Introduction To Graph Analytics And Oracle Cloud Service

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Spatial and Graph Summit @Introduction to Graph Analytics and Oracle Cloud ServiceHans ViehmannProduct Manager EMEAOracle@SpatialHannesJean IhmProduct Manager USOracle@JeanIhmAnalytics and Data Summit 2019

Spatial and Graph Sessions 25 Spatial and Graph related sessions See yellow track on agenda Room 103 for most sessions Tuesday: Morning: Graph technical sessions Afternoon: Spatial technical sessions, Graph hands on lab Wednesday: Morning: Spatial use cases Afternoon: Graph use cases & Spatial sessions for developers Thursday: Morning: Graph tech sessions & use cases (RDF & property graph) Afternoon: Spatial - analytics & big data focusAnalytics and Data Summit 2019

Safe Harbor StatementThe following is intended to outline our general product direction. It is intended forinformation purposes only, and may not be incorporated into any contract. It is not acommitment to deliver any material, code, or functionality, and should not be relied uponin making purchasing decisions. The development, release, timing, and pricing of anyfeatures or functionality described for Oracle’s products may change and remains at thesole discretion of Oracle Corporation.Copyright 2019, Oracle and/or its affiliates. All rights reserved. 3

Program Agenda1Product Introduction2Use Cases3Feature Overview4DemoCopyright 2019, Oracle and/or its affiliates. All rights reserved. 4

Graph – an important growth area for data & analyticsSource: Gartner press release, 2/18/2019, chnoloCopyright 2019, Oracle and/or its affiliates. All rights reserved. Confidential – Oracle Internal/Restricted/Highly Restricted5

Following, no follow backFollower, no follow backFollow each otherhttps://twitter.jeffprod.comCopyright 2019, Oracle and/or its affiliates. All rights reserved. 6

Oracle’s Spatial and Graph StrategyEnabling Spatial and Graph use cases on every platformOracle DatabaseSpatial and Graph OptionExadataNon-Engineered SystemsOracle Big DataSpatial and GraphCloudServicesDatabase Cloud ServiceExadata Cloud ServiceBig Data ApplianceCommodity HadoopSparkCopyright 2019, Oracle and/or its affiliates. All rights reserved. 7

Two Graph Data ModelsProperty Graph ModelSocial NetworkAnalysisLinked DataKnowledgeGraphsUse Case Path Analytics Social Network Analysis Entity analyticsRDF Data Model Data federation Knowledge representationGraph Model FinancialRetail, MarketingSocial MediaSmart Manufacturing Life SciencesHealth CarePublishingFinanceIndustry DomainRDF Knowledge Graph Sessions: Technical overview, use cases on KnowledgeGraphs and BIM for Engineering (Bechtel) – Thursday 8:45 – 10:40amCopyright 2019, Oracle and/or its affiliates. All rights reserved.

Graph Database Features: Scalability, Performance, Security Graph Analytics Graph Query Language Graph VisualizationCourtesy Linkurious Standard Interfaces Integration with Machine Learning toolsCourtesy Tom Sawyer PerspectivesCopyright 2019, Oracle and/or its affiliates. All rights reserved. 9

Oracle Products Supporting Property GraphsOracle Big Data Spatial and GraphOracle Spatial and Graph (DB option) Available for Big Data platform Available with Oracle 12.2 andabove (EE) Supported both on BDA andcommodity hardware Using tables for graph persistence– Cloudera Distribution for Hadoop In-database graph analytics Database connectivity through BigData Connectors or Big Data SQL Part of Big Data Cloud Service– Sparsification, shortest path, page rank,triangle counting, WCC, sub graphgeneration SQL and PGQL queries possible Included in Database Cloud ServicesCopyright 2019, Oracle and/or its affiliates. All rights reserved. 10

Use CasesCopyright 2018, Oracle and/or its affiliates. All rights reserved. 11

Graph Analysis for Business InsightIdentifyInfluencersDiscover Graph Patternsin Big DataCopyright 2019, Oracle and/or its affiliates. All rights reserved. GenerateRecommendations12

Banco de Galicia Customer profitability analysis– Part of larger Hadoop/Big Data project Analysis of banking transactions– Focus on corporate customers Identification of undesired behaviouralpatterns, eg.– Customers using other banks to make largenumbers of transactions– Many of which flow back to Banco Galicia Increase fees, terminate contracts, ormove activities to Banco Galicia Implemented by Oracle ConsultingCopyright 2019, Oracle and/or its affiliates. All rights reserved. 13

Romanian Police Force Creating Knowledge Graphs from all kindsof contentBIG DATA since 2012– Social media networks, documents, images,audio, video, structured data– Using machine learning (text analysis,classification, entity extraction, facerecognition, speech2text, .) Enabling relationship analysis andsemantic search bigCONNECT platform built by mWARE– Running on Big Data Applicance, Big Data CloudService or commodity HadoopCopyright 2019, Oracle and/or its affiliates. All rights reserved. 14

Ministry of Finance, Eastern Europe Detecting relationships between people,accounts, companies Ingesting accounting data in SAF-T format– Hadoop-based processing (Oozie, Spark, Hive)– Terabytes of data, rapidly growingImporter„fake” companyTax authorityBufferResellerTax authorityExportercompany?BORDER 0% VAT BORDER 0% VAT BORDER 0% VAT BORDERCompany inother EU membercountryGettingVAT refund– Circular money transfers– Connections (existing path/shortest path) tocompanies in tax havensPaying VAT Identifying suspicious patternsNot paying VAT– Similar to Paradise PapersBORDER 0% VAT BORDER 0% VAT BORDER 0% VAT BORDEREU VAT fraudCompany inother EU membercountryTax authority Interactive graph analysis in Apex withCytoscape.jsCopyright 2019, Oracle and/or its affiliates. All rights reserved. 15

Mazda Management of Bill-of-materials– Automotive manufacturing process– Supporting high variance and shortinnovation cycles Data coming from various sources Complex PGQL queries to associateparts and subcomponents– Performance as key requirement– Happy with response times andscaleabilityCopyright 2019, Oracle and/or its affiliates. All rights reserved. 16

Paysafe Providing online payment solutions– Real-time payments, e-Wallets– 1bn revenue/yr– 500000 payments/day Strong demand for fraud detection––––Only feasible with graph dataIn real-time, upon money movementDuring account creationIn investigation, visualizing payment flows Storing payments in database– Refreshing graph using delta updateCopyright 2019, Oracle and/or its affiliates. All rights reserved. Oracle Confidential – Internal17

Graph use case sessions at AnD Summit ’19 Wednesday– Using Graph Analysis for Fraud Detection in Fintech at Paysafe – S. Dalekova/Y. Ivanov,Paysafe – 1:00pm– Building Consistent Crime Investigation Practices Using Big Data and GraphTechnologies – D. Belchior/F. Ferreira, Rio Public Prosecutor’s Office – 2:20pm– Room 103Copyright 2019, Oracle and/or its affiliates. All rights reserved. Confidential – Oracle Internal/Restricted/Highly Restricted18

Feature OverviewCopyright 2018, Oracle and/or its affiliates. All rights reserved. 19

In-memory Analytic EngineJava APIsGraph Storage ManagementBlueprints & SolrCloud / LucenePython, Perl, PHP, Ruby,Javascript, Graph AnalyticsREST Web ServiceVisualizationR Integration (OAAgraph)Spark integrationOracle Graph Analytics ArchitectureJava APIs/JDBC/SQL/PLSQLScalable and Persistent StorageCopyright 2019, Oracle and/or its affiliates. All rights reserved. 20

Interacting with the GraphOn-premise product geared towards data scientists and developers Access through APIs– Implementation of Apache Tinkerpop Blueprints APIs– Based on Java, REST plus SolR Cloud/Lucene support for text search Scripting– JShell, Python, Javascript, .– Apache Zeppelin integration Graphical UIs– Property Graph Visualization component (forthcoming), Cytoscape, plug-in available– Commercial Tools such as TomSawyer PerspectivesCopyright 2019, Oracle and/or its affiliates. All rights reserved. 21

Example: Betweenness Centrality in Big Data etTopKValues(15)FJGBAIHCKDECopyright 2019, Oracle and/or its affiliates. All rights reserved. 22

Pattern matching in Property Graphs using PGQL Finding a given pattern in graph– Fraud detection– Anomaly detection– Subgraph extraction– . SQL-like syntax but with graphpattern description and propertyaccess Proposed for standardization byOracle– Specification available on-line– Open-sourced front-end (i.e. parser)https://github.com/oracle/pgql-lang– Interactive (real-time) analysis– Supporting aggregates, comparison,such as max, min, order by, group byCopyright 2019, Oracle and/or its affiliates. All rights reserved. 23

More on PGQL PGQL: A Query Language for Property Graphs – O. van Rest, Oracle –Wednesday, 3:25pmCopyright 2019, Oracle and/or its affiliates. All rights reserved. Confidential – Oracle Internal/Restricted/Highly Restricted24

Basic graph pattern matching Find all instances of a given pattern/template in the data graphSELECTFROMMATCHWHEREv3.name, v3.agesocialNetworkGraph(v1:Person) –[:friendOf]- (v2:Person) –[:knows]- (v3:Person)v1.name ‘Amber’100:Personname ‘Amber’age 25:worksAt{1831}startDate ’09/01/2015’777:friendOf{1173}:friendOf {2513}since ’08/01/2014’:Personname ‘Paul’age 30socialNetworkGraphQuery: Find all people who are knownby friends of ‘Amber’.300:knows{2200}200:Companyname ‘Oracle’location ‘Redwood City’:Personname ‘Heather’age 27Copyright 2019, Oracle and/or its affiliates. All rights reserved. 25

Basic graph pattern matching Find all instances of a given pattern/template in the data graphSELECTFROMMATCHWHEREv3.name, v3.agesocialNetworkGraph(v1:Person) –[:friendOf]- (v2:Person) –[:knows]- (v3:Person)v1.name ‘Amber’100:Personname ‘Amber’age 25:worksAt{1831}startDate ’09/01/2015’777:friendOf{1173}:friendOf {2513}since ’08/01/2014’:Personname ‘Paul’age 30socialNetworkGraphQuery: Find all people who are knownby friends of ‘Amber’.300:knows{2200}200:Companyname ‘Oracle’location ‘Redwood City’:Personname ‘Heather’age 27Copyright 2019, Oracle and/or its affiliates. All rights reserved. 26

Regular path expressions Matching a patternrepeatedly– Define a PATH expression at thetop of a query– Instantiate the expression in theMATCH clause– Match repeatedly, e.g. zero ormore times (*) or one or moretimes ( )PATHSELECTFROMMATCH,WHEREhas parent AS (child) –[:has father has mother]- (parent)x.name, y.name, ancestor.namesnGraph(x:Person) –/:has parent /- (ancestor)(y) -/:has parent /- (ancestor)x.name 'Peter' AND x ysnGraph:Personname ‘Amber’age 292100:likessince ‘2016-04-04’0:likessince ‘2016-04-04’400:Personname ‘Dwight’age 15:has father200:has father3:Person1:Personname ‘Paul’age 64300 name ‘Retta’age 434:has mother5:has mother6:likessince ‘2013-02-14’Copyright 2019, Oracle and/or its affiliates. All rights reserved. 7:likessince ‘2015-11-08’500:Personname ‘Peter’age 1227

Regular path expressions Matching a patternrepeatedly– Define a PATH expression at thetop of a queryPATHSELECTFROMMATCH,WHEREhas parent AS (child) –[:has father has mother]- (parent)x.name, y.name, ancestor.namesnGraph(x:Person) –/:has parent /- (ancestor)(y) -/:has parent /- (ancestor)x.name 'Peter' AND x y– Instantiate the expression in theMATCH clause– Match repeatedly, e.g. zero ormore times (*) or one or moretimes ( ) -------- -------- --------------- x.name y.name ancestor.name -------- -------- --------------- Peter Retta Paul Peter Dwight Paul Peter Dwight Retta -------- -------- --------------- snGraph:Personname ‘Amber’age 292100:likessince ‘2016-04-04’Result set0:likessince ‘2016-04-04’400:Personname ‘Dwight’age 15:has father200:has father3:Person1:Personname ‘Paul’age 64300 name ‘Retta’age 434:has mother5:has mother6:likessince ‘2013-02-14’Copyright 2019, Oracle and/or its affiliates. All rights reserved. 7:likessince ‘2015-11-08’500:Personname ‘Peter’age 1228

Notebook integration Multi-purpose notebook for data analysisand visualization– Browser-based script and query execution For documentation and interactiveanalysis– Typically used by Data Scientist Interpreters for graph analysis and graphpattern matching– PGX, PGQL, Markdown Graph visualization Integrated with Graph Cloud ServiceCopyright 2019, Oracle and/or its affiliates. All rights reserved. 29

DemoCopyright 2018, Oracle and/or its affiliates. All rights reserved. 30

Copyright 2019, Oracle and/or its affiliates. All rights reserved. 31

In-memory Analytic EngineJava APIsGraph Storage ManagementBlueprints & SolrCloud / LucenePython, Perl, PHP, Ruby,Javascript, Graph AnalyticsREST Web ServiceVisualizationR Integration (OAAgraph)Spark integrationOracle Graph Analytics ArchitectureJava APIs/JDBC/SQL/PLSQLScalable and Persistent StorageCopyright 2019, Oracle and/or its affiliates. All rights reserved. 32

In-memory Analytic EnginePGQL in PGXJava APIsGraph Storage ManagementBlueprints & SolrCloud / LucenePGQL-to-SQLPython, Perl, PHP, Ruby,Javascript, Graph AnalyticsREST Web ServiceVisualizationR Integration (OAAgraph)Spark integrationSupport for Graph Pattern MatchingJava APIs/JDBC/SQL/PLSQLScalable and Persistent StorageCopyright 2019, Oracle and/or its affiliates. All rights reserved. 33

Path Query (Parallel Recursive With)PGQL:PATH knows path : () -[:knows]- ()SELECT s1.fname, s2.fnameWHERE (s1) -/:knows path*/- (o) -/:knows path*/-(s2)ORDER BY s1,s2Find the pairs of people who areconnected to a common personthrough the “knows” relationSQL:SELECT T2.T AS "s1.fname T",T2.V AS "s1.fname V",T2.VN AS "s1.fname VN",T2.VT AS "s1.fname VT",T3.T AS "s2.fname T",T3.V AS "s2.fname V",T3.VN AS "s2.fname VN",T3.VT AS "s2.fname VT"FROM (/*Path[*/SELECT DISTINCT SVID, DVID FROM ( SELECT VID AS SVID, VID AS DVID FROM "GRAPH1VT " UNION ALL SELECTFROM (WITH RW (ROOT, SVID, DVID, LVL) AS ( SELECT ROOT, SVID, DVID, LVL FROM (SELECT SVID ROOT, SVID, DVID,FROM (SELECT T0.SVID AS SVID, T0.DVID AS DVID FROM "GRAPH1GT " T0 WHERE (T0.EL n'knows'))) UNION ALL SELECT DISTINCT RW.ROOT, R.SVID, R.DVID, RW.LVL 1 FROM (SELECT T1.SVID AS SVID,T1.DVID AS DVID FROM "GRAPH1GT " T1 WHERE (T1.EL n'knows')) R, RW WHERE RW.DVID R.SVID )CYCLE SVID SET cycle col TO 1 DEFAULT 0 SELECT ROOT SVID, DVID FROM RW ))/*]Path*/) T6,(/*Path[*/SELECT DISTINCT SVID, DVID FROM ( SELECT VID AS SVID, VID AS DVID FROM "GRAPH1VT " UNION ALL SELECTFROM (WITH RW (ROOT, SVID, DVID, LVL) AS ( SELECT ROOT, SVID, DVID, LVL FROM (SELECT SVID ROOT, SVID, DVID,FROM (SELECT T4.SVID AS SVID, T4.DVID AS DVID FROM "GRAPH1GT " T4 WHERE (T4.EL n'knows'))) UNION ALL SELECT DISTINCT RW.ROOT, R.SVID, R.DVID, RW.LVL 1 FROM (SELECT T5.SVID AS SVID,T5.DVID AS DVID FROM "GRAPH1GT " T5 WHERE (T5.EL n'knows')) R, RW WHERE RW.DVID R.SVID )CYCLE SVID SET cycle col TO 1 DEFAULT 0 SELECT ROOT SVID, DVID FROM RW ))/*]Path*/) T7,"GRAPH1VT " T2, "GRAPH1VT " T3WHERE T2.K n'fname' AND T3.K n'fname' AND T6.SVID T2.VID AND T6.DVID T7.DVID AND T7.SVID T3.VIDORDER BY T6.SVID ASC NULLS LAST, T7.SVID ASC NULLS LASTCopyright 2019, Oracle and/or its affiliates. All rights reserved. SVID,DVID1 LVLSVID,DVID1 LVL34

Combining Graph Analytics and Machine LearningGraph Analytics Compute graph metric(s) Explore graph or computenew metrics using ML resultMachine LearningAdd tostructured dataAdd to graph Build predictive modelusing graph metric Build model(s) andscore or classify dataCopyright 2019, Oracle and/or its affiliates. All rights reserved. 35

Machine learning session When Graphs Meet Machine Learning – S. Hong/R. Patra, Oracle –Thursday, 10:55amCopyright 2019, Oracle and/or its affiliates. All rights reserved. Confidential – Oracle Internal/Restricted/Highly Restricted36

OAAgraph integration with R OAAgraph integrates in-memory engine into ORE and ORAAH Adds powerful graph analytics and querying capabilities to existinganalytical portfolio of ORE and ORAAH Built in algorithms of PGX available as R functions PGQL pattern matching Concept of “cursor” allows browsing of in-memory analytical results usingR data structures (R data frame), allows further client-side processing in R Exporting data back to Database / Spark allows persistence of results andfurther processing using existing ORE and ORAAH analytical functionsCopyright 2019, Oracle and/or its affiliates. All rights reserved.

Graph Analytics on SPARK vs. GraphX Use SPARK for conventional tabulardata processing (RDD, Dataframe, -set) Define graph view of the dataPGXSPARKPGXSPARK– View it as node table and edge table Load into PGX Execute graph algorithms in PGX– Orders of magnitude faster than GraphX– More scaleable Push analysis results back into SPARKas additional tables Continue SPARK analysisSPARKSPARK data structure andcommunication mechanism notoptimized for graph analysis workloadsCopyright 2019, Oracle and/or its affiliates. All rights reserved. 38

Property Graph Visualization Tool (new) Lightweight visualizationcomponent Single-Page Web Application basedon Oracle JET and D3.js Takes PGQL Query as input, rendersresult set visually Will support PGQL-to-PGX initially,but can work with anything thatsupports PGQL (including PGQL-toSQL)Copyright 2019, Oracle and/or its affiliates. All rights reserved. Confidential – Oracle Internal/Restricted/Highly Restricted40

Graph Visualization – Commercial ToolsSee Tom Sawyer vis demo: Partner lightning round – Tuesday 12:00pm (Auditorium),Finding Malicious Network Packets Using Anomaly Detection with Graph Analytics –Thursday, 12:00pmCopyright 2019, Oracle and/or its affiliates. All rights reserved. 41

Distributed Graph Analysis EngineHandling extremely large graphs Oracle Big Data Spatial and Graph uses very compact graph representation– Can fit graph with 23bn edges into one BDA node Distributed implementation scales beyond this– Processing even larger graphs with several machines in a cluster (scale-out)– Interconnected through fast network (Ethernet or, ideally, Infiniband) Integrated with YARN for resource management– Same client interface, but not all APIs implemented yet Again, much faster than other implementations– Comprehensive performance comparison with GraphX, GraphLabCopyright 2019, Oracle and/or its affiliates. All rights reserved.

Graph Cloud ServiceFully managed graph cloud service “One-click” deployment: no installation, zero configuration– Automated failure detection and recovery Automated graph modeler– Easily convert your relational data into property graphs Pre-built algorithms, flows and interactive queries– JavaSession: Graph Cloud Preview: How toAnalyze Data Warehouse Data as a Graph –K. Schmid/J. Sharma, Oracle – Tuesday11:15am– PGQL– Rest APIs Rich User Interface– Low code / zero code features– Notebook support and powerful data visualization featuresCopyright 2019, Oracle and/or its affiliates. All rights reserved.

SummaryGraph capabilities in Oracle Database and Big Data Spatial and Graph Graph databases are powerful tools, complementing relational databases– Especially strong for analysis of graph topology and multi-hop relationships Graph analytics offer new insight– Especially relationships, dependencies and behavioural patterns Oracle Property Graph technology offers– Comprehensive analytics through various APIs, integration with relational database– Scaleable, parallel in-memory processing– Secure and scaleable graph storage using Oracle Database or Big Data Platform Available both on-premise or in the CloudCopyright 2019, Oracle and/or its affiliates. All rights reserved. 44

Graph sessions at AnD Summit ’19 TuesdayAll sessions in room 103unless otherwise noted– Graph Cloud Preview: How to Analyze Data Warehouse Data as a Graph – K. Schimd/J.Sharma, Oracle–11:15am– Hands On Lab: Introduction to Property Graphs in Oracle Databases – K. Hare, JCCConsulting – 3:35pm room 202 Wednesday– Using Graph Analysis for Fraud Detection in Fintech at Paysafe – S. Dalekova/Y. Ivanov,Paysafe – 1:00pm– Building Consistent Crime Investigation Practices Using Big Data and GraphTechnologies – D. Belchior/F. Ferreira, Rio Public Prosecutor’s Office – 2:20pm– PGQL: A Query Language for Property Graphs – O. van Rest, Oracle – 3:25pm– Translating Natural Language to Graph Queries for Financial Crime Investigation – M.Brantner, Oracle – 4:30pmCopyright 2019, Oracle and/or its affiliates. All rights reserved. Confidential – Oracle Internal/Restricted/Highly Restricted45

Graph sessions at AnD Summit ’19 (cont.)All sessions in room 103unless otherwise noted Thursday– Build Knowledge Graphs with Oracle RDF to Extract More Value from Your Data – S.Das/M. Perry/M. Annamalai, Oracle – 8:45am– Semantic Middleware - the Cornerstone of Your Next IT – S. Gabler, Semantic WebCompany – 9:50am– Oracle Spatial and Graph RDF Semantic Model for BIM Classification & Scheduling – T.McLane, Bechtel – 10:15am– When Graphs Meet Machine Learning – S. Hong/R. Patra, Oracle – 10:55am– Finding Malicious Network Packets Using Anomaly Detection with Graph Analytics – S.Hong, Oracle – 12:00pm– I know what you mean: leveraging graph for linking entities into knowledge base – S.Hong, Oracle – 3:40pm room 202Copyright 2019, Oracle and/or its affiliates. All rights reserved. Confidential – Oracle Internal/Restricted/Highly Restricted46

The Spatial & Graph SIG User CommunityWe are a vibrant community ofcustomers and partners that connectsand exchanges knowledge online,and at conferences and events.Join us UG ht 2019, Oracle and/or its affiliates. All rights reserved.

Engage with the Spatial and Graph SIGPromotes interaction andcommunication to drivethe market for spatial adgraph technology anddataMembers connect andexchange knowledge viaonline communities andat conferences andevents Talk with us at the Summit! Look for badges with yellowribbonsBirds of a Feather LunchWednesday 12-1pmAuditoriumReceptionsTues & Wed evenings Join us online tinyurl.com/oraclespatialcommunity Search for “Oracle Spatial and Graph Community” Contact ht 2019, Oracle and/or its affiliates. All rights reserved.

Spatial and Graph Summit @Analytics and Data Summit 2019

Analytics and Data Summit 2019 Spatial and Graph Sessions 25 Spatial and Graph related sessions See yellow track on agenda Room 103 for most sessions Tuesday: Morning: Graph technical sessions Afternoon: Spatial technical sessions, Graph hands on lab Wednesday: Morning: Spatial use cases Afternoon: Graph use cases & Spatial sessions for developers

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