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MX2017003188A - Sistema y procedimiento de fusion visual de datos bayesianos. - Google Patents

Sistema y procedimiento de fusion visual de datos bayesianos.

Info

Publication number
MX2017003188A
MX2017003188A MX2017003188A MX2017003188A MX2017003188A MX 2017003188 A MX2017003188 A MX 2017003188A MX 2017003188 A MX2017003188 A MX 2017003188A MX 2017003188 A MX2017003188 A MX 2017003188A MX 2017003188 A MX2017003188 A MX 2017003188A
Authority
MX
Mexico
Prior art keywords
bayesian
data
objective attribute
visual fusion
procedure
Prior art date
Application number
MX2017003188A
Other languages
English (en)
Other versions
MX390124B (es
Inventor
Gautam Shroff
Geetika Sharma
Aditeya Pandey
Singh Karamjit
Gupta Garima
Agarwal Puneet
Ashokbhai Paneri Kaushal
Sehgal Gunjan
Original Assignee
Tata Consultancy Services Ltd
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 Tata Consultancy Services Ltd filed Critical Tata Consultancy Services Ltd
Publication of MX2017003188A publication Critical patent/MX2017003188A/es
Publication of MX390124B publication Critical patent/MX390124B/es

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2455—Query execution
    • G06F16/24553—Query execution of query operations
    • G06F16/24558—Binary matching operations
    • G06F16/2456—Join operations
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2462—Approximate or statistical queries
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/248—Presentation of query results
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • G06F16/256—Integrating or interfacing systems involving database management systems in federated or virtual databases
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/26—Visual data mining; Browsing structured data
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/901—Indexing; Data structures therefor; Storage structures
    • G06F16/9027—Trees
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Probability & Statistics with Applications (AREA)
  • Artificial Intelligence (AREA)
  • Computing Systems (AREA)
  • Evolutionary Computation (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Mathematical Analysis (AREA)
  • Algebra (AREA)
  • Mathematical Optimization (AREA)
  • Computational Mathematics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Fuzzy Systems (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Image Analysis (AREA)

Abstract

Se describe un sistema y procedimiento para la fusión visual de datos bayesianos. Como ejemplo, se obtiene una pluralidad de conjuntos de datos asociados con un tema a partir de un lago de datos. Cada una de las pluralidades de conjuntos de datos incluye información correspondiente a varios atributos del tema. Además, la pluralidad de conjuntos de datos se une para obtener un conjunto de datos unido. Por otra parte, la distribución asociada con un atributo objetivo se predice utilizando modelado bayesiano seleccionando una pluralidad de atributos (k) en base a la información mutua, con el atributo objetivo en el conjunto de datos unido; aprendiendo una estructura bayesiana basada en árbol de expansión mínima usando los atributos seleccionados y el atributo objetivo; aprendiendo las tablas de probabilidad condicional en cada nodo de la estructura bayesiana basada en árbol de expansión mínima; y prediciendo la distribución asociada con el atributo objetivo mediante la consulta de las tablas de probabilidad condicional, lo que facilita la fusión visual de datos bayesianos. [Se deberá publicar con la figura 4].
MX2017003188A 2016-03-10 2017-03-10 Sistema y procedimiento de fusion visual de datos bayesianos. MX390124B (es)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
IN201621008419 2016-03-10

Publications (2)

Publication Number Publication Date
MX2017003188A true MX2017003188A (es) 2018-08-15
MX390124B MX390124B (es) 2025-03-04

Family

ID=58277171

Family Applications (1)

Application Number Title Priority Date Filing Date
MX2017003188A MX390124B (es) 2016-03-10 2017-03-10 Sistema y procedimiento de fusion visual de datos bayesianos.

Country Status (6)

Country Link
US (1) US10430417B2 (es)
EP (1) EP3217334B1 (es)
JP (1) JP6431945B2 (es)
AU (1) AU2017201653B2 (es)
CA (1) CA2960505C (es)
MX (1) MX390124B (es)

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EP3217334B1 (en) * 2016-03-10 2021-09-29 Tata Consultancy Services Limited System and method for visual bayesian data fusion
CN110347688B (zh) * 2019-07-10 2020-09-11 星环信息科技（上海）有限公司 多元信息的特征融合方法、装置、设备及存储介质
US11250014B2 (en) 2019-08-18 2022-02-15 International Business Machines Corporation Generating previews of possible join relationships
JP2021033895A (ja) * 2019-08-29 2021-03-01 株式会社豊田中央研究所 変数選定方法、変数選定プログラムおよび変数選定システム
CN110705132B (zh) * 2019-10-31 2023-04-28 哈尔滨工业大学 一种基于多源异质数据的制导控制系统性能融合评估方法
CN110837868A (zh) * 2019-11-08 2020-02-25 奇瑞汽车股份有限公司 贝叶斯图的获取方法及装置、存储介质
CN111221794A (zh) * 2020-02-17 2020-06-02 湖南工学院 配电网广域量测控制系统多源数据融合方法
CN112231313A (zh) * 2020-11-05 2021-01-15 河钢数字技术股份有限公司 一种钢厂多源环境数据融合的方法
CN113111284B (zh) * 2021-04-12 2024-07-16 中国铁塔股份有限公司 归类信息展示方法、装置、电子设备和可读存储介质
CN113626482A (zh) * 2021-08-17 2021-11-09 北京深演智能科技股份有限公司 基于系统融合id表的查询方法和装置
CN113807453B (zh) * 2021-09-24 2024-01-30 沈阳理工大学 基于加权概率融合并行贝叶斯网络的异常行为检测方法
CN114741510B (zh) * 2022-04-06 2025-01-21 中山大学 基于无监督学习聚类算法的数据空间异构数据自动化融合的方法
CN114896426B (zh) * 2022-07-14 2023-10-13 中国人民解放军国防科技大学 一种电子目标认知图谱的构建方法
JP7260704B1 (ja) * 2022-08-29 2023-04-18 株式会社博報堂Ｄｙホールディングス 情報処理システム、コンピュータプログラム、及び情報処理方法
US12094039B1 (en) * 2022-11-01 2024-09-17 Bentley Systems, Incorporated User interface for visualizing high-dimensional datasets
CN116153450B (zh) * 2023-04-13 2023-06-27 合肥科颖医药科技有限公司 基于智能分析的访视内容数据比对方法及系统
CN117009921B (zh) * 2023-08-04 2024-02-23 振宁(无锡)智能科技有限公司 一种数据融合引擎的优化数据处理方法及系统
CN117056826B (zh) * 2023-08-14 2025-12-16 江苏大学 一种基于贝叶斯理论的金属回收率预测方法、系统
CN118395388B (zh) * 2024-06-27 2024-09-24 广东海洋大学 面向农业温室环境的icdo-rbf多传感器数据融合方法

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Also Published As

Publication number Publication date
CA2960505A1 (en) 2017-09-10
EP3217334A1 (en) 2017-09-13
EP3217334B1 (en) 2021-09-29
US20170262506A1 (en) 2017-09-14
JP2017199358A (ja) 2017-11-02
CA2960505C (en) 2018-12-11
US10430417B2 (en) 2019-10-01
JP6431945B2 (ja) 2018-11-28
MX390124B (es) 2025-03-04
AU2017201653A1 (en) 2017-09-28
AU2017201653B2 (en) 2018-11-08
BR102017004835A2 (pt) 2018-03-20

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