Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
2005-04-26
2005-04-26
Mizrahi, Diane D. (Department: 2165)
Data processing: database and file management or data structures
Database design
Data structure types
Reexamination Certificate
active
06886009
ABSTRACT:
Query routing is based on identifying the preeminent search systems and data sources for each of a number of information domains. This involves assigning a weight to each search system or data source for each of the information domains. The greater the weight, the more preeminent a search system or data source is in a particular information domain. These weights Wi{1=0, 1,2, . . . N] are computed through a recursive learning process employing meta processing. The meta learning process involves simultaneous interrogation of multiple search systems to take advantage of the cross correlation between the search systems and data sources. In this way, assigning a weight to a search system takes into consideration results obtained about other search systems so that the assigned weights reflect the relative strengths of each of the systems or sources in a particular information domain. In the present process, a domain dataset used as an input to query generator. The query generator extracts keywords randomly from the domain dataset. Sets of the extracted keywords constitute a domain specific search query. The query is submitted to the multiple search systems or sources to be evaluated. Initially, a random average weight is assigned to each search system or source. Then, the meta learning process recursively evaluates the search results and feeds back a weight correction dWi to be applied to each system or source server by using weight difference calculator. After a certain number of iterations, the weights Wi reach stable values. These stable values are the values assigned to the search system under evaluation. When searches are performed, the weights are used to determine search systems or sources that are interrogated.
REFERENCES:
patent: 5398302 (1995-03-01), Thrift
patent: 5499366 (1996-03-01), Rosenberg et al.
patent: 5737734 (1998-04-01), Schultz
patent: 5899991 (1999-05-01), Karch
patent: 5956708 (1999-09-01), Dyko et al.
patent: 6041326 (2000-03-01), Amro et al.
patent: 6085186 (2000-07-01), Christianson et al.
patent: 6102969 (2000-08-01), Christianson et al.
patent: 6240412 (2001-05-01), Dyko et al.
patent: 6778979 (2004-08-01), Grefenstette et al.
patent: 6829599 (2004-12-01), Chidlovskii
patent: 20020156792 (2002-10-01), Gombocz et al.
patent: 20040024745 (2004-02-01), Jeng et al.
patent: 20040068486 (2004-04-01), Chidlovskii
patent: 851368 (1997-12-01), None
Victor Holmes et al., Integrating Metadata tools with the data services archive to provide web-based managment of large-scale scientific simulation, 2004, IEEE Computer Soc., pp. 72-79.*
Wai Lam et al., A meta-learning approach for text categorization, 2001, ACM Press, pp. 303-309.*
Stefan Siersdorfer, Restrictive clustering and metaclustering for self-organization document collections, 2004, ACM Press, pp. 226-233.*
Mark Stephenson et al., Meta optimization: improving compiler heuristics with machine learning, 2003, ACM Press, pp. 77-90.*
Ricardo Vilalta & Youssef Drissi, “A Perspective Vision & Survey of Meta-Learning”; Metal tex 2002 Kluwer Acad. Pub. pp 1 to 21.
Drissi Youssef
Jeng Jun-Jang
Kim Moon Ju
Kozakov Lev
Leon-Rodriquez Juan
Herzberg Louis
Mizrahi Diane D.
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