The field of process mining is concerned with supporting the analysis, improvement and understanding of business processes. A range of promising techniques have been proposed for process mining tasks such as process discovery and conformance checking. However there are challenges, originally stemming from the area of data mining, that have not been investigated extensively in context of process...

Sensor networks, in addition to stock tickers, network event logs, scientific simulations, credit card transactional flows, and surveillance video cameras, output a continuous flow of data, called a data stream. Mining data from a network of sensors provides insight into the health and reliability of a system. This process is known as data stream mining. This paper examines the issues associate...

There is an emerging focus on real-time data stream analysis on mobile devices. A wide range of data stream processing applications are targeted to run on mobile handheld devices with limited computational capabilities such as patient monitoring, driver monitoring, providing real-time analysis and visualisation for emergency and disaster management, real-time optimisation for courier pick-up an...

In recent years, advances in hardware technology have facilitated new ways of collecting data continuously. One such application is that of sensor data, which may continuously monitor large amounts of data for storage and processing. In this paper, we will discuss the general issues which arise in mining large amounts of sensor data. In many cases, the data patterns may evolve continuously, as ...

Repeating patterns represent temporal relations among data items, which could be used for data summarization and data prediction. More and more data of various applications is generated as a data stream. Based on time sensitive concern, mining repeating patterns from the whole history data sequence of a data stream does not extract the current trend of patterns in the stream. Therefore, the tra...

This paper introduces Debellor (www.debellor.org) – an open source extensible data mining platform with stream-based architecture, where all data transfers between elementary algorithms take the form of a stream of samples. Data streaming enables implementation of scalable algorithms, which can efficiently process large volumes of data, exceeding available memory. This is very important for dat...

Mining frequent itemsets over data streams is an emergent research topic in recent years. Previous approaches generally use a fixed support threshold to discover the patterns in the stream. However, the threshold will be changed to cope with the needs of the users and the characteristics of the incoming data in reality. Changing the threshold implies a re-mining of the whole transactions in a n...

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 ...

Automated adversarial detection systems can fail when under attack by adversaries. As part of a resilient data stream mining system to reduce the possibility of such failure, adaptive spike detection is attribute ranking and selection without class-labels. The first part of adaptive spike detection requires weighing all attributes for spiky-ness to rank them. The second part involves filtering ...