Report of the PAKDD 2014 conference (part 2)

This post will continue my report of the PAKDD 2014 in Tainan (Taiwan).

About big data

Another interesting talk at this conference was given by Jian Pei. The topic was Big Data.

Some key ideas in this talk was that to make a technology useful, you have to make it small and invisible. A system relying on data mining may have to detect when a user needs a data mining service and provides the service as early as possible.

Other desirable characteristics of a data mining system are that a user should be able to set preferences. Moreover, if a user interactively changes its preferences, results should be updated quickly.   A data mining system should also be context aware.

It was also mentionned that big data is always relative.  Some papers in the 1970s were already talking about large data and recently some conference have even adopted the theme “extremely large databases”. But even if “big” is relative, since 2003 we record more data every few days in the world than everything that had been recorded before.

Social activities and organization

In general, PAKDD was very-well organized. The organizers did a huge job. It is personally one of the best conference that I have attended in terms of organization.  I was also able to met many interesting people from the field of data mining that I had not met before.

The social activities and banquet were also nice.

Location of PAKDD 2015

The location of PAKDD 2015 was announced. It will be in Ho Chi Minh City, Vitenam from 19-22 May 2014.  The website is http://pakdd2015.pakdd.org

The deadline for paper submission is 3 October 2014 and notification is the 26 December 2014.

Continue reading my PAKDD 2014 report (part 1) here   or part 3 here

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That is all I wanted to write for now. If you like this blog, you can tweet about it and/or subscribe to my twitter account @philfv to get notified about new posts.

Philippe Fournier-Viger is an assistant professor in Computer Science and also the founder of the open-source data mining software SPMF, offering more than 52 data mining algorithms.