Apache AVRO, Apache Parquet and XML Teradata SQL Other DB SQL Apache MADlib ML/Statistics/Graph Python. R, Java, Perl, C Programmatic Apache SOLR Text PostGIS GeoSpatial Custom Apps thegiac.com Greenplum: 机器学习工具集 2017.thegiac.com • PL/X：各种语言实现自定义函数（存储过程） • MADLib: 数据挖掘、统计分析、图（Graph）等算法 • GPText：文本检索和分析 • GeoSpatial：地理信息数据分析 • Image: 图像数据分析 Greenplum 机器器学习⼯工具集 2017.thegiac Categorical Variables Pivot Stemming Sept 2017 Graph All Pairs Shortest Path (APSP) Breadth-First Search Average Path Length Closeness Centrality Graph Diameter In-Out Degree PageRank Single Source

Bayes Classification 393 References 397 Graph Analytics 397 Graph Analytics 0 What is a Graph? 397 Graph Analytics on Greenplum 398 Using Graph 398 Graph Modules 400 All Pairs Shortest Path (APSP) Weakly Connected Components 402 Measures 402 Average Path Length 402 Closeness Centrality 403 Graph Diameter 403 In-Out Degree 403 References 403 Geospatial Analytics 404 Geospatial Analytics 0 capabilities allow you to: Analyze a multitude of data types – structured, text, geospatial, and graph – in a single environment, which can scale to petabytes and run algorithms designed for parallelism

Bayes Classification 402 References 406 Graph Analytics 406 Graph Analytics 0 What is a Graph? 406 Graph Analytics on Greenplum 407 Using Graph 407 Graph Modules 409 All Pairs Shortest Path (APSP) Weakly Connected Components 411 Measures 411 Average Path Length 411 Closeness Centrality 412 Graph Diameter 412 In-Out Degree 412 References 412 Geospatial Analytics 413 Geospatial Analytics 0 capabilities allow you to: Analyze a multitude of data types – structured, text, geospatial, and graph – in a single environment, which can scale to petabytes and run algorithms designed for parallelism

Bayes Classification 328 References 332 Graph Analytics 332 Graph Analytics 0 What is a Graph? 332 Graph Analytics on Greenplum 333 Using Graph 333 Graph Modules 335 All Pairs Shortest Path (APSP) Weakly Connected Components 337 Measures 337 Average Path Length 337 Closeness Centrality 338 Graph Diameter 338 VMware Greenplum v6.17 Documentation VMware, Inc. 15 In-Out Degree 338 References capabilities allow you to: Analyze a multitude of data types – structured, text, geospatial, and graph – in a single environment, which can scale to petabytes and run algorithms designed for parallelism

Tanzu Greenplum v6.20 Documentation VMware, Inc. 18 Graph Analytics 401 What is a Graph? 401 Graph Analytics on Greenplum 402 Using Graph 403 Graph Modules 404 All Pairs Shortest Path (APSP) 404 Breadth-First Weakly Connected Components 406 Measures 406 Average Path Length 406 Closeness Centrality 407 Graph Diameter 407 In-Out Degree 407 References 408 Geospatial Analytics 408 About PostGIS 408 Greenplum capabilities allow you to: Analyze a multitude of data types – structured, text, geospatial, and graph – in a single environment, which can scale to petabytes and run algorithms designed for parallelism

714 Naive Bayes Classification 715 References 719 Graph Analytics 719 What is a Graph? 720 Graph Analytics on Greenplum 721 Using Graph 721 Graph Modules 723 All Pairs Shortest Path (APSP) 723 Breadth-First Weakly Connected Components 725 Measures 725 Average Path Length 725 Closeness Centrality 725 Graph Diameter 726 In-Out Degree 726 References 726 Geospatial Analytics 726 Greenplum PostGIS Extension sales_summary (seller_no, invoice_date); The materialized view might be useful for displaying a graph in the dashboard created for sales people. You could schedule a job to update the summary information

914 Naive Bayes Classification 916 References 920 Graph Analytics 920 What is a Graph? 920 Graph Analytics on Greenplum 921 Using Graph 922 Graph Modules 923 All Pairs Shortest Path (APSP) 923 Breadth-First Weakly Connected Components 925 Measures 925 Average Path Length 926 Closeness Centrality 926 Graph Diameter 926 In-Out Degree 926 References 927 Geospatial Analytics 927 About PostGIS 927 Greenplum sales_summary (seller_no, invoice_date); The materialized view might be useful for displaying a graph in the dashboard created for sales people. You could schedule a job to update the summary information

762 Naive Bayes Classification 763 References 767 Graph Analytics 767 What is a Graph? 767 Graph Analytics on Greenplum 768 Using Graph 768 Graph Modules 770 All Pairs Shortest Path (APSP) 770 Breadth-First Weakly Connected Components 772 Measures 772 Average Path Length 772 Closeness Centrality 772 Graph Diameter 772 In-Out Degree 773 References 773 Geospatial Analytics 773 About PostGIS 773 Greenplum sales_summary (seller_no, invoice_date); The materialized view might be useful for displaying a graph in the dashboard created for sales people. You could schedule a job to update the summary information

Classification 905 References 909 Graph Analytics 909 VMware Greenplum 6 Documentation VMware, Inc 37 What is a Graph? 910 Graph Analytics on Greenplum 910 Using Graph 911 Graph Modules 913 All Pairs Shortest Weakly Connected Components 915 Measures 915 Average Path Length 915 Closeness Centrality 915 Graph Diameter 916 In-Out Degree 916 References 916 Geospatial Analytics 916 About PostGIS 917 Greenplum sales_summary (seller_no, invoice_date); The materialized view might be useful for displaying a graph in the dashboard created for sales people. You could schedule a job to update the summary information

875 Naive Bayes Classification 877 References 881 Graph Analytics 881 What is a Graph? 881 Graph Analytics on Greenplum 882 Using Graph 883 Graph Modules 884 All Pairs Shortest Path (APSP) 884 Breadth-First Weakly Connected Components 886 Measures 886 Average Path Length 887 Closeness Centrality 887 Graph Diameter 887 In-Out Degree 887 References 888 Geospatial Analytics 888 About PostGIS 888 Greenplum sales_summary (seller_no, invoice_date); The materialized view might be useful for displaying a graph in the dashboard created for sales people. You could schedule a job to update the summary information