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WO2023034328A3 - Correlating parallelized data from disparate data sources to aggregate graph data portions to predictively identify entity data - Google Patents

Correlating parallelized data from disparate data sources to aggregate graph data portions to predictively identify entity data Download PDF

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Publication number
WO2023034328A3
WO2023034328A3 PCT/US2022/042077 US2022042077W WO2023034328A3 WO 2023034328 A3 WO2023034328 A3 WO 2023034328A3 US 2022042077 W US2022042077 W US 2022042077W WO 2023034328 A3 WO2023034328 A3 WO 2023034328A3
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WO
WIPO (PCT)
Prior art keywords
data
parallelized
correlating
graph
portions
Prior art date
Legal status (The legal status 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 status listed.)
Ceased
Application number
PCT/US2022/042077
Other languages
French (fr)
Other versions
WO2023034328A2 (en
Inventor
Shawn Andrew Pardue Smith
Bryon Kristen Jacob
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Data World Inc
Original Assignee
Data World Inc
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
Priority claimed from US17/461,982 external-priority patent/US11755602B2/en
Application filed by Data World Inc filed Critical Data World Inc
Publication of WO2023034328A2 publication Critical patent/WO2023034328A2/en
Publication of WO2023034328A3 publication Critical patent/WO2023034328A3/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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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
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed 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
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • 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
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases
    • G06F16/285—Clustering or classification

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  • Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Probability & Statistics with Applications (AREA)
  • Software Systems (AREA)
  • Computational Linguistics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Debugging And Monitoring (AREA)

Abstract

Various embodiments relate generally to data science and data analysis, computer software and systems, and data-driven control systems and algorithms based on graph-based data arrangements, among other things, and, more specifically, to a computing platform configured to receive or analyze datasets in parallel by implementing, for example, parallel computing processor systems to correlate subsets of parallelized data from disparately-formatted data sources to identify entity data and to aggregate graph data portions. In some examples, a method may include classifying data parallelized data to identify a class of observation data, constructing one or more content graphs in a graph data format, correlating parallelized data to other subsets of parallelized data associated with a class of observation data; and aggregating observation data to represent an individual entity.
PCT/US2022/042077 2021-08-30 2022-08-30 Correlating parallelized data from disparate data sources to aggregate graph data portions to predictively identify entity data Ceased WO2023034328A2 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US17/461,982 2021-08-30
US17/461,982 US11755602B2 (en) 2016-06-19 2021-08-30 Correlating parallelized data from disparate data sources to aggregate graph data portions to predictively identify entity data

Publications (2)

Publication Number Publication Date
WO2023034328A2 WO2023034328A2 (en) 2023-03-09
WO2023034328A3 true WO2023034328A3 (en) 2023-04-13

Family

ID=85413050

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2022/042077 Ceased WO2023034328A2 (en) 2021-08-30 2022-08-30 Correlating parallelized data from disparate data sources to aggregate graph data portions to predictively identify entity data

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WO (1) WO2023034328A2 (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10515085B2 (en) 2016-06-19 2019-12-24 Data.World, Inc. Consolidator platform to implement collaborative datasets via distributed computer networks
US10853376B2 (en) 2016-06-19 2020-12-01 Data.World, Inc. Collaborative dataset consolidation via distributed computer networks
US11068453B2 (en) 2017-03-09 2021-07-20 data.world, Inc Determining a degree of similarity of a subset of tabular data arrangements to subsets of graph data arrangements at ingestion into a data-driven collaborative dataset platform
US11888910B1 (en) * 2022-09-15 2024-01-30 Neptyne Inc System to provide a joint spreadsheet and electronic notebook interface

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030229652A1 (en) * 2000-02-28 2003-12-11 Reuven Bakalash Enterprise-wide data-warehouse with integrated data aggregation engine
US20080162409A1 (en) * 2006-12-27 2008-07-03 Microsoft Corporation Iterate-aggregate query parallelization
US20120011144A1 (en) * 2010-07-12 2012-01-12 Frederik Transier Aggregation in parallel computation environments with shared memory
US20140143760A1 (en) * 2012-11-16 2014-05-22 Ab Initio Technology Llc Dynamic graph performance monitoring
US20140297665A1 (en) * 2013-03-15 2014-10-02 Akuda Labs Llc Optimization for Real-Time, Parallel Execution of Models for Extracting High-Value Information from Data Streams

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030229652A1 (en) * 2000-02-28 2003-12-11 Reuven Bakalash Enterprise-wide data-warehouse with integrated data aggregation engine
US20080162409A1 (en) * 2006-12-27 2008-07-03 Microsoft Corporation Iterate-aggregate query parallelization
US20120011144A1 (en) * 2010-07-12 2012-01-12 Frederik Transier Aggregation in parallel computation environments with shared memory
US20140143760A1 (en) * 2012-11-16 2014-05-22 Ab Initio Technology Llc Dynamic graph performance monitoring
US20140297665A1 (en) * 2013-03-15 2014-10-02 Akuda Labs Llc Optimization for Real-Time, Parallel Execution of Models for Extracting High-Value Information from Data Streams

Also Published As

Publication number Publication date
WO2023034328A2 (en) 2023-03-09

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