1. Panel on New Research Directions in KDD Ted E. Senator 703-696-2231 tsenator@darpa.mil Disclaimer: Views are My Own, not necessarily those of DARPA, Department of Defense, or the US Government Disclaimer: Views are My Own, not necessarily those of DARPA, Department of Defense, or the US Government

2. 2 Some Important (Research ?) Problems Structured/Linked Data (much more than structural models)  Examples: Human Organizations (formal or informal), (Bio)Chemistry, WWW  Characteristics Non-Homogeneous Entities and Links; Non-Independent Examples Significance of Connected Components (i.e., network fragments) is Apparent Only When Entities/Events are Linked Rare but Important Events: “Micro-Mining” Structural Understanding – Explore Space of Features Continuous Knowledge Discovery and Management  Move from Project Orientation to Process Orientation  Institutionalize Use and Management of Discovered Knowledge  Dynamic, Ongoing Domains – Problem Evolution  Repeatable, Automatable  Autonomous/On-Line  Tight Coupling with Database/DataWarehouse Metrics and Measures of Effectiveness (vice Measures of Performance) Increased Statistical Rigor/Foundations Use of Domain/Background Knowledge Innovative Combination of Techniques for Data Exploration

3. 3 Problems Receiving Too Much Attention Algorithm (Fine) Tuning and Extensions  A New {Decision Tree, Association Rule, Clustering} Algorithm for … Research Masquerading as Applications  “Bicycles Have Two Wheels” Results  “Look Ma, No Hands” Results  Claims of “Look Ma, No Wheels” Results Knowledge-Free Data Mining

4. 4 Potential KDD Applications and Contributions NOT Marketing or Customer Profiling, Perhaps Personalization – if Privacy Can Be Maintained Support for Strategic Business Decisions  New Products  New Markets  New Processes  Policy Formulation

5. 5 Commodity Software ? Is This Really a Good Thing ? Is it What We (KDD Community) Want ?  Commodity: low-cost producers of standard product wins  High-End versus Low-End  Are More Powerful Tools More Easily Misused ? And Results Misinterpreted ? Barriers  Recognized Advantages overcome Costs Alternative Strategies  Results of Ongoing Data Mining: Competitive Advantage for Information Services Providers  Domain-Specific Analytical Techniques Will Fate of KDD Be Similar to That of Expert Systems ?  Hype  Disillusionment  Techniques Embodied in Widely-Used Database Technology, or in Enterprise Management Software Prediction: Less Capable Add-Ons will Dominate More Capable Stand-Alone Applications at both Low End (e.g., Excel) and High End (e.g., Oracle)