Tremendous progress has been made in systems and analytics of big data, e.g. Hadoop/MapReduce, STORM. However, modern data analytics faces a confluence of growing challenges. First, the increasing data deluge in social networks, online retails, web pages, mobile data, etc creates the need to scale out across hundreds of thousands of commodity machines. Second, the complexity of data analytics has also grown to include sophisticated machine learning algorithm with data dependencies. Third, many systems process streaming data and have real time requirements.
We believe that this emerging field will benefit from close interaction among researchers and industry practitioners. To this end, this workshop brings together academics and practitioners in computer systems, databases, networking, machine learning, and algorithms to share their research accomplishments and identify core problems on big data.
Topics of interest include but are not limited to the following:
Systems Issues related to large datasets: storage, data centers/clouds, streaming system, and architecture.
New programming model for big data beyond Hadoop/MapReduce, STORM, streaming languages
Streaming big data processing
Mining algorithms of big data in non-traditional formats (unstructured, semi-structured)
Scalable, distributed and parallel algorithms
Applications: mobile data, social network systems, smart grid, social media systems, scientific data mining, environmental, health analytics, financial analytics and smart cities.
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