The knowledge embedded in an online data stream is likely to change over time due to the dynamic evolution of the stream. Consequently, in frequent episode mining over an online stream, frequent episodes should be adaptively extracted from recently generated stream segments instead of the whole stream. However, almost all existing frequent episode mining approaches find episodes frequently occu...

Among data-intensive applications that are beyond the reach of traditional Data Base Management Systems (DBMS), data mining stands out because of practical importance and the complexity of the research problems that must be solved before the vision of Inductive DBMS can become a reality. In this paper, we first discuss technical developments that have occurred since the very notion of Inductive...

In the data stream computational model examples are processed once, using restricted computational resources and storage capabilities. The goal of data stream mining consists of learning a decision model, under these constraints, from sequences of observations generated from environments with unknown dynamics. Most of the stream mining works focus on centralized approaches. The phenomenal growt...

Stream data mining has gained a lot of attention over the last years due to an abundance of streaming data in professional as well as personal applications. Solutions have been proposed for many mining tasks such as clustering, classification, frequent item set mining and aggregation. Stream mining is especially challenging due to the large (usually endless) amount of data and the time constrai...

Many massive web and communication network applications create data which can be represented as a massive sequential stream of edges. For example, conversations in a telecommunication network or messages in a social network can be represented as a massive stream of edges. Such streams are typically very large, because of the large amount of underlying activity in such networks. An important app...

Recent advances in ubiquitous devices open an opportunity to apply new data stream mining techniques to support intelligent decision making in the next generation of ubiquitous applications. This paper motivates and describes a novel Context-aware Collaborative data stream mining system CC-Stream that allows intelligent mining and classification of time-changing data streams on-board ubiquitous...

Recently many researchers have focused on mining data streams and they proposed many techniquesand algorithms for data streams. It refers to the process of extracting knowledge from nonstop fast growing data records. They are data stream classification, data stream clustering, and data stream frequentpattern items and so on. Data stream clustering techniques are highly helpful to cluster the si...