According to Gartner, by year 2020, revenue generated from Internet of Thing’s (IOT) products and services will exceed $300 billion. When discussing about IOT, the thing that keeps visiting our mind, is the huge amount of data stream that is getting generated by the use of IOT applications. The data stream is different from the traditional data as only one scan is possible for mining because of...

In recent years, data streams have become an increasingly important area of research for the computer science, database and statistics communities. Data streams are ordered and potentially unbounded sequences of data points created by a typically non-stationary data generating process. Common data mining tasks associated with data streams include clustering, classification and frequent pattern ...

The developments of information and communication technologies dramatically change the data collection and processing methods. Data mining is now moving to the era of bounded rationality. In this work we discuss the implications of the resource constraints impose by the data stream computational model in the design of learning algorithms. We analyze the behavior of stream mining algorithms and ...

Data stream classification has drawn increasing attention from the data mining community in recent years, where a large number of stream classification models were proposed. However, most existing models were merely focused on mining from single-label data streams. Mining from multi-label data streams has not been fully addressed yet. On the other hand, although some recent work touched the mul...

Wireless sensor networks are usually deployed for monitoring given physical phenomena taking place in a specific space and over a specific duration of time. The spatio-temporal distribution of these phenomena often correlates to certain physical events. To appropriately characterise these events-phenomena relationships over a given space for a given time frame, we require continuous monitoring ...

In resource-constrained devices, adaptation of data stream processing to variations of data rates, availability of resources and environment changes is crucial for consistency and continuity of running applications. Context-aware and resource-aware adaptation, as a new dimension of research in data stream mining, enhances and improves distributed data stream processing tasks. Context-awareness ...

In resource-constrained devices, adaptation of data stream processing to variations of data rates and availability of resources is crucial for consistency and continuity of running applications. However, to enhance and maximize the benefits of adaptation, there is a need to go beyond mere computational and device capabilities to encompass the full spectrum of contextawareness. This paper presen...

In recent years, data streams have emerged as a new data type that has attracted much attention from the data mining community. They arise naturally in a number of applications (Brian et al., 2002), including financial service (stock ticker, financial monitoring), sensor networks (earth sensing satellites, astronomic observations), web tracking and personalization (webclick streams). These stre...

From the advent of association rule mining, it has become one of the most researched areas of data exploration schemes. In recent years, implementing association rule mining methods in extracting rules from a continuous flow of voluminous data, known as Data Stream has generated immense interest due to its emerging applications such as network-traffic analysis, sensor-network data analysis. For...

Developments in data streams, coupled with the growth in mobile and pervasive devices, have led to the emergence of Ubiquitous Data Mining (UDM). UDM aims to perform data stream mining in a ubiquitous environment with resourceconstrained and/or mobile devices. Over the past few years, stream mining techniques have attracted the attention of the data mining community. However these techniques ha...