data mining: crossing the chasm rakesh agrawal ibm almaden research center
TRANSCRIPT
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Data Mining: Crossing the Chasm
Rakesh Agrawal
IBM Almaden Research Center
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Thesis
• The greatest challenge facing data mining is to make the transition from being an early market technology to mainstream technology
• We have the opportunity to make this transition successful
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Outline
• Chasm in the technology adoption life cycle, à la Geoffrey Moore†
• Experience with Quest/Intelligent Miner
• Ideas for successful chasm crossing
† Geoffrey A Moore. Crossing the Chasm. Harper Business. http://www.chasmgroup.com
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Technology Adoption Life Cycle
Techies: Try it!
Visionaries: Get ahead of the herd!
Pragmatists: Stick with the herd!
Conservatives: Hold on!
Skeptics: No way!
Late Majority
Early Majority
Early Adopters
LaggardsInnovators
Psychographic profile of each group is different
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Innovators: Technology Enthusiasts
• Intrigued by any fundamental advance in technology
• Like to alpha test new products
• Can ignore the missing elements
• Want access to top technologists
• Want no-profit pricing (preferably free)
Gatekeepers to early adopters
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Early Adopters: Visionaries
• Driven by vision of dramatic competitive advantage via revolutionary breakthroughs
• Great imagination for strategic applications
• Not so price-sensitive
• Want rapid time to market
• Demand high degree of customization
Fund the development of early market
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Early Majority: Pragmatists
• Want sustainable productivity improvement through evolutionary change
• Astute managers of mission-critical apps
• Understand real-world issues and tradeoffs
• Focus on proven applications; want to see the solution in production
Bulwark of the mainstream market
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Late Majority: Conservatives
• Want to stay even with the competition
• Risk averse
• Price sensitive
• Need completely pre-assembled solutions
Extend technology life cycles
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Laggards: Skeptics
• Driven to maintain status quo
• Good at debunking marketing hype
• Disbelieve productivity-improvement arguments
• Can be formidable opposition to early adoption of a technology
Retard the development of high-tech markets
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Crack in the curve
Early Market Mainstream Market
Chasm
The greatest peril in the development of a high-tech market lies in making the transition from an early market dominated by a few visionaries to a mainstream market dominated by pragmatists.
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Visionaries vs. Pragmatists
• Adventurous• First strike capability• Early buy-in• State of the art• Think big• Spend big
• Prudent• Staying power• Wait-and-see• Industry standard• Manage expectation• Spend to budget
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Is data mining following this curve?
• Yes!!!
• My personal viewpoint based on Quest/Intelligent Miner experience
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Quest
• Started as skunk work in early nineties
• Inspired by needs articulated by industry visionaries:– Transaction data collected over a long period– Current tools/SQL don’t cut it– About ready to throw data
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Approach
• Examine “real” applications
• Identify operations that cut across applications
• Design fast, scalable algorithms for each operation
• Develop applications by composing operations
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Operations
• Associations• Sequential Patterns• Similar time series
• New Operations• Completeness,
scalability
• Classification• Clustering• Deviations
• Adopted from Statistics/Learning
• Scalability
http://www.almaden.ibm.com/cs/quest
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Bringing Quest to market
• Visionaries who inspired Quest did not become first customers:– Wanted evidence that the technology “worked”
• Frustrating attempts to interest major IBM customers:– Integration with existing applications– Too-far-out technology– Resistance from in-house analytic groups
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First hits
• Small information-based companies who provided data in exchange for free results
• CIO who wanted to be seen as the technology pioneer in his industry
• CIO who wanted the success story to feature in the company’s annual report
Led to the formation of a group offering services using Quest
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Characteristics of engagements
• Mostly associations and sequential patterns
• Completeness a big plus
• Unanticipated uses
• Feedback for further development
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Into the product land
• Formation of a small “out-of-plan” product group to productize Quest
• Facilitated by a closet mathematician
• Successes of the services group used for market validation
• Continued development and infusion of technology
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Intelligent Miner
• Serious product
• Integrates technologies from various groups
• Fast, scalable, runs on multiple platforms
• Several “early market” success stories
http://www.software.ibm.com/data/iminer/
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Are we in the chasm?
• Perceived to be sophisticated technology, usable only by specialists
• Long, expensive projects
• Stand-alone, loosely-coupled with data infrastructures
• Difficult to infuse into existing mission-critical applications
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Chasm Crossing
• Personal speculations on some technical challenges
• Do not imply IBM research/product directions
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XML-based Data Mining Standard (1)
• Model Building:– A pair of standard
DTDs for each operation
– Interchangeable library of operator implementations
Operator
Model
Parameters
Data Specs Standard DTD
Standard DTD
Library
Ack: Mattos, Pirahesh, Schwenkries
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XML-based Data Mining Standard (2)
• Model Deployment:– Mapping XML object
provides mapping between names and format in the model object and the data record
– Model could have been developed on a different system
Application
Result
Mapping
Standard DTDs
Standard DTD
Library
Model DataRecord
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Implications
• Standard interfaces for application developers to incorporate data mining
• Coupling with relational databases – mappings from DTDs to relational schemas– implementation using existing infrastructure
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Data Mining Benchmarks
• UC Irvine repository
• Generating synthetic benchmarks modeled after real data sets is a hard problem– How to map names into meaningful literals– How to preserve empirical distributions
Ack: Srikant, Ullman
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Auto-focus data mining
• Automatic parameter tuning
• Automatic algorithm selection (à la join method selection in database query optimization)
Ack: Andreas Arning
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Web: Greatest opportunity
• Huge collection of data (e.g. Yahoo collecting ~50GB every day)
• Universal digital distribution medium makes data mining results actionable in fundamentally new ways
• But watch for privacy pitfall
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Privacy-preserving data mining
• Technical vs. legislated solutions
• Implication for data mining algorithms when some fields of a data record have been fudged according to the user’s privacy sensitivity
Ack: R. Srikant
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Personalization
• Internet might provide for the first time tools necessary for users to capture information about themselves and to selectively release this information†
• Will we be providing these tools?
† John Hagel, Marc Singer. Net Worth. Harvard Business School Press.
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What about Association Rules?
• Very long patterns
• Separating wheat from chaff
• Principled introduction of domain knowledge
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What else?
• Formal foundations of data mining
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Summary
• Closely couple data mining with database systems
• Embed data mining into applications
• Focus on web
• Standard interfaces• Benchmarks• Auto focussing
• Personalization• Privacy
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Concluding remarks
• Data mining, a great technology– Combination of intriguing theoretical questions
with large commercial interest in the technology
• Poised for transitioning into mainstream technology
• Will we rise to the challenge as a community?
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Acknowledgments
Arning Arnold Bayardo Baur Bollinger Brodbeck
Baune Carey Chandra Cody Faloutsos Gardner
Gehrke Ghosh Greissl Gruhl Grove Gunopulos
Gupta Haas Ho Imielinski Iyer Lent
Leyman Lin Lingenfelder Mason McPherson Megiddo
Mehta Miranda Psaila Raghavan Rissanen Sawhney
Sarawagi Schwenkries Schkolnick Shafer Shim Somani
Srikant Staub Swami Traiger Vu Zait