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The Five Graphs of Government: How Federal Agencies can Utilize Graph Technology

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In this session from Neo4j Government Graphday, Philip Rathle discusses how federal agencies and contractors can utilize graphs to power their applications.

Published in: Government & Nonprofit
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The Five Graphs of Government: How Federal Agencies can Utilize Graph Technology

  1. 1. WASHINGTON D.C. FEBRUARY 28, 2017 09:00-09:30 09:30-10:15 10:15-11:00 11:00-11:30 11:30-12:15 12:30-13:30 13:30-17:00 Breakfast and Registration How Government Agencies use Neo4j to Build the Next Generation of Applications and Services Intelligence Analysis with Neo4j Break Leveraging the Graph for Knowledge Architecture at NASA Lunch Training Session Agenda
  2. 2. Today’s Journey….
  3. 3. Fred Kagan David Mesa Chief Knowledge Architect for NASA Kimberly Kagan Director Critical Threats Project American Enterprise Institute President Institute for the Study of War Today’s Guest Speakers
  4. 4. “Life can only be understood backwards; but it must be lived forwards.” -Søren Kierkegaard
  5. 5. Yellowstone National Park Ecosystem Known Influences Entered One-at-a-Time (Willow)-[:HABITAT_FOR]->(Lincoln’s Sparrow) (Aspen)-[:FOOD_FOR]->(Beaver) (Beaver Ponds)-[:HABITAT_FOR]->(Beaver) (Deer)-[:BROWSE_ON]->(Cottonwood) (Berry Shrubs)-[:FOOD_FOR]->(Bears) …
  6. 6. Yellowstone National Park Ecosystem Known Influences Revealed as a Graph
  7. 7. MATCH path = (:Animal {Entity:"Wolves"})-[*]->(:Landscape {Entity:"Rivers"}) WITH extract(node IN nodes(path) | node.Yellowstone) AS factor, rand() AS number RETURN factor AS How_Wolves_Affect_RiverStability ORDER BY number LIMIT 5 Yellowstone National Park Ecosystem Query for Trophic Cascades Conclusion:
  8. 8. 1. Where do graph databases fit into the overall data landscape? 2. What is a graph database & when is it useful? 3. Be inspired to find your next graph in government Takeaways from this Session:
  9. 9. Source: http://dataconomy.com/2014/06/understanding-big-data-ecosystem/ Big Data Landscape “Big Data Landscape 3.0”
  10. 10. Discrete Data Minimally connected data All You Really Need to Know (at least for today) Other NoSQL Relational DBMS Neo4j Graph DB Connected Data Focused on Data Relationships
  11. 11. e of Graphs has created some of the most successful companies in the wo
  12. 12. “Graph analysis is possibly the single most effective competitive differentiator for organizations pursuing data-driven operations and decisions after the design of data capture.” By the end of 2018, 70% of leading organizations will have one or more pilot or proof-of-concept efforts underway utilizing graph databases. Analyst Perspective “Forrester estimates that over 25% of enterprises will be using graph databases by 2017” IT Market Clock for Database Management Systems, 2014 https://www.gartner.com/doc/2852717/it-market-clock-database-management TechRadar™: Enterprise DBMS, Q1 2014 http://www.forrester.com/TechRadar+Enterprise+DBMS+Q1+2014/fulltext/-/E-RES106801 Making Big Data Normal with Graph Analysis for the Masses, 2015 http://www.gartner.com/document/3100219
  13. 13. Source: https://www.forrester.com/report/Market+Overview+Graph+Databases/-/E-RES121473
  14. 14. Empowering Journalists To Make Sense of Data Taking mankind to MarsHelping Cure Cancer 2016: A Year in Graphs
  15. 15. SOFTWARE FINANCIAL SERVICES RETAIL MEDIA & OTHER SOCIAL NETWORKS TELECOM HEALTHCAR E 2016: A Year in Graphs
  16. 16. Real-Time Recommendations Dynamic Pricing Artificial Intelligence & IoT-applications Fraud Detection Network ManagementCustomer Engagement Supply Chain Efficiency Identity and Access Management 2016: A Year in Graphs
  17. 17. Graphs in Government 5.Planning 4.Science & Education 2.Resource Management 3.Oversight 1.Security 2016: A Year in Graphs
  18. 18. Some Perspective We are still here Journeying to here
  19. 19. THE PROPERTY GRAPH DATA MODEL
  20. 20. A way of representing data DATA DATA
  21. 21. Relational Database Good for: • Well-understood data structures that don’t change too frequently A way of representing data • Known problems involving discrete parts of the data, or minimal connectivity DATA
  22. 22. Graph Database Relational Database A way of representing data Good for: • Dynamic systems: where the data topology is difficult to predict • Dynamic requirements: the evolve with the business • Problems where the relationships in data contribute meaning & value Good for: • Well-understood data structures that don’t change too frequently • Known problems involving discrete parts of the data, or minimal connectivity
  23. 23. 27 A unified view for ultimate agility • Easily understood • Easily evolved • Easy collaboration between business and IT #1 Benefit: Project Agility The Whiteboard Model Is the Physical Model
  24. 24. Connectedness and Size of Data Set ResponseTime Relational and Other NoSQL Databases 0 to 2 hops 0 to 3 degrees Thousands of connections 1000x Advantage Tens to hundreds of hops Thousands of degrees Billions of connections Neo4j “Minutes to milliseconds” #2 Benefit: “Minutes to Milliseconds” Real-Time Query Performance
  25. 25. “We found Neo4j to be literally thousands of times faster than our prior MySQL solution, with queries that require 10-100 times less code. Today, Neo4j provides eBay with functionality that was previously impossible.” - Volker Pacher, Senior Developer “Minutes to milliseconds” performance Queries up to 1000x faster than RDBMS or other NoSQL #3 Benefit: “Minutes to Milliseconds” Real-Time Query Performance
  26. 26. Where’s the Magic?
  27. 27. At Write Time: data is connected as it is stored At Read Time: Lightning-fast retrieval of data and relationships via pointer chasing Index free adjacency Magic Ingredient #1 of 3: Graph Optimized Memory & Storage
  28. 28. MATCH (:Person { name:“Dan”} ) -[:MARRIED_TO]-> (spouse) MARRIED_TO Dan Ann NODE RELATIONSHIP TYPE LABEL PROPERTY VARIABLE Magic Ingredient #2 of 3: A Productive and Powerful Graph Query Language
  29. 29. 3 3 Example HR Query in SQL The Same Query using Cypher MATCH (boss)-[:MANAGES*0..3]->(sub), (sub)-[:MANAGES*1..3]->(report) WHERE boss.name = “John Doe” RETURN sub.name AS Subordinate, count(report) AS Total Project Impact Less time writing queries • More time understanding the answers • Leaving time to ask the next question Less time debugging queries: • More time writing the next piece of code • Improved quality of overall code base Code that’s easier to read: • Faster ramp-up for new project members • Improved maintainability & troubleshooting Magic Ingredient #2 of 3: A Productive and Powerful Graph Query Language
  30. 30. Graph Transactions Over ACID Consistency Graph Transactions Over Non-ACID DBMSs 34 Maintains Integrity Over Time Becomes Corrupt Over Time Magic Ingredient #3 of 3: ACID Graph Writes
  31. 31. GRAPHS IN GOVERNMENT
  32. 32. Graphs in Government 5.Planning 4.Science & Education 2.Resource Management 3.Oversight 1.Security
  33. 33. Graphs in Government 5.Planning 4.Science & Education 2.Resource Management 3.Oversight Law Enforcement 1.Security Cyber Security Intelligence
  34. 34. “Don’t consider traditional technology adequate to keep up with criminal trends” Market Guide for Online Fraud Detection, April 27, 2015
  35. 35. Law Enforcement Use Case: Information and Data Synchronization in Law Enforcement Law Enforcement Agencies use Neo4j to model the information into graphs to improve efficiency and make direct and implicit patterns readily apparent in real time. A suspect often appears in several different databases Financial recordsConvictions Adresses Vehicles Traffic cameras Arrests Police Reports Agency Records Public Records Traffic Records SUSPECT The Graphs In Government
  36. 36. The Graphs In Government 01 Bystander investigated due to deep connection found Use Case: Modeling Graphs in Investigations Neo4j is used by LE to track all parts of criminal investigations, including witnesses, suspects, forensic evidence, and locations. All related directly and indirectly. Law Enforcement
  37. 37. INVESTIGATE Revolving Debt Number of Accounts INVESTIGATE Normal behavior Fraud Detection With Discrete Analysis
  38. 38. Revolving Debt Number of Accounts Normal behavior Fraudulent pattern Fraud Detection With Connected Analysis
  39. 39. The Graphs In Government 01 ACCOUNT HOLDER 2 ACCOUNT HOLDER 1 ACCOUNT HOLDER 3 CREDIT CARD BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT ADDRESS PHONE NUMBER PHONE NUMBER SSN 2 UNSECURED LOAN SSN 2 UNSECURED LOAN Law Enforcement Use Case: Modeling Fraud Rings as Graphs Organizing a fraud ring in the real world is relatively simple. A group of people share their personal information to create synthetic identities. For example with just 2 individuals sharing names and social security numbers can create 4 different identities. This can be discovered with connected analysis.
  40. 40. Cyber Security Attack Analysis Source: http://neo4j.com/graphgist/40caddf1d7537bce962e/ https://linkurio.us/graph-data-visualisation-cyber-security-threats-analysis/ UDP storm attack Domain Model Connected Domains
  41. 41. Graphs in Government 5.Planning 4.Science & Education Asset & Inventory Management Supply Chain Network & IT Operations 2.Resource Management 3.Oversight 1.Security
  42. 42. Network & IT Operations • Impact & Dependency Analysis • Root Cause Analysis • Network Design • Network Security Analysis • Network Asset Management (CMDB)
  43. 43. Supply Chain Example from Industry
  44. 44. Graphs in Government 5.Planning 4.Science & Education 2.Resource Management Anti-Money Laundering/Fraud Risk Ownership 3.Oversight 1.Security
  45. 45. Asset Graph: Financial Ownership • Impact & Dependency Analysis • Risk Assessment • Compliance Enforcement
  46. 46. The Graphs In Government 01 Withdraw Use Case: Combating Money Laundering With Graphs Neo4j is used to combat advanced money laundering schemes. Money laundering is all about how funds travel across a network of parties. Without graph analysis capabilities, some of these patterns can be impossible to detect. Washed in complex series of transfers Anti-Money Laundering Deposit
  47. 47. The Graphs In Government 01 The Cali Cartel Money Laundering Scheme Money Laundering
  48. 48. Source: http://neo4j.com/blog/analyzing-panama-papers-neo4j/ Case Study: “The Panama Papers” • The International Consortium of Investigative Journalists (ICIJ) exposed highly connected networks of offshore tax structures used by the world’s richest elites. • With 11,5 million documents, it’s the largest financial leak of all times. • The unfolded connections in “The Panama Papers” was a major news story 2016. The Graphs In Government 01 Money Laundering
  49. 49. Graphs in Government 5.Planning Research Exploration Environment 4.Science & Education 2.Resource Management 3.Oversight 1.Security
  50. 50. Coming soon…
  51. 51. Patents, Papers, and Legislation Graph-Based Search 1) Patentula Search Demo: https://www.youtube.com/watch?v=GpHSO5j8nvQ 2) Visualizing and searching relationships between academic papers using Neo4j Graph database http://dspace.thapar.edu:8080/jspui/bitstream/10266/4014/1/801432008_Karan_cse_16-final-.pdf
  52. 52. Graphs in Government 5.Planning Lessons Learned Dependency Management Consolidation 4.Science & Education 2.Resource Management 3.Oversight 1.Security
  53. 53. Product RDBMS CRM RDBMS Payment RDBMS Marketing RDBMS Logistics RDBMS
  54. 54. Product RDBMS CRM RDBMS Payment RDBMS Marketing RDBMS Logistics RDBMS
  55. 55. Predicting WWI [Easley and Kleinberg]
  56. 56. THANK YOU!
  57. 57. WASHINGTON D.C. FEBRUARY 28, 2017 09:00-09:30 09:30-10:15 10:15-11:00 11:00-11:30 11:30-12:15 12:30-13:30 13:30-17:00 Breakfast and Registration How Government Agencies use Neo4j to Build the Next Generation of Applications and Services Intelligence Analysis with Neo4j Break Leveraging the Graph for Knowledge Architecture at NASA Lunch Training Session Agenda

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