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WIREs Data Mining Knowl Discov
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Data stream mining in ubiquitous environments: state‐of‐the‐art and current directions

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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 and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single‐node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context‐awareness in this area of research. Context‐awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models.

Conflict of interest: The authors have declared no conflicts of interest for this article.

A roadmap of the survey.
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Coll‐Stream: training and classifying.
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Collaborative learning process.
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The left‐hand side of the figure shows the start screen of pocket data mining. The screen displayed in the middle shows the main screen used for creating a new Agent Miner. The right‐hand side of the figure shows the main screen for creating a Mobile Agent Decision Maker.
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Android version of pocket data mining running on four Android smartphones and one Windows 7 laptop.
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A flow chart describing the collaborative data mining process implemented in the pocket data mining framework.
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The architecture of pocket data mining.
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Mobile data mining for logistics and Global Positioning System (GPS) analysis.
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Mobile data mining for remote health monitoring.
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Mobile data mining for energy conservation in wireless sensor networks.
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Mobile data mining for mobile activity recognition.
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Input and output rate adaptation based on resource levels using Algorithm Input Granularity (AIG) and Algorithm Output Granularity (AOG).
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Mobile data mining for mobile crowd‐sensing.
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Conceptual architecture of the open mobile miner toolkit.
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Browse by Topic

Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining
Technologies > Structure Discovery and Clustering
Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining

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