Data stream mining has recently emerged in response to the rapidly increasing continuous data generation. While majority of Ant Colony Optimisation (ACO) rule induction algorithms have proved be successful producing both accurate and comprehensive classification models nonstreaming (batch) settings, currently ACO-based for problems are not suited applied mining. One main challenges is iterative...

With sensors and mobile devices becoming ubiquitous, situation monitoring applications are becoming a reality. Data Stream Management Systems (DSMSs) have been proposed to address the data processing needs of such applications that require collection of high-speed data, computing results on-the-fly, and taking actions in real-time. Although a lot of work appears in the area of DSMS, not much ha...

In a wide range of applications, multiple data streams need to be examined together in order to discover trends or patterns existing across several data streams. One common practice is to redirect all data streams into a central place for joint analysis. This “centralized” practice is challenged by the fact that data streams often are private in that they come from different owners. In this pap...

Due to the dynamic nature of knowledge and data in semantic applications, i.e., ontology stream querying technologies are essential for knowledge driven data exploitation systems. Nowadays, many proposed stream reasoning solutions and implemented systems apply forward chaining completion algorithms to handle the removal and addition of axioms. In this deliverable, we propose a novel approach to...

Abstract. Unsupervised identification of groups in large data sets is important for many machine learning and knowledge discovery applications. Conventional clustering approaches (kmeans, hierarchical clustering, etc.) typically do not scale well for very large data sets. In recent years, data stream clustering algorithms have been proposed which can deal efficiently with potentially unbounded ...

Data stream mining has attracted much research attention from the data mining community. With the advance of wireless networks and mobile devices, the concept of ubiquitous data mining has been proposed. However, mobile devices are resource-constrained, which makes data stream mining a greater challenge. In this paper, we propose the RA-HCluster algorithm that can be used in mobile devices for ...

In the last few years, we have been witnessing an evergrowing need for continuous observation and monitoring applications. This need is driven by recent technological advances that have made streaming applications possible, and by the fact that analysts in various domains have realized the value that such applications can provide. In this paper, we propose a general framework for computing effi...

Data stream mining has attracted considerable attention over the past few years owing to the significance of its applications. Streaming data is often evolving over time. Capturing changes could be used for detecting an event or a phenomenon in various applications. Weather conditions, economical changes, astronomical, and scientific phenomena are among a wide range of applications. Because of ...