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Graph Gurus Episode 8: Location, Location, Location - Geospatial Analysis with a Native Parallel Graph Database
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Graph Gurus Episode 8: Location, Location, Location - Geospatial Analysis with a Native Parallel Graph Database
1.
Graph Gurus Episode
8 Location, Location, Location: Geospatial Analysis with A Native Parallel Graph Database
2.
© 2018 TigerGraph.
All Rights Reserved Welcome ● Attendees are muted but you can talk to us via Chat in Zoom ● Send questions at any time using the Q&A tab in the Zoom menu ● We will have 10 min for Q&A at the end ● The webinar will be recorded and sent via email ● A link to the presentation and reproducible steps will be emailed 2 ZOOM ISSUES: update to the latest version of Zoom and if you are using multiple monitors disable “use dual monitors” in settings
3.
© 2018 TigerGraph.
All Rights Reserved Developer Edition Available We now offer Docker versions and VirtualBox versions of the TigerGraph Developer Edition, so you can now run on ● MacOS ● Windows 10 ● Linux Developer Edition Download https://www.tigergraph.com/developer/ 3
4.
© 2018 TigerGraph.
All Rights Reserved Today's Gurus 4 Emma Liu Product Manager ● BS in Engineering from Harvey Mudd College, MS in Engineering Systems from MIT ● Prior work experience at Oracle and MarkLogic ● Focus - Cloud, Containers, Enterprise Infra, Monitoring, Management, Connectors Xinyu Chang Solution Team Lead ● 4 Years with TigerGraph ● Co-authored GSQL ● Created solutions for most major customers ● Expert in graph solutions and algorithms
5.
© 2018 TigerGraph.
All Rights Reserved© 2018 TigerGraph. All Rights Reserved Agenda ● GeoSpatial Use Case ● Geospatial Search with Graph Database ● Why TigerGraph for GeoSpatial Analytics? ● GeoGraph Concepts and Definitions ● GeoGraph Design and Implementation ● GeoGraph Demo 5
6.
© 2018 TigerGraph.
All Rights Reserved 6 Traffic Inflow (predicted) Traffic Outflow (predicted) Saturday, 11:15 pm Japan Standard Time Number of Taxis needed: 105 Number of Taxis available: 65 Geospatial Operational Analytics Example: Real-time Taxi Positioning
7.
© 2018 TigerGraph.
All Rights Reserved 40 Matching Demand & Supply with Predicted Traffic Flow Data For Busy Locations 78 96 120 92 45 45 80 29 45 323445 60 35 17 54 35 14 79 25 72 45 35 44 57 89 648745 45 98 34 65 35 78 78 85 78 45 45 35 98 64 33 63 654 69 Input Driver Suggested Moving direction 120 In Flow 40 Out Flow Flow Prediction Based on Historical data 40 Number of Taxis in that mesh
8.
© 2018 TigerGraph.
All Rights Reserved 8 Develop Location-Based Customer 360 Profiles
9.
© 2018 TigerGraph.
All Rights Reserved Recommendation Similar Users With Similar Movement Pattern = Grid Input User Geo-Temporal Patterns for Recommendation
10.
11.
© 2018 TigerGraph.
All Rights Reserved GeoSpatial Analytics with Graph Database 11 Finding entities (nodes/edges) in a connected network matching certain geolocation patterns One More Dimension(s) to Your Solution Space
12.
© 2018 TigerGraph.
All Rights Reserved Common GeoSpatial Questions 12 RDBMS • Real World Challenge: • Not designed for complex Geo based search questions in real time • Rely on third party index • No easy SQL for answering these questions TigerGraph • Real World Benefit: • GeoSpatial data is on a graph • Designed for discovery/exploratory type of analytics • GSQL can naturally and efficiently express and solve the Geo search queries A. Who are the closest target entities (e.g., taxi drivers) to customer A? B. Find all hospitals in real time which are within this region with Rh-negative blood type. C. Find all doctors nearby who are specialists in congenital heart defects.
13.
13 Why TigerGraph for
GeoSpatial Analytics? Not Just Search, But Real-time Geo Intelligence!
14.
© 2018 TigerGraph.
All Rights Reserved Why TigerGraph for GeoSpatial Search? Deep Link Analysis in Physical World • Geo Aware Multi Hop Entity Relationship • Native Parallel Graph Enabled by MPP Geo Enriched Graph Applications • GeoSpatial Intelligence to All Use Cases • Example: Smart Mobile Location Based Recommendation 14 • Movement at the Same Time with Many Entities (People and Things) • Continuous Big Data Real-Time Geo Insights Unique TigerGraph Features
15.
© 2018 TigerGraph.
All Rights Reserved GeoSpatial Search (Intelligence) with TigerGraph 15 Problem: Finding entities (nodes/edges) in a connected network matching certain geolocation patterns with movements with continuously increasing real time data Solution: First and Only OLAP and OLTP Graph Database
16.
16 GeoGraph In Depth TigerGraph
GeoSpatial Search Solution and Demo Xinyu Chang, Solution Team Lead
17.
© 2018 TigerGraph.
All Rights Reserved What is GeoGraph? 17 1. The earth is cut into a grid of bounding boxes or cells. 2. Each box can be represented by a vertex in the graph. 3. Each vertex has a predictable ID, which is x + y*c, where c is the number of columns. 4. Each object vertex is connected to the corresponding geo location vertex based on its coordinates.
18.
© 2018 TigerGraph.
All Rights Reserved Graph Representation 18 123122 124 22 23 24 223222 224 Object Object Relationship between objects Location Edge Geo Location Vertex Object
19.
© 2018 TigerGraph.
All Rights Reserved R-Tree vs. Grid 19 R-Tree Approach • Advantages: Search for arbitrary regions, points Allows us to estimate the number of dots in a region without a full data scan • Disadvantages: Significant redundancy in the data storage Slow update Grid Approach • Advantages: Stored as vertices and edges, naturally integrated with TIgerGraph System. Do the analytics including geo in a MPP way. Fast to update. Easy to implement and maintain. • Disadvantages: Might have uneven distribution of objects on each grid
20.
© 2018 TigerGraph.
All Rights Reserved Example Dataset California Healthcare Facility Locations https://data.chhs.ca.gov/dataset/healthcare-facility-locations City Zipcode and Location https://www.aggdata.com/
21.
© 2018 TigerGraph.
All Rights Reserved Graph Schema & Data Loading 21 Token Function getGridId: Given latitude and longitude, return the id of the grid vertex id.
22.
© 2018 TigerGraph.
All Rights Reserved Graph Exploration & Queries
23.
© 2018 TigerGraph.
All Rights Reserved Query Mechanism Expression Function getNearbyGridId: Given latitude, longitude and distance, return the nearby grid vertex ids within distance. Expression Function geoDistance: Given two pairs of latitude and longitude, return the distance between the two locations.
24.
© 2018 TigerGraph.
All Rights Reserved Query Mechanism User Geo Grid Geo Grid Geo Grid Geo Grid Geo Grid Geo Grid Geo Grid Geo Grid Geo Grid Facility Facility Facility Facility Facility
25.
© 2018 TigerGraph.
All Rights Reserved Demo Geospatial Search UI http://54.88.6.143:14240/geo/# GraphStudio UI http://54.88.6.143:14240
26.
26 Takeaways
27.
© 2018 TigerGraph.
All Rights Reserved Summary • Graph Database is a natural fit for Geospatial Analytics • TigerGraph is designed from the ground up for fast GeoSpatial Analytics • GeoSpatial Analytics algorithms with GSQL Queries • Graph Studio Demo for GeoSpatial Analytics 27
28.
Q&A Please send your
questions via the Q&A menu in Zoom 28
29.
© 2018 TigerGraph.
All Rights Reserved Additional Resources 29 New Developer Portal https://www.tigergraph.com/developers/ Download the Developer Edition or Enterprise Free Trial https://www.tigergraph.com/download/ Guru Scripts https://github.com/tigergraph/ecosys/tree/master/guru_scripts Join our Developer Forum https://groups.google.com/a/opengsql.org/forum/#!forum/gsql-users @TigerGraphDB youtube.com/tigergraph facebook.com/TigerGraphDB linkedin.com/company/TigerGraph
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