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A Ubiquity symposium is an organized debate around a proposition or point of view. It is a means to explore a complex issue from multiple perspectives. An early example of a symposium on teaching computer science appeared in Communications of the ACM (December 1989).

To organize a symposium, please read our guidelines.


New in Ubiquity Symposia: 

"Big Data"

Table of Contents

  1. Big Data, Digitization, and Social Change (Opening Statement) by Jeffrey Johnson, Peter Denning, David Sousa-Rodrigues, Kemal A. Delic
  2. Big Data and the Attention Economy by Bernardo A. Huberman
  3. Big Data for Social Science Research by Mark Birkin
  4. Technology and Business Challenges of Big Data in the Digital Economy by Dave Penkler
  5. High Performance Synthetic Information Environments: An integrating architecture in the age of pervasive data and computing By Christopher L. Barrett , Jeffery Johnson, and Madhav Marathe (February 2018)
  6. Developing an Open Source "Big Data" Cognitive Computing Platform by Michael Kowolenko and Mladen Vouk (February 2018)
  7. When Good Machine Learning Leads to Bad Cyber Security by Tegjyot Singh Sethi and Mehmed Kantardzic (March 2018)
  8. Corporate Security is a Big Data Problem by Louisa Saunier and Kemal Delic (March 2018)
  9. Big Data: Business, technology, education, and science by Jeffrey Johnson, Luca Tesei, Marco Piangerelli, Emanuela Merelli, Riccardo Paci, Nenad Stojanovic, Paulo Leitão, José Barbosa, and Marco Amador (April 2018)
  10. Big Data or Big Brother? That is the question now (Closing Statement) by Jeffrey Johnson, Peter Denning, David Sousa-Rodrigues, Kemal A. Delic (April 2018)

Previous Ubiquity Symposia:

"The Internet of Things"

"The Technological Singularity"

"MOOCs and Technology to Advance Learning and Learning Research"

"The Multicore Transformation"

"The Science in Computer Science"

"Evolutionary Computation and the Processes of Life"

"What is Computation"

  • Big data: developing an open source "big data" cognitive computing platform

    The ability to leverage diverse data types requires a robust and dynamic approach to systems design. The needs of a data scientist are as varied as the questions being explored. Compute systems have focused on the management and analysis of structured data as the driving force of analytics in business. As open source platforms have evolved, the ability to apply compute to unstructured information has exposed an array of platforms and tools available to the business and technical community. We have developed a platform that meets the needs of the analytics user requirements of both structured and unstructured data. This analytics workbench is based on acquisition, transformation, and analysis using open source tools such as Nutch, Tika, Elastic, Python, PostgreSQL, and Django to implement a cognitive compute environment that can handle widely diverse data, and can leverage the ever-expanding capabilities of infrastructure in order to provide intelligence augmentation.