD) 2007 conference organized by the American Computing Machinery (ACM) The selected papers cover a large spectrum in the research of Knowledge Discovery from Data Streams that goes from recommendation algorithms, Clustering, Drifting Concepts and Frequent pattern mining. The common concept in all the papers is that learning occurs while data continuously flows. The first paper Novelty Detection with Application to Data Streams by Spinosa, Carvalho and Gama, presents and evaluates a new approach to novelty detection from data streams. The ability to detect novel concepts is an important aspect of a machine learning system, essential when dealing with nonstationary distributions. The approach presented here intends to take novelty detection beyond one class
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