WO2007147166A3 - Consilence of data-mining - Google Patents

Consilence of data-mining Download PDF

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Publication number
WO2007147166A3
WO2007147166A3 PCT/US2007/071488 US2007071488W WO2007147166A3 WO 2007147166 A3 WO2007147166 A3 WO 2007147166A3 US 2007071488 W US2007071488 W US 2007071488W WO 2007147166 A3 WO2007147166 A3 WO 2007147166A3
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WO
WIPO (PCT)
Prior art keywords
models
data
framework
knowledge
integrated
Prior art date
Application number
PCT/US2007/071488
Other languages
French (fr)
Other versions
WO2007147166A2 (en
Inventor
Akhileswar Ganesh Vaidyanathan
Eli T Faulkner
Benjamin B Perry
Peter J Angeline
Gunjan Kalra
Srikanth V Kallurkar
David C Shepherd
Gary F Holness
Apperson H Johnson
Original Assignee
Quantum Leap Res Inc
Akhileswar Ganesh Vaidyanathan
Eli T Faulkner
Benjamin B Perry
Peter J Angeline
Gunjan Kalra
Srikanth V Kallurkar
David C Shepherd
Gary F Holness
Apperson H Johnson
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Quantum Leap Res Inc, Akhileswar Ganesh Vaidyanathan, Eli T Faulkner, Benjamin B Perry, Peter J Angeline, Gunjan Kalra, Srikanth V Kallurkar, David C Shepherd, Gary F Holness, Apperson H Johnson filed Critical Quantum Leap Res Inc
Publication of WO2007147166A2 publication Critical patent/WO2007147166A2/en
Publication of WO2007147166A3 publication Critical patent/WO2007147166A3/en

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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computing arrangements using knowledge-based models
    • G06N5/02Knowledge representation; Symbolic representation

Abstract

The present invention relates to an integrated framework providing dynamic discovery of knowledge (106), construction of predictive models (105), and decision support services (107). The framework can efficiently exploit dynamically changing data from distributed sources, and is scaleable over local and distributed processor environments. A priori background knowledge (101) is included to gain learning efficiency and to apply logical constraints to the learned models. Extended graphical models are generated by single or iterative uses of the framework, supporting a concentration of the most useful model components (105), and the development of multiple layers of model abstraction. Constructed virtual models can act as proxies for the data they represent, and can be used to build subsequent models on data that is not typically available as an integrated body. The system exploits information about cross-domain models to find information unifying relationships.
PCT/US2007/071488 2006-06-16 2007-06-18 Consilence of data-mining WO2007147166A2 (en)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US81442506P 2006-06-16 2006-06-16
US60/814,425 2006-06-16
US89284607P 2007-03-03 2007-03-03
US60/892,846 2007-03-03

Publications (2)

Publication Number Publication Date
WO2007147166A2 WO2007147166A2 (en) 2007-12-21
WO2007147166A3 true WO2007147166A3 (en) 2008-12-04

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PCT/US2007/071488 WO2007147166A2 (en) 2006-06-16 2007-06-18 Consilence of data-mining

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WO (1) WO2007147166A2 (en)

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