IEEE BigData 2013: Big Data challenges and potential solutions
A report from inaugural IEEE Big Data conference highlights, including Berkeley Data Analytics Stack, Crowdsourcing for Data Analytics, Security for Big Data and more.
Guest post by Anmol Rajpurohit, Oct 18, 2013.
IEEE BigData 2013 - Multi-perspective analysis of Big Data challenges and potential solutions
Earlier this month leaders from academia as well as industry gathered to discuss the challenges and current state of research in the field of Big Data.
The 2013 IEEE International Conference on Big Data (IEEE BigData 2013) took place from 6-9 October 2013 in the heart of Silicon Valley, Santa Clara. The conference, organized by IEEE Computer Society, was attended by 400+ data scientists (both researchers and practitioners) from academia, industry, and government.
On Sunday October 6th, IEEE BigData 2013 kicked off with workshops and tutorials to promote basic skills for analytics. These covered a variety of topics such as BigData Visualization, Scalable Machine Learning, and Online Learning for Big Data Analytics. While the workshops provided immersive hands-on learning of popular technologies, the tutorials focused on brief functional and technical overview of key concepts.
On day two, Prof. Michael Franklin from UC Berkeley, USA delivered the keynote lecture on "The Berkeley Data Analytics Stack". This stack uses technologies such as Mesos for management platform, Spark for in-memory framework for interactive and iterative computations, and Hive over Spark to provide SQL-like support. The lecture was followed by a Coffee Break along with Poster Session, providing the attendees a great opportunity to interact and dive deeper into their areas of interest. In the evening, a Banquet was held to help everyone socialize and network.
Day three begun with the speech from Prof. Hector Garcia-Molina of Stanford University, USA on "Using Crowdsourcing for Data Analytics". His talk described how humans can be judiciously used to improve data analytics by cleansing, clustering and filtering critical data. The final day, Oct 9, started with a keynote lecture on "Security - A Big Question for Big Data" by Prof. Roger Schell, University of Southern California, USA. He discussed about pivotal choices for big data to leverage the mature security and privacy technology, while identifying remaining research challenges. He emphasized that since a key value proposition of big data is access to data from multiple and diverse domains, security and privacy will play a very important role in big data research and technology. Later in the afternoon, there were Panel Discussion on "Key Issues in Big Data Research" and "Big Data Funding Program Panel: Challenging and Opportunities".
The event was attended by several researchers, industry leaders, entrepreneurs and subject-matter experts. A wide range of topics were covered, demonstrating the significance of Big Data across industry. The acceptance rates for regular papers for this conference was 17% whereas for short papers it was 20%. More than 35% of the accepted papers from 18 countries all around the world were from US out of all paper.
Anmol Rajpurohit is currently a Research Intern at UCLA. He is finishing B.Tech. in Computer Science in India and is keenly interested in research and development work in the field of Computer Networks, Information Retrieval and Knowledge Management (Data Mining, Web Mining, etc.).
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