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Complex Telco Networks as Simple Graphs

Telecommunications networks are vast, complex graphs upon a map. Why is it then, that Telcos typically do not use graph technology as means to understand and traverse their networks of devices, systems and customers?

This webinar explores ways for Telecommunications and media vendors to experience their networks as graphs from Neo4j.

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Complex Telco Networks as Simple Graphs

  1. 1. Graph Databases in Telco Jeff Morris Apr 2017
  2. 2. Agenda • Graphs are a technology launchpad • Graph Networks Across an Organization • Why Graphs vs. Alternatives • Case Studies in Telecommunications
  3. 3. Graphs power the transformative companies by highlighting the RELATIONSHIPS in Data
  4. 4. Real-Time Recommendations Dynamic Pricing Artificial Intelligence & IoT-applications Fraud Detection Network Management Customer Engagement Supply Chain Efficiency Identity and Access Management Relationship-Driven Applications
  5. 5. network topology
  6. 6. Mesh Router Gateway Router Router Router Mesh Router Router Router Mesh Router Gateway Access Point CPU CPU CPU CPU Mobile Mobile Mobile Mobile Base Station CPU CPU CPU CPU Access Point
  7. 7. 8 A unified view for ultimate agility • Easily understood • Easily evolved • Easy collaboration between business and IT The Whiteboard Model Is the Physical Model for Graph
  8. 8. Relational DBMSs Can’t Handle Relationships Well • Cannot model or store data and relationships without complexity • Performance degrades with number and levels of relationships, and database size • Query complexity grows with need for JOINs • Adding new types of data and relationships requires schema redesign, increasing time to market … making traditional databases inappropriate when data relationships are valuable in real-time Slow development Poor performance Low scalability Hard to maintain
  9. 9. Sample of Connected Graphs Organization Identity & Access Network & IT Ops
  10. 10. How Graphs fit within Infrastructure
  11. 11. Money Transferring Purchases Bank Services Relational database Develop Patterns Data Science-team + Good for Discrete Analysis – No Holistic View of Data-Relationships – Slow query speed for connections
  12. 12. Money Transferring Purchases Bank Services Relational database Data Lake + Good for Map Reduce + Good for Analytical Workloads – No holistic view – Non-operational workloads – Weeks-to-months processes Develop Patterns Data Science-team Merchant Data Credit Score Data Other 3rd Party Data
  13. 13. Money Transferring Purchases Bank Services Graph powers 360° view of transactions in real-time Graph Cluster SENSE Transaction stream RESPOND Alerts & notification LOAD RELEVANT DATA Relational database Data Lake Visualization UI Fine Tune Patterns Develop Patterns Data Science-team Merchant Data Credit Score Data Other 3rd Party Data
  14. 14. Money Transferring Purchases Bank Services Graph powers 360° view of transactions in real-time Graph Cluster SENSE Transaction stream RESPOND Alerts & notification LOAD RELEVANT DATA Relational database Data Lake Visualization UI Fine Tune Patterns Develop Patterns Data Science-team Merchant Data Credit Score Data Other 3rd Party Data Data-set used to explore new insights
  15. 15. Neo4j vs. Alternatives
  16. 16. Graph databases are designed for data relationships Discrete Data Minimally connected data Fit for Purpose: The Right Architecture for the Right Job Other NoSQL Relational DBMS Graph DB Connected Data Focused on Data Relationships Development Benefits Easy model maintenance Easy query Deployment Benefits Ultra high performance Minimal resource usage
  17. 17. Queries can take non-sequential, arbitrary paths through data Real-time queries need speed and consistent response times Queries must run reliably with consistent results Q A single query can touch a lot of data Relationship Queries Strain Traditional Databases 1 8
  18. 18. 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 Graph Optimized Memory & Storage
  19. 19. 2 0 Example HR Query in SQL The Same Query using openCypher MATCH (boss)-[:MANAGES*0..3]->(sub), (sub)-[:MANAGES*1..3]->(report) WHERE = “John Doe” RETURN 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 Productivity Gains with Graph Query Language The query asks: “Find all direct reports and how many people they manage, up to three levels down”
  20. 20. 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 Graph “Minutes to milliseconds” “Minutes to Milliseconds” Real-Time Query Performance
  21. 21. NoSQL Databases Don’t Handle Relationships • No data structures to model or store relationships • No query constructs to support data relationships • Relating data requires “JOIN logic” in the application • No ACID support for transactions … making NoSQL databases inappropriate when data relationships are valuable in real-time
  22. 22. UNIFIED, IN-MEMORY MAP Lightning-fast queries due to replicated in-memory architecture and index-free adjacency MACHINE 1 MACHINE 2 MACHINE 3 Slow queries due to index lookups + network hops Using Graph Using Other NoSQL to Join Data Q R Q R Relationship Queries on non-native Graph Architectures 2 3
  23. 23. Graph Transactions Over ACID Consistency Graph Transactions Over Non-ACID DBMSs 24 Maintains Integrity Over Time Eventual Consistency Becomes Corrupt Over Time The Importance of ACID Graph Writes • Ghost vertices • Stale indexes • Half-edges • Uni-directed ghost edges
  24. 24. The Graph Database for Connected Data Look for an enterprise-grade native graph database that enables you to: • Store and query data relationships • Traverse any levels of depth on real-time • Add and connect new data on the fly • Performance • ACID Transactions • Agility 2 5 Designed, built and tested natively for graphs from the start to ensure: • Developer Productivity • Hardware Efficiency
  25. 25. Why Graph: Key Technology Benefits ACID Transactions • ACID transactions with causal consistency • Security Foundation delivers enterprise- class security and control Hardware Efficiency • Native graph query processing and storage requires 10x less hardware • Index-free adjacency requires 10x less CPU Agility • Native property graph model • Modify schema as business changes without disrupting existing data Developer Productivity • Easy to learn, declarative graph query language • Procedural language extensions • Open library of procedures and functions • Worldwide developer network … all backed by Neo’s track record of leadership and product roadmap Performance • Index-free adjacency delivers millions of hops per second • In-memory pointer chasing for fast query results
  26. 26. Telco and Communications Customer Case Studies 27
  27. 27. Background • Oslo-based telcom provider is #1 in Nordic countries and #10 in world • Online, mission-critical, self-serve system lets users manage subscriptions and plans • availability and responsiveness is critical to customer satisfaction Business Problem • Logins took minutes to retrieve relational access rights • Massive joins across millions of plans, customers, admins, groups • Nightly batch production required 9 hours and produced stale data Solution and Benefits • Shifted authentication from Sybase to Neo4j • Moved resource graph to Neo4j • Replaced batch process with real-time login response measured in milliseconds that delivers real-time data, not yesterday’s snapshot • Mitigated customer retention risks Identity and Access Management Telenor COMMUNICATIONS SUBSCRIBED_BY CONTROLLED_BY PART_OFUSER_ACCESS Account Customer CustomerUser Subscription 28
  28. 28. Background • Second largest communications company in France • Based in Paris, part of Vivendi Group, partnering with Vodafone Solution and Benefits • Flexible inventory management supports modeling, aggregation, troubleshooting • Single source of truth for entire network • New apps model network via near-1:1 mapping between graph and real world • Schema adapts to changing needs Network and IT Operations SFR COMMUNICATIONS Business Problem • Infrastructure maintenance took week to plan due to need to model network impacts • Needed what-if to model unplanned outages • Identify network weaknesses to uncover need for additional redundancy • Info lived on 30+ systems, with daily changes LINKED LINKED DEPENDS_ON Router Service Switch Switch Router Fiber Link Fiber Link Fiber Link Oceanfloor Cable 29
  29. 29. Background • World’s largest provider of IT infrastructure, software and services • Unified Correlation Analyzer (UCA) helps comms operators manage large networks with carrier-class resource and service management, root cause and impact analysis Business Problem • Use network topology to identify root problems causes on the network • Simplify and speed alarm handling by operators • Automate handling of certain types of alarms • Filter/group/eliminate redundant alarms via event correlation Solution and Benefits • Accelerated product development time • Extremely fast network-topology queries • Graph representation a perfect domain fit • 24x7 carrier-grade reliability with Neo4j High Availability clustering • Met objective in under six months Hewlett Packard WEB/ISV COMMUNICATIONS Network and IT Operations30
  30. 30. Background • Hong Kong-based telephony provider branching into VOIP services via Maaii app, white-label services, and VOIP APIs • Exclusive China Mobile partner for toll-free services, SMS hub and other offerings • 2012 Red Herring Top 100 Global Winner Business Problem • Maaii app allows consumers to communicate by voice and text – similar to Line, Viber, Rebtel and VoxOx • Must relate devices, users and contacts via user address books and central database • 3 million users with 200 million graph nodes Solution and Benefits • Provide fast transactions for key operations such as suggesting friends, updating contacts, and blocking calls • Deliver high availability via Neo4j clusters • Embedded Neo4j is great architecture fit Social and Mobile Communications Maaii COMMUNICATIONS 31
  31. 31. Master Data Management Background • Part of Hutchison Whampoa, one of the world’s largest telecom conglomerates • Operates in the Nordics and UK • Moving toward real-time customer profiling and analytics Solution and Benefits • Customer-facing apps access Neo4j cluster containing a billing-information graph • Graph model gives services reps timely and insightful customers profiles • Much faster query performance • Faster app and feature development Business Problem • New business requirement to give customers more insight into their own usage patterns • Changing data model was slow and painful • New queries were difficult to write • Very large RDBMS data sets creating serious connected query (>L2) performance issues Tre TELECOMMUNICATIONS 32
  32. 32. Background • Started in 2011 in Lyon, France • Offers video communication and collaboration accessed in one click from social networks • Patented interface brings an unlimited number of online participants together in a virtual meeting space Solution and Benefits • Designed a competitive platform in one-third the anticipated development time • Introduced both real-time and social graphs • Enjoyed huge performance improvements, regardless of query complexity Business Problem • Store all contacts from all social networks in a graph, and manage all real-time interactions • Original app represented users in graphs, but used SQL to display and read them • Displaying complex queries proved impossible Glowbl COMMUNICATIONS Social Networks33
  33. 33. Graph-Based Search Background • Communications equipment giant ranks #91 in the Global 2000 with $44B in annual sales • Had success with Neo4j in Master Data Management and Real-Time Recommendations apps, so wanted to use it for this Content Management and Graph-Based Search problem Solution and Benefits • Created Intelligent Query Service, an internal document discovery system with automated keyword assignment • Time required to find precisely the right sales asset slashed from 2 weeks to 20 minutes Business Problem • Sales reps wasted days looking for appropriate materials to send to prospects • Keyword indexing system was too slow • Deal sales cycles were suffering Cisco COMMUNICATIONS INTELLIGENT QUERY SERVICE 34
  34. 34. Background • San Jose-based communications equipment giant ranks #91 in the Global 2000 with $44B in annual sales • Needed high-performance system that could provide master-data access services 24x7 to applications company-wide Solution and Benefits • New Hierarchy Management Platform (HMP) manages master data, rules and access • Cut access times from minutes to milliseconds • Graphs provided flexibility for business rules • Expanded master-data services to include product hierarchies Business Problem • Sales compensation system didn’t meet needs • Oracle RAC system had reached its limits • Inflexible handling of complex organizational hierarchies and mappings • ”Real-time” queries ran for more than a minute • P1 system must have zero downtime Cisco COMMUNICATIONS Master Data Management35
  35. 35. Background • San Jose-based communications equipment giant ranks #91 in the Global 2000 with $44B in annual sales • Needed real-time recommendations to encourage knowledge base use on company’s support portal Solution and Benefits • Faster problem resolution for customers and decreased reliance on support teams • Scrape cases, solutions, articles et al continuously for cross-reference links • Provide real-time reading recommendations • Uses Neo4j Enterprise HA cluster Business Problem • Reduce call-center volumes and costs via improved online self-service quality • Leverage large amounts of knowledge stored in service cases, solutions, articles, forums, etc. • Reduce resolution times and support costs Cisco COMMUNICATIONS Real-Time Recommendations Solution Support Case Support Case Knowledge Base Article Message Knowledge Base Article Knowledge Base Article 36
  36. 36. Neo4j for the Enterprise ENTERPRISE-CLASS PRODUCT Ready for Production • Performance & Scalability • Clustered Replication across Data Centers • Unlimited graph sizes • Intelligent online space reuse • Enterprise lock manager • Compiled Runtime for common queries • Monitoring & Administration: • Advanced Monitoring by role • Cypher Query Tracing • Hot backups • Enterprise Security • Enterprise Schema Features • Property Existence Constraints • Composite and Node key constraints ENTERPRISE-CLASS SERVICES Dedication to customer success • Certified & hardened for Production • World-Class Support with SLAs • Access to Professional Services • Training and deployment services • Access to Support Portal & Knowledge Base Growing Innovation Network • Growing service provider network • Growing OEM & VAR network • Growing Technology partner network • Growing Contribution network
  37. 37. Innovation Networks Grow in Value as They Add Members Graphs are a proven launchpad for your best next application • Intuitive foundation for AI, NLP, Machine Learning, Advances Analytics, IoT In nature, science and society dense networks innovate faster than sparse ones • 3rd Party Research—”Innovation: Where Good Ideas Come From” by Steven Johnson (2010) • …AND ideas compound via socialization—relationships—to find the next “adjacent possibility” Graphs need to be mastered—it still requires 10,000 hours of experience • We have amassed millions of hours of expertise to shrink your learning curve The question is: • Will you innovate faster as a member of the innovation network or as an “island” near it?
  38. 38. The Connected Enterprise Value Proposition Fastest path to Graph Success Graph Expertise Graph Database Platform Innovation Accelerator Enterprise-Grade Innovation Launchpad • Neo4j Enterprise Edition • HA, Causal Cluster, MDC • Better performance • Hardened product The Next Innovation • Density of the network accelerates innovation opportunity • Thousands of project successes • Partners, Service Providers, Vendors, Academics, Researchers Millions of Graph Hours • Shrink learning curve • Design advice • Contextual experience • Deploy & Ops support 39 Neo4j Commercial Network Membership
  39. 39. Valuable Resources! Sandbox Use Cases
  40. 40. Q&A
  41. 41. Graph Databases Jeff Morris Apr 2017