Neo4j GraphTalks - Graph Databases and Real-World Use Cases
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Neo4j GraphTalks event on November 2016 included: 1) An introduction to graph databases and Neo4j by Bruno Ungermann from Neo4j. 2) Darko Krizic from PRODYNA AG presenting their experience implementing a global knowledge hub for product information using Neo4j. 3) An open networking session.
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Neo4j GraphTalks - Graph Databases and Real-World Use Cases
- 1. Neo4j GraphTalks Herzlich Willkommen! November 2016 bruno.ungermann@neotechnology.com
- 2. Neo4j GraphTalks • 09:00-09:30 Frühstück und Networking • 09:30-10:15 Einführung in Graph-Datenbanken und Neo4j (Bruno Ungermann, Neo4j) • 10.15-11.00 ADAMA: Weltweiter Knowledge-Hub für Produktinformationen Erfahrungswerte aus der Implementierung und Demo (Darko Krizic, CTO PRODYNA AG) • Open End
- 3. Complexity
- 5. Domain Model Logistics Process
- 6. Traditional Approach: Fixed Schema, Tables
- 7. Graph Model: Nodes & Relationships Container Load USING_CARRIER Vessel Physical Container Container Load Shipment Carrier Emission Class A Shipment Carrier Route 10520km Route 823km Fueling Max Wgt 80 Type Gas B Town: Tokyo Town: Hong Kong Town: Hamburg Container LoadContainer LoadContainer Load Parcel Weight 15.5kg
- 9. A Naturally Adaptive Model vs Fixed Schema Flexibility
- 10. “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 other tested database types Speed
- 11. Discrete Data Minimally connected data Neo4j is designed for data relationships Other NoSQL Relational DBMS Neo4j Graph DB Connected Data Focused on Data Relationships Development Benefits Easy model maintenance Easy query Deployment Benefits Ultra high performance Minimal resource usage Use the Right Database for the Right Job
- 12. 2000 2003 2007 2009 2011 2013 2014 20152012 GraphConnect, first conference for graph DBs First Global 2000 Customer Introduced first and only declarative query language for property graph Published O’Reilly book on Graph Databases First native graph DB in 24/7 production Invented property graph model Contributed first graph DB to open source Extended graph data model to labeled property graph 150+ customers 50K+ monthly downloads 500+ graph DB events worldwide Neo4j: The Graph Database Leader
- 13. SOFTWARE FINANCE RETAIL MANUFACTU RING more SOCIAL TELECOM HEALTHCA RE
- 14. 2012 2015
- 15. “Forrester estimates that over 25% of enterprises will be using graph databases by 2017” “Neo4j is the current market leader in graph databases.” “Graph analysis is possibly the single most effective competitive differentiator for organizations pursuing data-driven operations and decisions after the design of data capture.” 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 Graph Databases – and Their Potential to Transform How We Capture Interdependencies (Enterprise Management Associates) http://blogs.enterprisemanagement.com/dennisdrogseth/2013/11/06/graph-databasesand-potential-transform-capture-interdependencies/ Neo4j Leads the Graph Database Revolution
- 17. Real-Time Recommendation s Fraud Detection Network & IT Operations Knowledge Managemen t Graph Based Search Identity & Access Management Common Graph Use Cases
- 18. Knowledge Management: Status Quo Dr. Andreas Weber | semantic data management | 11.11.2016 QS / LIMS ERP Logistik Warehouse- management Produkt- management Technisches PDM/PLM Dokumenten- management Excel Excel Powerpoint Powerpoint Excel Excel
- 19. Logistik RDBMS CRM RDBMS Mails Mailsyst Dokumente Filesystem Media Library Filesyse m CMS RDBMS Social RDBMS LogFiles RDBMS Ecommerce RDBMS Graph Based Knowledge Management (MDM, Enterprise Search..)
- 20. Adidas Shared Meta Data Service 20 Knowledge Management Background • Global leader in sporting goods industry services firm footware, apparel, hardware, 14.5 bln sales, 53,000 people • Multitude of products, markets, media, assets and audiences Business Problem • Beset by a wide array of information silos including data about products, markets, social media, master data, digital assets, brand content and more • Provide the most compelling and relevant content to consumers • Offering enhanced recommendations to drive revenue Solution and Benefits • Save time and cost through stadardized access to content sharing-system with internal teams, partners, IT units, fast, reliable, searchable avoiding reduandancy • Inprove customer experience and increase revenue by providing relevant content and recommentations
- 21. Background • Toy Manufacturer, founded 80+ years ago, plastic figurines sold in 50+ countries • 100 Mio, 250 employees • Production Process in different countries like China • Polymer Processing, Children‘s toys, high responsibility Business Problem • Product related data stored in many different data stores including SAP, Navision, Laboratory Systems, Document Systems, Powerpoint, Excel.. • Hard to find correct answers for authorities, , internally, parents Solution and Benefits • Neo4j powers integrated platform that provides visibility across whole supply chain • Domain Experts create and evolve data model • Correct answers within seconds Schleich Product Information Management 21 Knowledge Management
- 22. Schleich: transparency & reliable answers Is there a critical substance ? ? product materials substances lab tests measured values statutory thresholds law local context batches Bartagame14675 (Charge 11A1) processing steps
- 23. Schleich example: joint development by domain experts & architects law XYZ product idea briefing boardconcept board budget project model project profile product version product components (bill of material BOM) chemical risk assessment component X tool technical specification and documents production process approval approval approval
- 24. Background • Mid-size German insurer founded in 1858 • Project executed by Delvin, a subsidiary of die Bayerische Versicherung and an IT insurance specialist Business Problem • Field sales needed easy, dynamic, 24/7 access to policies and customer data • Existing DB2 system unable to meet performance and scaling demands Solution and Benefits • Enabled flexible searching of policies and associated personal data • Raised the bar on industry practices • Delivered high performance and scalability • Ported existing metadata easily Die Bayerische Versicherung INSURANCE Knowledge Management24
- 25. Background • Leading European Airline • 100+ mln passengers • 2+ mln tons freight per year • 700+ aircrafts Business Problem • Need for flexible high performant Inflight Asset Management, onboard entertainment, byod • Complex data set: CMDB, CMS, Aircraft data feed, media library • Maintain individual configuration for each Aircraft • Complex data model, aircrafts, hardware, vitual containers, licenses, business rules, versions, content ... Solution and Benefits • Neo4j powers integrated platform that provides fast access to all aspects needed to maintain complex system • Fast implementation • Higly flexible data model enable constant evolution Lufthansa Digital Asset Mangagement 25 Graph Based Search, Knowledge Managment
- 26. Related products People who bought X also bought Y The main product Recommendations (In Real-Time)
- 31. Returns Home delivery Inventory Express goods Complaints reviews Tweets Emails Location/Ad ress KITCHE N AID SERIES Promotions Bundling
- 32. Returns Purchase History Price-range Home delivery Inventory Express goods Complaints reviews Tweets Emails Category Promotions Bundling Location/Ad ress KITCHE N AID SERIES
- 33. Returns Purchase History Price-range Home delivery Inventory Express goods Complaints reviews Tweets Emails Category Promotions Bundling Location KITCHE N AID SERIES
- 35. Business Problem • Optimize walmart.com user experience • Connect complex buyer and product data to gain super-fast insight into customer needs and product trends • RDBMS couldn’t handle complex queries Solution and Benefits • Replaced complex batch process real-time online recommendations • Built simple, real-time recommendation system with low-latency queries • Serve better and faster recommendations by combining historical and session data Background • Founded in 1962 and based in Arkansas • 11,000+ stores in 27 countries with walmart.com online store • 2M+ employees and $470 billion in annual revenues Walmart RETAIL Real-Time Recommendations35
- 36. Background • One of the world’s largest logistics carriers • Projected to outgrow capacity of old system • New parcel routing system Single source of truth for entire network B2C and B2B parcel tracking Real-time routing: up to 7M parcels per day Business Problem • Needed 365x24x7 availability • Peak loads of 3000+ parcels per second • Complex and diverse software stack • Need predictable performance, linear scalability • Daily changes to logistics network: route from any point to any point Solution and Benefits • Ideal domain fit: a logistics network is a graph • Extreme availability, performance via clustering • Greatly simplified routing queries vs. relational • Flexible data model reflect real-world data variance much better than relational • Whiteboard-friendly model easy to understand Accenture LOGISTICS 36 Real-Time Routing Recommendations
- 37. 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 37
- 40. 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 40
- 41. Business Problem • Original RDBMS solution could handle only 5,000 servers • Improve net performance company-wide • Leverage M&A legacy systems with no room for error Solution and Benefits • Store UNIX server and network config in Neo4j • Combine Splunk log data into an application that visualizes events on the network • Neo4j vastly improved app performance • New apps built much faster with Neo4j than SQL Large Investment Bank FINANCIAL SERVICES Network and IT Operations41 Background • One of the world’s oldest and largest banks • 100+ year-old bank with more than 1000 predecessor institutions • 500,000 employees and contractors • Needed to manage and visualize ~50,000 Unix servers in its network
- 42. Identity Relationship ManagementIdentity Access Management Applications and data Endpoints People Customers (millions) Partners and Suppliers Workforce (thousands) PCs Tablets On-premises Private Cloud Public Cloud Things (Tens of millions) WearablesPhones PCs Customers (millions) On-premises Applications and data Endpoints People
- 43. 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, vw yday’s snapshot • Mitigated customer retention risks Identity and Access Management Telenor COMMUNICATIONS SUBSCRIBED_BY CONTROLLED_BY PART_O F USER_ACCESS Account Customer CustomerUser Subscription 43
- 44. Background • Top investment bank with $1+ trillion in assets • Using a relational database and Gemfire to manage employee permissions to research document and application-service resources • Permissions for new investment managers and traders provisioned manually Business Problem • Lost an average of 5 days per new hire while they waited to be granted access to hundreds of resources, each with its own permissions • Replace an unsuccessful onboarding process implemented by a competitor • Regulations left no room for error Solution and Benefits • Store models, groups and entitlements in Neo4j • Exceeded performance requirements • Major productivity advantage due to domain fit • Graph visualization ease permissioning process • Fewer compromises than with relational • Expanded Neo4j solution to online brokerage UBS FINANCIAL SERVICES Identity and Access Management44
- 45. INVESTIGATE Revolving Debt Number of Accounts INVESTIGATE Normal behavior Fraud Detection with Discrete Analysis
- 46. Revolving Debt Number of Accounts Normal behavior Fraud Detection With Connected Analysis Fraudulent pattern
- 47. Background • Global financial services firm with trillions of dollars in assets • Varying compliance and governance considerations • Incredibly complex transaction systems, with ever- growing opportunities for fraud Business Problem • Needed to spot and prevent fraud detection in real time, especially in payments that fall within “normal” behavior metrics • Needed more accurate and faster credit risk analysis for payment transactions • Needed to dramatically reduce chargebacks Solution and Benefits • Lowered TCO by simplifying credit risk analysis and fraud detection processes • Identify entities and connections uniquely • Saved billions by reducing chargebacks and fraud • Enabled building real-time apps with non-uniform data and no sparse tables or schema changes London and New York Financial FINANCIAL SERVICES Fraud Detection s 47
- 48. Background • Panama based lawyers Mossack & Fonseca do business in hosting “letterbox companies” • Suspected to support tax saving and organized crime • Altogether: 2.6 TB, 11 milo files, 214.000 letter box companies Business Problem • Goal to unravel chains Bank-Person–Client– Address–Intermediaries – M&F • Earlier cases: spreadsheet based analysis (back- and-forth) & pencil to extract such connections • This case: sheer amount of data & arbitrarily chain length condemn such approaches to fail Solution and Benefits • 400 journalists, investigate/update/share, 2 people with IT background • Identify connections quickly and easily • Fast Results wouldn‘t be possible without GraphDB Panama Papers Fraud Detection Fraud Detection48
- 50. How to Start?
- 51. ADAMA
Editor's Notes
- More concrete and closer to reality Flexible , no Fixed Schema
- And deriving value from data-relationships is exactly what some of the most successful companies in the world have done. Google created perhaps the most valuable advertising system of all time on top of their search-enginge, which is based on relationships between webpages. Linkedin created perhaps the most valuable HR-tool ever based on relationships amongst professional And this is also what pay-pal did, creating a peer-to-peer transaction service, based on relationships.
- When it comes to shopping online, probably the most important feature is the product recommendations you make, because they will have a direct impact on your sales. Off course, we all know Amazon has set the standard for how online-recommendations work. In this example we see a user who’s looking to buy a “Kitchen Aid”. And normally you would see recommendations based on “Related Products” or something like “People who bought product X also bought product Y”.
- This would be a classical retail recommendation. This is also very easy to model with a graph. The question here is though, if this is a limited way of looking at recommendations? – because you risk leaving out a lot of information about your user that actually affects what a good recommendation is.
- For example — it makes NO SENSE, recommending a product to user that you know he or she has made complaints about. And this is information you have in your CRM-data.
- It makes NO SENSE to recommend a product to a customer who has already bought and returned a product. It’s just a bad recommendation.
- It makes NO SENSE to recommend a product that you don’t have on stock.
- Say this is a Christmas-present… it makes NO SENSE recommending a product that you can’t deliver on time for christmas. And you know this, because your logistics data will tell you this.
- And lastly, this person looking at a kitchen aid, could be an anomaly, it doesn't necessarily mean he’s interested in kitchen stuff at all. And your Payments and Purchase data will tell you this. Perhaps this persons pattern tells you that what you should actually recommend is “Surfing Equipment” or…
- …Smart TV’s.
- The important thing to remember is, the more you know about your consumer, the more relevant your recommendations will be, the better the chance is that you’ll actually be able to make a sale. And this is a numbers game – and once you start doing this on scale…
- When we say that networks are graphs, we mean that networks by default are entities that are connected. If you do a quick search on “network topology” you basically end up with a display of a bunch of graphs…
- And if we zoom in on one of them, which seems to be a mesh network of some sort, with routers, gateways — this would be very easy to translate and model into a graph in Neo4j.
- So let’s see what’s happening in the the world of IAM. Access Management used to be pretty straight forward. And the IAM-processes used to represent a pretty simplistic world of what access meant. People accessed applications hosted on-premiss, through specific devices. And in a scenario like this one, access management isn’t really that complicated. Today, this is simply not a reality. As we discussed previously, 1) people take on several different roles, 2) and (even if you don’t think about it) they will be connected and require secure access to millions of things, they will use different types of devices with different types of dependencies, 3) and all of these individuals and roles will expect to access and use services and applications in a very granular and personalized way. So all of this is, of course, highly interconnected. And all these relationships have tremendous value. and your IAM-processes has an enormously important role to play, and from many different perspectives. …And I think this picture show you that what’s emerging are the incredibly rich data-relationships between people and things, and the different personas of people and things, and the job of IAM is going to be to use these relationships to manage who gets access to what — whether it is about accessing data coming from an IOT device or whether it’s about access to control devices remotely, or whether a device should have access to a cloud API or whether a person could share information with another person, etc… In all these different scenarios you can provide a richer experience by leveraging these relationships between all these people and things and be able to play out these different scenarios and ask those questions in real-time. This is what the world looks like, and it’s scaling rapidly. We’re going to reach an environment where we’ll see connected devices and people by the billions, so just imagine how many data-relationships that have to be in place to make sense of all this, knowing that when devices are being connected, if they’re not properly secured, it’s a huge risk from a privacy and cyber security point of view. So data-relationships are going to be a key part of the future when we build IAM-systems and when managing digital identity. And, an enterprise who doesn’t appreciate and understand the full complexity of who the customers are in an environment like this, will probably start faltering quite quickly. So it’s very exciting times for IAM, and especially for graph databases within IAM. I think how we securely manage these billions of relationships between users and things, and collaborators, employees, customers and consumers is going to be one of the epic undertakings of the future.
- [In this simple fraud detection approach to detect credit card fraud, it is relatively easy to spot outliers. But what if the fraudster commits fraud while still exhibiting normal behavior. Well - this is exactly how fraud rings operate]
- [A fraud ring rarely strays outside the normal behavior band. Instead they operate within normal limits and commit widespread fraud. This is very hard to detect by systems that are looking for outliers or activities outside the normal band.]