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US Patent 10331380 Scalable distributed in-memory computation utilizing batch mode extensions

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10331380
Date of Patent
June 25, 2019
Patent Application Number
15582743
Date Filed
April 30, 2017
Patent Citations
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US Patent 10015106 Multi-cluster distributed data processing platform
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US Patent 10127352 Distributed data processing platform for metagenomic monitoring and characterization
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US Patent 10122806 Distributed analytics platform
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US Patent 10111492 Article of footwear with dynamic edge cavity midsole
Patent Citations Received
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US Patent 12131044 Intelligent application placement in a hybrid infrastructure
7
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US Patent 11874817 Optimizing distributed and parallelized batch data processing
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US Patent 11474881 Optimizing distributed and parallelized batch data processing
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US Patent 10496926 Analytics platform for scalable distributed computations
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US Patent 10505863 Multi-framework distributed computation
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US Patent 10509684 Blockchain integration for scalable distributed computations
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US Patent 10511659 Global benchmarking and statistical analysis at scale
...
Patent Primary Examiner
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Jerry B Dennison
Patent abstract

An apparatus in one embodiment comprises at least one processing device having a processor coupled to a memory. The processing device is configured to distribute in-memory computations across a plurality of data processing clusters associated with respective data zones, and to combine local processing results of the distributed in-memory computations from the data processing clusters. The distributed in-memory computations utilize local data structures of respective ones of the data processing clusters. A given one of the local data structures in one of the data processing clusters receives local data of the corresponding data zone and is utilized to generate the local processing results of that data processing cluster that are combined with local processing results of other ones of the data processing clusters. The local data structures are configured to support batch mode extensions such as Spark SQL, Spark MLlib or Spark GraphX for performance of the distributed in-memory computations.

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