Patent application EP17160100A (EP), filed 2017-03-09
Publication number: EP3217334A1
Title: SYSTEM AND METHOD FOR VISUAL BAYESIAN DATA FUSION
Abstract: System and method for visual Bayesian data fusion are disclosed. In an example, a plurality of datasets associated with a topic are obtained from a data lake. Each of the plurality of datasets include information corresponding to various attributes of the topic. Further, the plurality of datasets are joined to obtain a joined dataset. Furthermore, distribution associated with a target attribute is predicted using Bayesian modeling by selecting a plurality of attributes (k) based on mutual information with the target attribute in the joined dataset, learning a minimum spanning tree based Bayesian structure using the selected attributes and the target attribute, learning conditional probabilistic tables at each node of the minimum spanning tree based Bayesian structure; and predicting the distribution associated with the target attribute by querying the conditional probabilistic tables, thereby facilitating visual Bayesian data fusion.
IPC codes: G06F  17/    30            A I
G06N   3/    08            A I
G06N  20/    00            A I
G06N   7/    00            A I
G06F  16/  2455            A I
G06F  16/  2458            A I
G06F  16/    25            A I
G06F  16/    26            A I
Claims: number: 1
text: A processor-implemented method comprising:
number: 2
text: The method according to claim 1, wherein the plurality of datasets are joined based on a type of join and wherein the type of join comprises an inner join, an outer join, a left join, and a right join.
number: 3
text: The method according to claim 1, wherein learning a minimum spanning tree based Bayesian structure using the selected attributes and the target attribute comprises:
number: 4
text: The method according to claim 1, wherein the plurality of attributes and the target attribute comprise discrete and continuous variables.
number: 5
text: The method according to claim 4, wherein learning the conditional probabilistic tables at each node of the minimum spanning tree based Bayesian structure comprises:
number: 6
text: The method according to claim 1, further comprising:
number: 7
text: A system (100) comprising:
number: 8
text: The system according to claim 7, wherein the plurality of datasets are joined based on a type of join and wherein the type of join comprises an inner join, an outer join, a left join, and a right join.
number: 9
text: The system according to claim 7, wherein one or more hardware processors are capable of executing programmed instructions to:
number: 10
text: The system according to claim 7, wherein the plurality of attributes and the target attribute comprise discrete and continuous variables.
number: 11
text: The system according to claim 10, wherein one or more hardware processors are capable of executing programmed instructions to:
number: 12
text: The system according to claim 7, wherein one or more hardware processors are further capable of executing programmed instructions to:
number: 13
text: A non-transitory computer readable medium embodying a program executable in a computing device, said program comprising:
Office action: search_report, dated 2017-09-13
Rejection reasons: novelty_epc_54
inventive_step_epc_56
Prior art cited: ref_id: CN104598707A
ref_type: patent
severity: background
ref_id: - KARAMJIT SINGH ET AL: "Minimally-Supervised Attribute Fusion for Data Lakes", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 4 January 2017 (2017-01-04), XP080739566
ref_type: npl
severity: background
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ref_type: npl
severity: background
ref_id: US2005216496A1
ref_type: patent
severity: background
ref_id: US8447710B1
ref_type: patent
severity: background
ref_id: - SURYA YADAV ET AL: "Business data fusion", 18TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION, 6 July 2015 (2015-07-06), Piscataway, pages 1876 - 1885, XP055359001, ISBN: 978-0-9824438-6-6
ref_type: npl
severity: background
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ref_type: npl
severity: background
ref_id: - SINGH KARAMJIT ET AL: "Visual Bayesian fusion to navigate a data lake", 2016 19TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), ISIF, 5 July 2016 (2016-07-05), pages 987 - 994, XP032935117
ref_type: npl
severity: background
ref_id: - AGARWAL PUNEET ET AL: "Approximate Incremental Big-Data Harmonization", 2013 IEEE INTERNATIONAL CONGRESS ON BIG DATA, IEEE, 27 June 2013 (2013-06-27), pages 118 - 125, XP032481274, DOI: 10.1109/BIGDATA.CONGRESS.2013.24
ref_type: npl
severity: background
ref_id: US2005216496
ref_type: patent
severity: background
ref_id: US8447710
ref_type: patent
severity: background
ref_id: - SURYA YADAV ET AL, "Business data fusion", 18TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION, Piscataway, (20150706), ISBN 978-0-9824438-6-6, pages 1876 - 1885, XP055359001 [Y] 1-13 * abstract * * page  1876, column  l, line  1  - page  1882, column  r, line  l *
ref_type: npl
severity: obviousness
ref_id: - WAI LAM ET AL, "LEARNING BAYESIAN BELIEF NETWORKS: AN APPROACH BASED ON THE MDL PRINCIPLE", COMPUTATIONAL INTELLIGENCE, (19940801), vol. 10, no. 3, doi:10.1111/j.1467-8640.1994.tb00166.x, ISSN 0824-7935, pages 269 - 293, XP055390778 [Y] 1-13 * abstract * * page  278, line  18  - page  284, line  16 * * page  286, line  7  - line  14 *
ref_type: npl
severity: obviousness
ref_id: - SINGH KARAMJIT ET AL, "Visual Bayesian fusion to navigate a data lake", 2016 19TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), ISIF, (20160705), pages 987 - 994, XP032935117 [XP] 1-13 * abstract * * page  987, column  l, line  1 *
ref_type: npl
severity: novelty_destroying
ref_id: - AGARWAL PUNEET ET AL, "Approximate Incremental Big-Data Harmonization", 2013 IEEE INTERNATIONAL CONGRESS ON BIG DATA, IEEE, (20130627), doi:10.1109/BIGDATA.CONGRESS.2013.24, pages 118 - 125, XP032481274 [A] 6,12 * abstract * * page  120, column  l, line  1  - page  122, column  r, line  l *
ref_type: npl
severity: background
Final disposition: pending (2017-09-13)