Computing canonical hierarchical schemas
作者： Michael Dietrich , Gunther Stuhec , Jens Lemcke
DOI:
关键词: Theoretical computer science 、 Schema (psychology) 、 Data mining 、 Computer science
摘要: Methods, systems, and computer-readable storage media for receiving a plurality of source hierarchical schemas, processing the schemas to generate merged graph, which is comprising nodes, each node being provided based on one or more nodes from at least two determining resolving using conflicts in graph conflict-free wherein comprises splitting into respective sub-sets defining constraints satisfaction problem (CSP) mediated (MHSs), MHS solution CSP, identifying an MHSs as optimum MHS, canonical schema (CHS) storing CHS document memory.
lens.org 参考文章(30)
Domenico Beneventano, Maurizio Vincini, Sonia Bergamaschi, Francesco Guerra, The Momis approach to Information Integration international conference on enterprise information systems. ,vol. 1, pp. 194- 198 ,(2001)
Jeffrey Scott Eder, Complete Context(tm) Query System ,(2008)
Hung Jung Lu, William Wai Shing Lui, Wai-Keung Tang, Business enterprise risk model and method ,(2003)
Jeff Scott Eder, Entity centric computer system ,(2003)
Paul Embrechts, Filip Lindskog, Alexander Mcneil, Chapter 8 – Modelling Dependence with Copulas and Applications to Risk Management Handbook of Heavy Tailed Distributions in Finance. pp. 329- 384 ,(2003) , 10.1016/B978-044450896-6.50010-8
Mark Noetzold, Dirk Noetzold, Method and system for valuation of complex systems, in particular for corporate rating and valuation ,(2003)
Charles Walden, Marcus Harte, Jeffrey Lange, Kenneth Charles Baron, Enhanced parimutuel wagering ,(2004)
Jeffrey Lange, Digital options having demand-based, adjustable returns, and trading exchange therefor ,(2002)
Ivan Kojadinovic, Jun Yan, Modeling Multivariate Distributions with Continuous Margins Using the copula R Package Journal of Statistical Software. ,vol. 34, pp. 1- 20 ,(2010) , 10.18637/JSS.V034.I09
Lise Getoor, Prithviraj Sen, Amol Deshpande, PrDB: managing and exploiting rich correlations in probabilistic databases very large data bases. ,vol. 18, pp. 1065- 1090 ,(2009) , 10.1007/S00778-009-0153-2