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Organize a Symposium

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. Its purpose is to reveal the many sides of a complex issue and promote learning about the issue.

A symposium is a set of papers, beginning with a position paper (max. 2500 words) by a protagonist, followed by 5-10 commentaries (max. 2500 words) by antagonists, and ending with a closing statement (max. 500 words) by the protagonist. The symposium organizer begins with a short symposium overview (max. 250 words) and a table of contents to help orient the readers. The protagonist's closing statement will emphasize what was learned from the exchange and minimize rebuttals to individual responders.

The papers in the symposium will be released weekly until the close of the symposium.

If you are interested in organizing a symposium, send a proposal that includes a title, draft of the organizer's introduction, protagonist, and names of several antagonists, to organizesymposium@ubiquity.acm.org.

To read Ubiquity's symposia, see the Symposia page.

2018 Symposia

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.

 

Ubiquity Symposium: 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
  6. Developing an Open Source "Big Data" Cognitive Computing Platform by Michael Kowolenko and Mladen Vouk
  7. When Good Machine Learning Leads to Bad Cyber Security by Tegjyot Singh Sethi and Mehmed Kantardzic
  8. Corporate Security is a Big Data Problem by Louisa Saunier and Kemal Delic
  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
  10. Big Data or Big Brother? That is the question now (Closing Statement) by Jeffrey Johnson, Peter Denning, David Sousa-Rodrigues, Kemal A. Delic