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Oracle big data spatial and graph
1.
Copyright © 2014
Oracle and/or its affiliates. All rights reserved. Big Data Spatial and Graph An Overview ilOUG Tech Days - June, 2015 Michel Benoliel Master Principal Consultant Oracle Israel
2.
Copyright © 2014
Oracle and/or its affiliates. All rights reserved. | Safe Harbor Statement The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle. 2
3.
Copyright © 2015
Oracle and/or its affiliates. All rights reserved. Agenda • Spatial and Graph Strategy •Introduction to Big Data Spatial and Graph • Spatial Features •Graph Features 3
4.
Copyright © 2015
Oracle and/or its affiliates. All rights reserved. Oracle’s Spatial and Graph Strategy Enable Spatial and Graph use cases on every Big Data platform NoSQL Oracle Big Data Spatial and Graph Oracle Database Spatial and Graph 4
5.
Copyright © 2015
Oracle and/or its affiliates. All rights reserved. Conventional database or Big Data technologies Typical technical decision criteria 0 1 2 3 4 5 Tooling maturity Stringent Non-Functionals ACID transactional requirement Security Variety of data formats Data sparsity ETL simplicity Cost effectively store low value data Ingestion rate Straight Through Processing (STP) Hadoop Relational Hadoop and/or NoSQL Relational Database 5
6.
Copyright © 2015
Oracle and/or its affiliates. All rights reserved. The Big Picture – Oracle Big Data Management System SOURCES DATA RESERVOIR DATA WAREHOUSE Oracle Database Oracle Industry Models Oracle Advanced Analytics Oracle Spatial & Graph Big Data Appliance Apache Flume Oracle GoldenGate Oracle Event Processing Cloudera Hadoop Oracle Big Data SQL Oracle NoSQL Oracle R Distribution Oracle Big Data Spatial and Graph Oracle Database In-Memory, Multi-tenant Oracle Industry Models Oracle Advanced Analytics Oracle Spatial and Graph Exadata Oracle GoldenGate Oracle Event Processing Oracle Data Integrator Oracle Big Data Connectors Oracle Data Integrator B 6
7.
Copyright © 2015
Oracle and/or its affiliates. All rights reserved. Oracle Big Data Spatial and Graph Property Graph for Analysis of: • Social Media relationships • Internet of Things interactions • Cyber-Security Spatial Analysis Features for: • Location Data Enrichment • Proximity and containment analysis • Preparation of digital map and imagery data sets 7
8.
Copyright © 2015
Oracle and/or its affiliates. All rights reserved. Introduction: Spatial for Big Data 8
9.
Copyright © 2015
Oracle and/or its affiliates. All rights reserved. What is Spatial Data Integral part of almost every database • Business data that contains or describes location – Geographic features (roads, rivers, parks, etc.) – Assets (pipe lines, cables, transformers, – Sales data (sales territory, customer registration, etc.) – Street and postal address (customers, stores, factories, etc.) • Anything associated with a physical location • Described by coordinates or implicitly as text (place name), ... • Location is a “universal key” relating otherwise unrelated entities
10.
Copyright © 2015
Oracle and/or its affiliates. All rights reserved. Linking information by location Are these data points related? • Tweet: sailing by #goldengate • Instagram image subtitle: 골든게이트 교* • Text message: Driving on 101 North , just reached border between Marin County and San Francisco County • GPS Sensor: N 37°49′11″ W 122°28′44″ • Now find all data points around Golden Gate Bridge ... * Golden Gate Bridge (in Korean)
11.
Copyright © 2015
Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Highly Restricted Oracle Big Data – Spatial Features • Geo-enrichment for Data Harmonization – Resolution of location-related information – Determination of location hierarchies • Categorization and filtering – Tracking, proximity analysis, geo-fencing and categorization based on location • Data preparation – Large scale geoprocessing for cleansing, preparation of imagery, sensor data, and raw data input • Data visualization 11
12.
Copyright © 2015
Oracle and/or its affiliates. All rights reserved. Use Cases: Geocoding, Geo-Enrichment 12 (i) Geocode Call Data Records, sensor data, other sources to aggregate and display wireless network performance (dropped calls, utilization) (ii) Transportation origin-destination analysis. Combine transit card/payment info and other sources to determine where (and how many) people travel to, starting from any station on a transit network (iii) Geotagged Twitter: where are the tourists and locals tweeting i ii iii
13.
Copyright © 2015
Oracle and/or its affiliates. All rights reserved. Use case: Categorization, filtering, aggregation 13
14.
Copyright © 2015
Oracle and/or its affiliates. All rights reserved. Use case: Data preparation 14 Mosaic images Terrains and contours Shaded reliefs Pyramiding: layers at different resolution
15.
Copyright © 2015
Oracle and/or its affiliates. All rights reserved. Spatial Features – Technical overview • Support for spatial data in 2D or 3D in various formats, geodetic or projected • Support for geo-referenced imagery such as satellite images in many formats • MapReduce framework for resolution of placenames and determination location hierarchies, including reference dataset • Spatial indexing techniques for fast retrieval of spatial data • Library of spatial operators for geometric analysis (inside, within distance, nearest neighbor, ...) • Library of image processing functions (mosaic, reprojection, format conversion, analysis, ...) • Console for visual analysis, indexing, processing – Sample JEE application to be deployed in Jetty
16.
Copyright © 2015
Oracle and/or its affiliates. All rights reserved. Console Create Index on spatial data in HDFS
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Oracle and/or its affiliates. All rights reserved. Console Run Map Reduce job to perform categorization based on spatial hierarchy
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Oracle and/or its affiliates. All rights reserved. Console Results in Console “Tweets in May by State”
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Oracle and/or its affiliates. All rights reserved. Oracle Confidential – Internal/Restricted/Highly Restricted 19
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Oracle and/or its affiliates. All rights reserved. Graph Data Model What is a graph? – A set of edges (links) and vertexes (nodes) (and optionally properties) – A graph is simply linked data Why do we care? – Graphs are everywhere • Social networks/Social Web (Facebook, Linkedin, Twitter, Baidu, Google+,…) • Cyber networks, power grids, protein interaction graphs • Knowledge graphs (IBM Watson, Apple SIRI, Google Knowledge Graph) – Graphs are intuitive and flexible • Easy to navigate, easy to form a path, natural to visualize • Do not require a predefined schema E A D C B F
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Oracle and/or its affiliates. All rights reserved. Why Graph Databases Now? Rise of social networking Google, Yahoo, Twitter, Facebook, Linked In Enterprise applications increasingly need to model data relationships Telecoms: Network & Data center management, identity management Financial Services: Fraud detection; cross-selling Media & Publishing: Social apps, recommendation, sentiment Health Care: CRM, fraud detection Modeling complex relationships as graphs is efficient Improves performance Simplifies queries, traversal, search and analytics 21
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Oracle and/or its affiliates. All rights reserved. Graphs Are Big . . . and Getting Bigger • Social Scale* – 1 billion vertices, 100 billion edges • Web Scale* • 50 billion vertices, 1 trillion edges • Brain Scale* • 100 billion vertices, 100 trillion edges * An NSA Big Graph Experiment http://www.pdl.cmu.edu/SDI/2013/slides/big_graph_nsa_rd_2013_56002v1.pdf
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Oracle and/or its affiliates. All rights reserved. Graph for Social and Unstructured Data Analysis Graph is a powerful tool for Data Analysis you capture fine-grained, arbitrary By representing your data as a graph with relationships between data entities Individual relationships are represented as links When analyzing such a graph, you are using explicit relationships to find implicit information about your data Without computing multiple joins 24
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Oracle and/or its affiliates. All rights reserved. Oracle Supports Different Kinds of Graphs RDF Data Model • Data federation • Knowledge representation • Graph pattern analysis Social Network Analysis National Intelligence Public Safety Social Media search Marketing - Sentiment Linked Data / Enterprise Metadata Property Graph Model • Graph Search & Analysis • Big Data analytics • Entity analytics Life Sciences Health Care Publishing Finance Spatial Network Analysis Logistics Transportation Utilities Telcoms Network Data Model • Network path analysis • Multi-model modeling Use Case Graph Model Industry Domain
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Oracle and/or its affiliates. All rights reserved. The Property Graph Data Model • A set of vertices (or nodes) – each vertex has a unique identifier. – each vertex has a set of in/out edges. – each vertex has a collection of key-value properties. • A set of edges (or links) – each edge has a unique identifier. – each edge has a head/tail vertex. – each edge has a label denoting type of relationship between two vertices. – each edge has a collection of key-value properties. https://github.com/tinkerpop/blueprints/wiki/Property-Graph-Model
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Oracle and/or its affiliates. All rights reserved. Common Graph Analysis Use Cases Purchase Record customer items Product Recommendation Influencer Identification Communication Stream (e.g. tweets) Graph Pattern MatchingCommunity Detection Recommend the most similar item purchased by similar people Find out people that are central in the given network – e.g. influencer marketing Identify group of people that are close to each other – e.g. target group marketing Find out all the sets of entities that match to the given pattern – e.g. fraud detection 27
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Oracle and/or its affiliates. All rights reserved. Graph Analysis Examples: • Attribute searching (Get people with a given name) • Vertex/edge adjacency (Get people that like a given Web page) • Fixed-length paths (Get the friends of the friends of a given person) • Reach-ability (Is there a “friend” connection between two people?) • Pattern matching (Get the common friends between two people) • Aggregates (Get the number of friends of a given person) 28
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Oracle and/or its affiliates. All rights reserved. Use cases Social Media Management & Analysis Identifying common friends and interests; identify primary influencers in social network; graph data management of large social media services Online Product Recommendations Recommender systems; sentiment analysis; customer churn analysis; customer behavior analytics; customer trend prediction Internet of Things Manage data properties and relationships for complex webs of inter-operating devices and systems; predictive modeling of system behavior Cyber-Security Fraud detection; identity management; reveal clusters of similar behaviors and properties; discover relationships based on pattern matching 29
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Oracle and/or its affiliates. All rights reserved. Product Details: Graph Features 30
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Oracle and/or its affiliates. All rights reserved. Graph Architecture Oracle Big Data Spatial and Graph Scalable and Persistent Storage Graph Data Access Layer API Graph Analytics In-memory Analytic Engine RESTWebService Blueprints & SolrCloud / Lucene Property Graph Support on Apache HBase and Oracle NoSQL Python,Perl,PHP,Ruby, Javascript,… Java APIs Java APIs 31
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Oracle and/or its affiliates. All rights reserved. Big Data Spatial and Graph Property Graph Features • Highly scalable graph database and analytics engine • Implemented on Apache HBase and Oracle NoSQL Database • Rich developer APIs – Blueprints, REST, Java graph plus support for Groovy, Python, PHP, Perl, Ruby, and JavaScript • Fast, scalable suite of social network analysis functions – Ranking, centrality, recommender, community detection, path finding… – Targeted to address main industry requirements • Manageability – Bulk load – Console to execute Java and Gremlin APIs 32
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Oracle and/or its affiliates. All rights reserved. Support for Open Source TinkerPop Graph Tool Stack Oracle Big Data Spatial and Graph Blueprints API implementation provides support for the de-facto graph database standard TinkerPop component stack. These include query language, dataflow, REST APIs, and others. 33
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Oracle and/or its affiliates. All rights reserved. Property Graph Data Access Layer • Tinkerpop Tools: Blueprints, Gremlin, Rexter supported • Graph schema optimized on Apache HBase • Graph schema optimized on Oracle NoSQL Database • GraphML, GML, GraphSON, and Oracle-defined flat files (.ope & .opv) • Bulk load of property graph data 34
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Oracle and/or its affiliates. All rights reserved. Property Graph Data Access Layer • Parallel scan of property graph data • Apache Lucene Text search of graph data • Groovy shell for accessing property graph data • iPython-based interface example • SolrCloud integration 35
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Oracle and/or its affiliates. All rights reserved. Developer APIs for Analytics • Blueprint APIs – Modify/insert/delete graph edges, vertices, key-values – Blueprints stack (Gremlin, Pipes, etc.) provide additional functionality • Java: High level graph analysis APIs expose core functions of graph engine – Customers perform graph analysis using these Java APIs – Graph and subgraph identification (using key-value constraints) – R access through Java APIs • REST APIs – Graph analysis – Graph and sub-graph identification (using key-value constraints) • Web scripting languages supported through REST APIs above – PHP, Python, Ruby, Groovy, etc.
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Oracle and/or its affiliates. All rights reserved. Property Graph in-Memory Graph Analytics • Parallel data reading from data access layer into memory • Choice of deployment – Standalone application server – On Hadoop node • Graph formats (in addition to those supported by the data access layer) – EBin, Adjacency list, Edge List • J2EE container support (WLS, Tomcat, Jetty)
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Oracle and/or its affiliates. All rights reserved. 35 Graph Functions Detecting Components and Communities Tarjan’s, Kosaraju’s, Weakly Connected Components, Label Propagation (w/ variants), Soman and Narang’s Ranking and Walking Pagerank, Personalized Pagerank, Betweenness Centrality (w/ variants), Closeness Centrality, Degree Centrality, Eigenvector Centrality, HITS, Random walking and sampling (w/ variants) Evaluating Community Structures ∑ ∑ Conductance, Modularity Clustering Coefficient (Triangle Counting) Adamic-Adar Path-Finding Hop-Distance (BFS) Dijkstra’s, Bi-directional Dijkstra’s Bellman-Ford’s Link Prediction SALSA (Twitter’s Who-to-follow) Other Classics Vertex Cover Minimum Spanning-Tree(Prim’s)
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Oracle and/or its affiliates. All rights reserved. 39
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Oracle and/or its affiliates. All rights reserved. In-Memory Graph Analysis Framework • Large graph analysis is time-consuming because … – The computation typically involves touching most nodes and edges in the graph – The data-access pattern is random • In-memory, parallel framework for fast graph analytics • Exploits the architecture of modern servers – The computation is parallelized using multiple CPU cores – The non-sequential data-access is mitigated with large DRAMs 40
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Oracle and/or its affiliates. All rights reserved. Oracle Property Graph Engine • Reads graph from Apache HBase or Oracle NoSQL • Data Access Layer filtering used to create subgraph for Property Graph Engine analytics Oracle Property Graph or RDF (HBase or NoSQL) Property Graph Engine Analytic Request Analytic Request Analytic Request Analytic Request Analytic Request Analytic Request Trans- actional Request 41
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Oracle and/or its affiliates. All rights reserved. 42
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