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The 3rd International Workshop on Pattern Mining and Application of Big Data (BigPMA)
@IEEE Big Data 2016
5~8 Dec. 2016, Washington D.C., USA

Pattern mining is an essential domain of data mining that involves finding representative structure in data. Traditionally, pattern mining research has concerned itself with achieving completeness efficiently. However, nowadays, easy data collection in a variety of research areas, including social networks, bioinformatics, mobile sensing, healthcare, security, etc., has led to a massive amount of diverse data, the so-called big data. As a result, there is an increasing demand for developing better methods or more practical applications for extracting patterns from big data.

International Workshop on Pattern Mining and Application of Big Data (BigPMA) will serve as a forum for researchers and technologists to discuss the state-of-the-art, present their contributions, and set future directions in big data pattern mining. This workshop encourages authors to develop applications and evaluate their methodologies using big datasets and investigate challenging problems based on large-scale deployment in the real world.

There are several important aspects to mining patterns from big data. (1) data collection: how to collect meaningful and high-quality data effectively, without compromising personal privacy in different research domains; (2) pattern mining: how to design appropriate algorithms for dealing with large data sets that are beyond the ability of commonly used software tools to capture, curate, manage, and process within a tolerable elapsed time; (3) pattern visualization: how to present mining results in a more intuitive way, and how to abstract key information to visualize the relationship between data; and (4) practical application: how pattern mining algorithms apply to the real-world.

The topics of interest related to this workshop include, but are not limited to:

    Data collection
    Data cleaning
    Data visualization
    Pattern mining technique:
        Association rule mining
        Sequential pattern mining
        Graph analysis
        Time series analysis
    Application:
        Social network
        Recommendation
        Mobile
        Sensor network
        Bioinformatics
        E-commerce
        Health care