Briefs

Thanks to a partnership with Sony, a team of academic researchers have obtained the largest set of data on social interactions they've ever gotten their hands on: the complete server logs of Everquest 2, which track every action performed in the game.

By John Timmer | February 15, 2009

Researchers ranging from psychologists to epidemiologists have wondered for some time whether online, multiplayer games might provide some ways to test concepts that are otherwise difficult to track in the real world. A Saturday morning session at the meeting of the American Association for the Advancement of Science described what might be the most likely way of finding out. With the cooperation of Sony, a collaborative group of academic researchers at a number of institutions have obtained the complete server logs from the company's Everquest 2 MMORPG.

Dmitri Williams introduced the project and described how researchers have been approaching various game developers over the years. He paraphrased the conversation with Sony as:
"What do you collect?"
"Well, everything - what do you want?"
"Can we have it all?"
"Sure."
The end result is a log that includes four years of data for over 400,000 players that took part in the game, which was followed up with demographic surveys of the users. All told, it makes for a massive data set with distinct challenges but plenty of opportunities.

Computer science challenges

Jaideep Srivastava is a computer scientist doing work on machine learning and data mining - in the past, he has studied shopping cart abandonment at Amazon.com, a virtual event without a real-world parallel. He spent a little time talking about the challenges of working with the Everquest II dataset, which on its own doesn't lend itself to processing by common algorithms. For some studies, he has imported the data into a specialized database, one with a large and complex structure. Regardless of format, many one-pass, exhaustive algorithms simply choke on a dataset this large, which is forcing his group to use some incremental analysis methods or to work with subsets of the data.

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