Open standards and cloud computing: KDD-2009 panel report

ABSTRACT

At KDD-2009 in Paris, a panel on open standards and cloud computing addressed emerging trends for data mining applications in science and industry. This report summarizes the answers from a distinguished group of thought leaders representing key software vendors in the data mining industry.

Supporting open standards and the Predictive Model Markup Language (PMML) in particular, the panel members discuss topics regarding the adoption of prevailing standards, benefits of interoperability for business users, and the practical application of predictive models. We conclude with an assessment of emerging technology trends and the impact that cloud computing will have on applications as well as licensing models for the predictive analytics industry.

REFERENCES

• 1: Data Mining Group Website. http://www.dmg.org
• 2: Nicholas Carr, The Big Switch: Rewiring the World, from Edison to Google, W.W. Norton & Company, 2009
• 3: Robert Grossman , Yunhong Gu, Data mining using high performance data clouds: experimental studies using sector and sphere, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2008, Las Vegas, Nevada, USA [doi>10.1145/1401890.1402000]
• 4: Robert Nisbet , John Elder , Gary Miner, Handbook of Statistical Analysis and Data Mining Applications, Academic Press, 2009
• 5: James Taylor , Neil Raden, Smart (enough) systems: how to deliver competitive advantage by automating the decisions hidden in your business, Prentice Hall Press, Upper Saddle River, NJ, 2007

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Additional Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Statistical software; Statistical computing

General Terms:
Languages, Management, Performance, Standardization

Keywords:
cloud computing, data mining, open standards, pmml, predictive analytics, predictive model markup language