Crowdsourced data streams are continuous flows of generated at high rate by users, also known as the crowd. These popular and extremely valuable in several domains. This is case tourism, where crowdsourcing platforms rely on tourist business inputs to provide tailored recommendations future tourists real time. The continuous, open non-curated nature crowd-originated requires robust stream minin...

In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile...

Smart home technology is a better choice for the people to care about security, comfort and power saving as well. It is required to develop technologies that recognize the Activities of Daily Living (ADLs) of the residents at home and detect the abnormal behavior in the individual's patterns. Data mining techniques such as Frequent pattern mining (FPM), High Utility Pattern (HUP) Mining were us...

We propose an image stream mining method in which images arrive with contexts (metadata) and need to be processed in real-time by the image mining system (IMS), which needs to make predictions and derive actionable intelligence from these streams. After extracting the features of the image by preprocessing, IMS determines online which of its available classifiers it should use on the extracted ...

The primary constraint in the effective mining of data streams is the large volume of data which must be processed in real time. In many cases, it is desirable to store a summary of the data stream segments in order to perform data mining tasks. Since density estimation provides a comprehensive overview of the probabilistic data distribution of a stream segment, it is a natural choice for this ...

Most previously proposed mining methods on data streams make an unrealistic assumption that “labelled” data stream is readily available and can be mined at anytime. However, in most real-world problems, labelled data streams are rarely immediately available. Due to this reason, models are refreshed periodically, that is usually synchronized with data availability schedule. There are several und...

Sequential pattern mining searches for the relative sequence of events, allowing users to make predictions on discovered sequential patterns. Due to drastically advanced information technology over recent years, data have rapidly changed, growth in data amount has exploded and real-time demand is increasing, leading to the data stream environment. Data in this environment cannot be fully stored...

In recent years, data streams have become ubiquitous because of the large number of applications which generate huge volumes of data in an automated way. Many existing data mining methods cannot be applied directly on data streams because of the fact that the data needs to be mined in one pass. Furthermore, data streams show a considerable amount of temporal locality because of which a direct a...

Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining PDM. Large amounts of available data streams to which smart phones can subscribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system. This emerging area of study has shown to be feasible ...

This paper presents StreamXPlore, a system that enables users to explore historical stream data in order to determine what events to monitor in the future. At the heart of StreamXPlore is a new event modeling mechanism. StreamXPlore enables the specification, analysis, and mining of these new types of events. Event analysis enables event refinement using data-cube-style slice, dice, drilldown, ...