Neo4j is a powerful and expressive tool for storing, querying and manipulating data. However modeling data as graphs is quite different from modeling data under a relational database. In this talk, Michael Hunger will cover modeling business domains using graphs and show how they can be persisted and queried in Neo4j. We'll contrast this approach with the relational model, and discuss the impact on complexity, flexibility and performance.
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
This developer-focused webinar will explain how to use the Cypher graph query language. Cypher, a query language designed specifically for graphs, allows for expressing complex graph patterns using simple ASCII art-like notation and offers a simple but expressive approach for working with graph data.
During this webinar you'll learn:
-Basic Cypher syntax
-How to construct graph patterns using Cypher
-Querying existing data
-Data import with Cypher
-Using aggregations such as statistical functions
-Extending the power of Cypher using procedures and functions
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A native graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
This developer-focused webinar will explain how to use the Cypher graph query language. Cypher, a query language designed specifically for graphs, allows for expressing complex graph patterns using simple ASCII art-like notation and offers a simple but expressive approach for working with graph data.
During this webinar you'll learn:
-Basic Cypher syntax
-How to construct graph patterns using Cypher
-Querying existing data
-Data import with Cypher
-Using aggregations such as statistical functions
-Extending the power of Cypher using procedures and functions
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A native graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
Modern Data Challenges require Modern Graph TechnologyNeo4j
Ā
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
Slides: Knowledge Graphs vs. Property GraphsDATAVERSITY
Ā
We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: Heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades.
Over the last few years, a number of new graph databases came to market. As we start the next decade, dare we say āthe semantic twenties,ā we also see vendors that never before mentioned graphs starting to position their products and solutions as graphs or graph-based.
Graph databases are one thing, but āKnowledge Graphsā are an even hotter topic. We are often asked to explain Knowledge Graphs.
Today, there are two main graph data models:
ā¢ Property Graphs (also known as Labeled Property Graphs)
ā¢ RDF Graphs (Resource Description Framework) aka Knowledge Graphs
Other graph data models are possible as well, but over 90 percent of the implementations use one of these two models. In this webinar, we will cover the following:
I. A brief overview of each of the two main graph models noted above
II. Differences in Terminology and Capabilities of these models
III. Strengths and Limitations of each approach
IV. Why Knowledge Graphs provide a strong foundation for Enterprise Data Governance and Metadata Management
Smarter Fraud Detection With Graph Data ScienceNeo4j
Ā
Join us for this 20-minute webinar to hear from Nick Johnson, Product Marketing Manager for Graph Data Science, to learn the basics of Neo4j Graph Data Science and how it can help you to identify fraudulent activities faster.
Optimizing Your Supply Chain with the Neo4j GraphNeo4j
Ā
With the worldās supply chain system in crisis, itās clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
Knowledge Graphs are Worthless, Knowledge Graph Use Cases are PricelessEnterprise Knowledge
Ā
At Knowledge Graph Forum 2022, Lulit Tesfaye and Sara Nash, Senior Consultant discuss the importance of establishing valuable and actionable use cases for knowledge graph efforts. The discussion draws on lessons learned from several knowledge graph development efforts to define how to diagnose a bad use case and outlined their impact on initiatives - including strained relationships with stakeholders, time spent reworking priorities, and team turnover. They also share guidance on how to navigate these scenarios and provide a checklist to assess a strong use case.
Easily Identify Sources of Supply Chain GridlockNeo4j
Ā
Join us for this 20-minute webinar to hear from Nick Johnson, Product Marketing Manager for Graph Data Science, as he explains the fundamentals of Neo4j Graph Data Science and its applications in optimizing supply chain management. Discover how leveraging graph analytics can help you identify bottlenecks, reduce costs, and streamline your supply chain operations more efficiently.
Volvo Cars - Retrieving Safety Insights using Graphs (GraphSummit Stockholm 2...Neo4j
Ā
Volvo Cars has developed a map attributes representation as a graph in Neo4j. By including real time car data, they are able to collect insights to learn on possible accident causes based on road infrastructure.
Modern Data Challenges require Modern Graph TechnologyNeo4j
Ā
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
Slides: Knowledge Graphs vs. Property GraphsDATAVERSITY
Ā
We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: Heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades.
Over the last few years, a number of new graph databases came to market. As we start the next decade, dare we say āthe semantic twenties,ā we also see vendors that never before mentioned graphs starting to position their products and solutions as graphs or graph-based.
Graph databases are one thing, but āKnowledge Graphsā are an even hotter topic. We are often asked to explain Knowledge Graphs.
Today, there are two main graph data models:
ā¢ Property Graphs (also known as Labeled Property Graphs)
ā¢ RDF Graphs (Resource Description Framework) aka Knowledge Graphs
Other graph data models are possible as well, but over 90 percent of the implementations use one of these two models. In this webinar, we will cover the following:
I. A brief overview of each of the two main graph models noted above
II. Differences in Terminology and Capabilities of these models
III. Strengths and Limitations of each approach
IV. Why Knowledge Graphs provide a strong foundation for Enterprise Data Governance and Metadata Management
Smarter Fraud Detection With Graph Data ScienceNeo4j
Ā
Join us for this 20-minute webinar to hear from Nick Johnson, Product Marketing Manager for Graph Data Science, to learn the basics of Neo4j Graph Data Science and how it can help you to identify fraudulent activities faster.
Optimizing Your Supply Chain with the Neo4j GraphNeo4j
Ā
With the worldās supply chain system in crisis, itās clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
Knowledge Graphs are Worthless, Knowledge Graph Use Cases are PricelessEnterprise Knowledge
Ā
At Knowledge Graph Forum 2022, Lulit Tesfaye and Sara Nash, Senior Consultant discuss the importance of establishing valuable and actionable use cases for knowledge graph efforts. The discussion draws on lessons learned from several knowledge graph development efforts to define how to diagnose a bad use case and outlined their impact on initiatives - including strained relationships with stakeholders, time spent reworking priorities, and team turnover. They also share guidance on how to navigate these scenarios and provide a checklist to assess a strong use case.
Easily Identify Sources of Supply Chain GridlockNeo4j
Ā
Join us for this 20-minute webinar to hear from Nick Johnson, Product Marketing Manager for Graph Data Science, as he explains the fundamentals of Neo4j Graph Data Science and its applications in optimizing supply chain management. Discover how leveraging graph analytics can help you identify bottlenecks, reduce costs, and streamline your supply chain operations more efficiently.
Volvo Cars - Retrieving Safety Insights using Graphs (GraphSummit Stockholm 2...Neo4j
Ā
Volvo Cars has developed a map attributes representation as a graph in Neo4j. By including real time car data, they are able to collect insights to learn on possible accident causes based on road infrastructure.
Driving Predictive Roadway Analytics with the Power of Neo4jNeo4j
Ā
This talk will present how a small start-up (Waveonics) with no prior graphical database experience employed the simplicity, versatility and flexibility of Neo4j to handle the complex and dynamic roadway conditions found in crowdsourced street map data. We map the route taken to transit from a relational to graphical data model in support of the next generation roadside service applications for an established leader in the roadside service industry (Agero). We will show how Waveonics and Agero used Neo4j to detect and understand changing roadway conditions in order to identify emerging trends and thereby improve driver safety and the driving experience.
Recent advances in mobile device sensors, GPS location services, communication technology, crowdsourced data acquisition, affordable cloud storage, machine learning and data analytics, has opened the door to an exciting world of new services. Capitalizing on these opportunities requires providers continually organize, store, manage, access and update terabytes of information efficiently. They need databases that are powerful enough to model the complexities of the business environment and easy to modify as the environment evolves - without sacrificing the performance demanded by data analytics. Waveonics is working with Agero Mobile to explore and validate the use of graphical databases (Neo4j) to drive Ageroās mission of delivering a āholistic approach [which] helps anticipate and meet customersā needs in a way that forms lasting connections with them.ā Agero is building on Neo4j to further its leadership position in the automotive, insurance, finance and roadside service industries.
Waveonics will outline best practices used to convert 175 GB of XML relational US street map data obtained from the Open Street Map (OSM) project into a Neo4j graph database in order to successfully enable predictive roadway analytics for Agero. Attendees will learn how Neo4jās graph data model and Cypher query language effortlessly supported an elegant representation of street map data, continually updated from customer mobile sensors, to reflect evolving road conditions.
Getting started with Graph Databases & Neo4jSuroor Wijdan
Ā
The presentation gives a brief information about Graph Databases and its usage in today's scenario. Moving on the presentation talks about the popular Graph DB Neo4j and its Cypher Query Language i.e., used to query the graph.
Recommendation and personalization systems are an important part of many modern websites. Graphs provide a natural way to represent the behavioral data that is the core input to many recommendation algorithms. Thomas Pinckney and his colleagues at Hunch (recently acquired by eBay) built a large scale recommendation system, and then ported the technology to eBay. Thomas will be discussing how his team uses Cassandra to provide the high I/O storage of their fifty billion edge graphs and how they generate new recommendations in real time as users click around the site.
Semantic Graph Databases: The Evolution of Relational DatabasesCambridge Semantics
Ā
In this webinar, Barry Zane, our Vice President of Engineering, discusses the evolution of databases from Relational to Semantic Graph and the Anzo Graph Query Engine, the key element of scale in the Anzo Smart Data Lake. Based on elastic clustered, in-memory computing, the Anzo Graph Query Engine offers interactive ad hoc query and analytics on datasets with billions of triples. With this powerful layer over their data, end users can effect powerful analytic workflows in a self-service manner.
Relational databases vs Non-relational databasesJames Serra
Ā
There is a lot of confusion about the place and purpose of the many recent non-relational database solutions ("NoSQL databases") compared to the relational database solutions that have been around for so many years. In this presentation I will first clarify what exactly these database solutions are, compare them, and discuss the best use cases for each. I'll discuss topics involving OLTP, scaling, data warehousing, polyglot persistence, and the CAP theorem. We will even touch on a new type of database solution called NewSQL. If you are building a new solution it is important to understand all your options so you take the right path to success.
Ready to leverage the power of a graph database to bring your application to the next level, but all the data is still stuck in a legacy relational database?
Fortunately, Neo4j offers several ways to quickly and efficiently import relational data into a suitable graph model. It's as simple as exporting the subset of the data you want to import and ingest it either with an initial loader in seconds or minutes or apply Cypher's power to put your relational data transactionally in the right places of your graph model.
In this webinar, Michael will also demonstrate a simple tool that can load relational data directly into Neo4j, automatically transforming it into a graph representation of your normalized entity-relationship model.
Designing and Building a Graph Database Application ā Architectural Choices, ...Neo4j
Ā
Ian closely looks at design and implementation strategies you can employ when building a Neo4j-based graph database solution, including architectural choices, data modelling, and testing.g
Graph Database Management Systems provide an effective
and efficient solution to data storage in current scenarios
where data are more and more connected, graph models are
widely used, and systems need to scale to large data sets.
In this framework, the conversion of the persistent layer of
an application from a relational to a graph data store can
be convenient but it is usually an hard task for database
administrators. In this paper we propose a methodology
to convert a relational to a graph database by exploiting
the schema and the constraints of the source. The approach
supports the translation of conjunctive SQL queries over the
source into graph traversal operations over the target. We
provide experimental results that show the feasibility of our
solution and the efficiency of query answering over the target
database.
This tutorial will provide you with a basic understanding of graph database technology and the ability to quickly begin development of a graph database application. You will have the capability to recognize graph-based problems and present the benefits of using graph technology for problem resolution.
The tutorial will give you an understanding of:
ā¢ Graph theory - origins and concepts
ā¢ Benefits of graph databases
ā¢ Different types of graph databases
ā¢ Typical graph database API
ā¢ Programming basics
ā¢ Use cases
Bring your laptops for a hands-on opportunity to practice some sample codes. A basic understanding of Java programming is a recommended prerequisite to understand this course. This session is led by the InfiniteGraph technical team and the demonstration code will be drawn from InfiniteGraph examples, however the broader educational presentation is product-neutral and not a commercial presentation of their products.
To participate in the hands-on portion of the graph tutorial users must have:
ā¢ Java programming experience
ā¢ Java Developer Kit (JDK)
ā¢ Current InfiniteGraph installed on laptop. (To download visit www.objectivity.com/infinitegraph)
ā¢ HelloGraph test ā Upon installing IG, run HelloGraph to test the install. (HelloGraph can be found online at http://wiki.infinitegraph.com/2.1/w/index.php?title=Download_Sample_Code)
Leon Guzenda was one of the founding members of Objectivity in 1988 and one of the original architects of Objectivity/DB. He currently works with Objectivity's major customers to help them effectively develop and deploy complex applications and systems that use the industry's highest-performing, most reliable DBMS technology, Objectivity/DB. He also liaises with technology partners and industry groups to help ensure that Objectivity/DB remains at the forefront of database and distributed computing technology. Leon has more than 35 years experience in the software industry. At Automation Technology Products, he managed the development of the ODBMS for the Cimplex solid modeling and numerical control system. Before that, he was Principal Project Director for International Computers Ltd. in the United Kingdom, delivering major projects for NATO and leading multinationals. He was also design and development manager for ICL's 2900 IDMS product. He spent the first 7 years of his career working in defense and government systems. Leon has a B.S. degree in Electronic Engineering from the University of Wales.
The trend nowadays is to represent the relationships between entities in a graph structure. Neo4j is a NOSQL graph database, which allows for fast and effective queries on connected data. Implementation of own algorithms is possible, which can improve the functionality of built in API. We make use of the graph database to model and recommend movies and other media content.
Implementing the Split-Apply-Combine model in Clojure and Incanter Tom Faulhaber
Ā
These are the slides from my talk to the Bay Area Clojure Group meeting in San Francisco on June 6, 2013.
The slides are not meant to stand alone, so they may not be completely useful if you did not attend.
Here is the description of the talk sent out in advance:
Tom Faulhaber will talk about interactive data analysis focusing on data organization and the split-apply-combine pattern. You'll find that split-apply-combine is a powerful tool that applies to many of the data problems that we look at in Clojure. This pattern is the basis of the popular plyr package developed by Hadley Wickham in the R language.
Tom will demonstrate some basic ideas of data analysis and show how they're implemented in the Incanter system. We'll discuss split-apply-combine and how it's used in Incanter today. Then, we'll discuss how to implement a full version of split-apply-combine in Clojure on top of Incanter's dataset type. Finally, we'll use our implementation to learn about some real data.
Presented at Wharton Web Conf 2013
Description:
Blank slates and green fields are all well and good, but the question of choosing a framework can be a critical leadership decision. How do you go about choosing whatās best for your team, for your problem, at this point in time? In this session, Pam Selle, a polyglot developer whoās built on her share of platforms, will talk about priorities to consider to help you make the best decision. Weāll also leave time for discussion where weāll share experiences and lessons learned. Be prepared to take some notes!
These slides refer to the talk I gave at the last ASE/IEEE SocialCom 2013 International Conference, where I presented the research work entitled "Trending Topics on Twitter Improve the Prediction of Google Hot Queries", which turned to be selected among the top-5% best accepted papers.
Once every five minutes, Twitter publishes a list of trending topics by monitoring and analyzing tweets from its users. Similarly, Google makes available hourly a list of hot queries that have been issued to the search engine. In this work, we analyze the time series derived from the daily volume index of each trend, either by Twitter or Google. Our study on a real-world dataset reveals that about 26% of the trending topics raising from Twitter "as-is" are also found as hot queries issued to Google. Also, we find that about 72% of the similar trends appear first on Twitter. Thus, we assess the relation between comparable Twitter and Google trends by testing three classes of time series regression models. We validate the forecasting power of Twitter by showing that models, which use Google as the dependent variable and Twitter as the explanatory variable, retain as significant the past values of Twitter 60% of times.
Using a Reputation Framework to Identify Community Leaders in Ontology Engine...Christophe Debruyne
Ā
Using a Reputation Framework to Identify Community Leaders in Ontology Engineering
C. Debruyne and N. Nijs
LNCS 8185, p. 677 ff.
Presented at ODBASE 2013, part of On the Move to Meaningful Internet Systems: OTM 2013 Conferences
UX & Wireframes Know Your Weapon of ChoiceIntelligent_ly
Ā
Whether you're a developer or not, you can absolutely still help build your own site. In fact, it's important that you do. Anticipating how your audience will interact with your site -- navigating, consuming, purchasing, sharing -- and structuring it to make it easier for them to do the things that matter most (to them and to you) is major.
Alec Harrison is the founder of Audacious Design, a design studio, and also works as a senior UI designer for Fresh Tilled Soil. He is passionate about data, data visualization, and technology. He specializes in creating intuitive UX/UI designs for many types of industries and products across mobile, tablet & web platforms
User experience can be drastically elevated by combining data science insights with user-based insights from research. Data analytics on its own can make themes and correlations difficult to explain and to provide accurate recommendations. For example, themes identified via large global surveys and usage data can be better understood with UX insights from focused user research, such as user interviews and/or cognitive walkthroughs. This presentation will highlight the complimentary nature of data science and UX and will focus on the benefits of bringing the two disciplines together. This will be buttressed with practical examples of enterprise projects and applications that combined data and skills from the two disciplines, guidance on how the two disciplines can better work together, and the skills needed to improve as a UX professional when working with data science teams.
Detecting and Analyzing Subpopulations within Connectivist MOOCs: Initial workMartin Hawksey
Ā
Presentation for the MOOC Research Initiative Conference in Arlington, TX 5-6th Decemeber highlighting some early research from 'Detecting and Analyzing Subpopulations within Connectivist MOOCs', which is in part examining data from ocTEL.
FIWARE Training: Introduction to Smart Data ModelsFIWARE
Ā
An online training course run by the FIWARE Foundation in conjunction with the i4Trust project and IShare Foundation. The core part of this virtual training camp (27 Jun - 01 Jul 2022) covered all the necessary skills to develop smart solutions powered by FIWARE. It introduces the basis of Digital Twin programming using NGSI-LD (the simple yet powerful open standard API enabling to publish and access digital twin data) combined with common smart data models
In addition, it covers the supplementary FIWARE technologies used to implement the rest of functions typically required when architecting a complete smart solution: Identity and Access Management (IAM) functions to secure access to digital twin data, and functions enabling the interface with IoT and 3rd systems, or the connection with different tools for processing and monitoring current and historic big data.
Extending this core part, the training camp also cover how you can easily integrate FIWARE systems with blockchain networks to create audit-proof logs of processes and ensure transparency.
Research @ RELEASeD (presented at SATTOSE2013)kim.mens
Ā
An overview of recent research results and directions at Prof. Kim Mens's RELEASeD research lab. Presented in July 2013 at SATTOSE2013 in Bern, Switzerland.
I describe a framework that the UX team at Recurly has been using to integrate UX and agile development. Lean UX is tough, but successful application of the principles leads to better products, happier engineers and designers, and a better relationship with stakeholders.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Ā
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges ā from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAGās diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of āhallucinationsā and improving the overall customer journey.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Ā
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4jās graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Ā
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges ā from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bankās data infrastructure but also positioned them as pioneers in the banking sectorās adoption of graph technology.
GraphAware - Transforming policing with graph-based intelligence analysisNeo4j
Ā
Petr Matuska, Sales & Sales Engineering Lead, GraphAware
Western Australia Police Forceās adoption of Neo4j and the GraphAware Hume graph analytics platform marks a significant advancement in data-driven policing. Facing the challenges of growing volumes of valuable data scattered in disconnected silos, the organisation successfully implemented Neo4j database and Hume, consolidating data from various sources into a dynamic knowledge graph. The result was a connected view of intelligence, making it easier for analysts to solve crime faster. The partnership between Neo4j and GraphAware in this project demonstrates the transformative impact of graph technology on law enforcementās ability to leverage growing volumes of valuable data to prevent crime and protect communities.
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product UpdatesNeo4j
Ā
David Pond, Lead Product Manager, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Shirley Bacso, Data Architect, Ingka Digital
āLinked Metadata by Designā represents the integration of the outcomes from human collaboration, starting from the design phase of data product development. This knowledge is captured in the Data Knowledge Graph. It not only enables data products to be robust and compliant but also well-understood and effectively utilized.
Your enemies use GenAI too - staying ahead of fraud with Neo4jNeo4j
Ā
Delivered by Michael Down at Gartner Data & Analytics Summit London 2024 - Your enemies use GenAI too: Staying ahead of fraud with Neo4j.
Fraudsters exploit the latest technologies like generative AI to stay undetected. Static applications canāt adapt quickly enough. Learn why you should build flexible fraud detection apps on Neo4jās native graph database combined with advanced data science algorithms. Uncover complex fraud patterns in real-time and shut down schemes before they cause damage.
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptxNeo4j
Ā
Delivered by Sreenath Gopalakrishna, Director of Software Engineering at BT, and Dr Jim Webber, Chief Scientist at Neo4j, at Gartner Data & Analytics Summit London 2024 this presentation examines how knowledge graphs and GenAI combine in real-world solutions.
BT Group has used the Neo4j Graph Database to enable impressive digital transformation programs over the last 6 years. By re-imagining their operational support systems to adopt self-serve and data lead principles they have substantially reduced the number of applications and complexity of their operations. The result has been a substantial reduction in risk and costs while improving time to value, innovation, and process automation. Future innovation plans include the exploration of uses of EKG + Generative AI.
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit MilanNeo4j
Ā
Look beyond the hype and unlock practical techniques to responsibly activate intelligence across your organizationās data with GenAI. Explore how to use knowledge graphs to increase accuracy, transparency, and explainability within generative AI systems. Youāll depart with hands-on experience combining relationships and LLMs for increased domain-specific context and enhanced reasoning.
Workshop 1. Architecting Innovative Graph Applications
Join this hands-on workshop for beginners led by Neo4j experts guiding you to systematically uncover contextual intelligence. Using a real-life dataset we will build step-by-step a graph solution; from building the graph data model to running queries and data visualization. The approach will be applicable across multiple use cases and industries.
LARUS - Galileo.XAI e Gen-AI: la nuova prospettiva di LARUS per il futuro del...Neo4j
Ā
Roberto Sannino, Larus Business Automation
Nel panorama sempre piĆ¹ complesso dei progetti basati su grafi, LARUS ha consolidato una solida esperienza pluriennale, costruendo un rapporto di fiducia e collaborazione con Neo4j. Attraverso il LARUS Labs, ha sviluppato componenti e connettori che arricchiscono lāecosistema Neo4j, contribuendo alla sua continua evoluzione. Tutto questo know-how ĆØ stato incanalato nellāinnovativa soluzione Galileo.XAI di LARUS, un prodotto allāavanguardia che, integrato con la Generative AI, offre una nuova prospettiva nel mondo dellāIntelligenza Artificiale Spiegabile applicata ai grafi. In questo speech, si esplorerĆ il percorso di crescita di LARUS in questo settore, mettendo in luce le potenzialitĆ della soluzione Galileo.XAI nel guidare lāinnovazione e la trasformazione digitale.
GraphSummit Milan - Visione e roadmap del prodotto Neo4jNeo4j
Ā
van Zoratti, VP of Product Management, Neo4j
Scoprite le ultime innovazioni di Neo4j che consentono unāintelligenza guidata dalle relazioni su scala. Scoprite le piĆ¹ recenti integrazioni nel cloud e i miglioramenti del prodotto che rendono Neo4j una scelta essenziale per gli sviluppatori che realizzano applicazioni con dati interconnessi e IA generativa.
GraphSummit Milan & Stockholm - Neo4j: The Art of the Possible with GraphNeo4j
Ā
Dr JesĆŗs Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges ā from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Ā
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Ā
Are you looking to streamline your workflows and boost your projectsā efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, youāre in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part āEssentials of Automationā series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Hereās what youāll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
Weāll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Donāt miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
Ā
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Ā
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Ā
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
Ā
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Ā
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as āpredictable inferenceā.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Ā
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
Ā
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties ā USA
Expansion of bot farms ā how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks ā Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
1. Data Modeling with
Neo4j
1
Michael Hunger, Neo Technology
@neo4j | michael@neo4j.org
Thanks to: Ian Robinson, Mark Needham,Alistair Jones
Samstag, 31. August 13
2. Please ask questions
in the chat
Iāll answer at the end.
Follow up email with missing answers,
video and slides.
2
Samstag, 31. August 13
4. This Webinar
ą¹Graphs are everywhere
ą¹Graph Model Building Blocks
ą¹(NOSQL) Data Models
ą¹Designing a Data Model
ą¹Embrace the Paradigm
4
Samstag, 31. August 13
21. Nodes
ą¹ Used to represent entities in your domain
ą¹ Can contain properties
ā¢ Used to represent entity attributes and/or metadata
(e.g. timestamps, version)
ā¢ Key-value pairs
ā£Java primitives
ā£Arrays
ā£null is not a valid value
ā¢ Every node can have different properties
Samstag, 31. August 13
23. Relationships
ą¹ Every relationship has a name and a direction
ā¢ Add structure to the graph
ā¢ Provide semantic context for nodes
ą¹ Can contain properties
ā¢ Used to represent quality or weight of relationship,
or metadata
ą¹ Every relationship must have a start node and end node
ā¢ No dangling relationships
Samstag, 31. August 13
24. Relationships (continued)
Nodes can have
more than one
relationship
Self relationships are
allowed
Nodes can be connected by
more than one relationship
Samstag, 31. August 13
25. Variable Structure
ą¹ Relationships are deļ¬ned with regard to node
instances, not classes of nodes
ā¢ Different nodes can be connected in different ways
ā¢ Allows for structural variation in the domain
ā¢ Contrast with relational schemas, where foreign key
relationships apply to all rows in a table
Samstag, 31. August 13
27. Labels
ą¹ Every node can have zero or more labels attached
ą¹ Used to represent roles (e.g. user, product, company)
ā¢ Group nodes
ā¢ Allow us to associate indexes and constraints with
groups of nodes
Samstag, 31. August 13
28. Four Building Blocks
ą¹ Nodes
ā¢ Entities
ą¹ Relationships
ā¢ Connect entities and structure domain
ą¹ Properties
ā¢ Attributes and metadata
ą¹ Labels
ā¢ Group nodes by role
Samstag, 31. August 13
31. 26
āThere is a significant downside - the whole approach works
really well when data access is aligned with the aggregates, but
what if you want to look at the data in a different way? Order
entry naturally stores orders as aggregates, but analyzing
product sales cuts across the aggregate structure. The
advantage of not using an aggregate structure in the database
is that it allows you to slice and dice your data different ways
for different audiences.
This is why aggregate-oriented stores talk so much about map-
reduce.ā
Martin Fowler
Aggregate Oriented Model
Samstag, 31. August 13
32. 27
The connected data model is based on fine grained elements
that are richly connected, the emphasis is on extracting many
dimensions and attributes as elements.
Connections are cheap and can be used not only for the
domain-level relationships but also for additional structures
that allow efficient access for different use-cases. The fine
grained model requires a external scope for mutating
operations that ensures Atomicity, Consistency, Isolation and
Durability - ACID also known as Transactions.
Michael Hunger
Connected Data Model
Samstag, 31. August 13
54. Method
1. Identify application/end-user goals
2. Figure out what questions to ask of the domain
3. Identify entities in each question
4. Identify relationships between entities in each
question
5. Convert entities and relationships to paths
These become the basis of the data model
6. Express questions as graph patterns
These become the basis for queries
Samstag, 31. August 13
55. From User Story to Model and Query
1.
User story
4.
Paths
3.
Entities and
relationships
?2.
Questions we want
to ask
5.
Data model
6.
Query
Samstag, 31. August 13
56. 1. Application/End-User Goals
As an employee
I want to know who in thecompany has similar skills to meSo that we can exchangeknowledge
Samstag, 31. August 13
57. 2. Questions To Ask of the Domain
Which people, who work for the same
company as me, have similar skills to me?
As an employee
I want to know who in thecompany has similar skills tome
So that we can exchangeknowledge
Samstag, 31. August 13
58. Which people, who work for the same
company as me, have similar skills to me?
Person
Company
Skill
3. Identify Entities
Samstag, 31. August 13
59. Which people, who work for the same
company as me, have similar skills to me?
Person WORKS_FOR Company
Person HAS_SKILL Skill
4. Identify Relationships Between
Entities
Samstag, 31. August 13
60. 5. Convert to Cypher Paths
Person WORKS_FOR Company
Person HAS_SKILL Skill
Samstag, 31. August 13
61. 5. Convert to Cypher Paths
Person WORKS_FOR Company
Person HAS_SKILL Skill
Relationship
Label
Samstag, 31. August 13
62. 5. Convert to Cypher Paths
Person WORKS_FOR Company
Person HAS_SKILL Skill
Relationship
Label
(:Person)-[:WORKS_FOR]->(:Company),
(:Person)-[:HAS_SKILL]->(:Skill)
Samstag, 31. August 13
67. 6. Express Question as Graph Pattern
Which people, who work for the same
company as me, have similar skills to me?
Samstag, 31. August 13
68. Cypher Query
Which people, who work for the same
company as me, have similar skills to me?
MATCH (company)<-[:WORKS_FOR]-(me:Person)-[:HAS_SKILL]->(skill),
(company)<-[:WORKS_FOR]-(colleague)-[:HAS_SKILL]->(skill)
WHERE me.name = {name}
RETURN colleague.name AS name,
count(skill) AS score,
collect(skill.name) AS skills
ORDER BY score DESC
Samstag, 31. August 13
69. Which people, who work for the same
company as me, have similar skills to me?
MATCH (company)<-[:WORKS_FOR]-(me:Person)-[:HAS_SKILL]->(skill),
(company)<-[:WORKS_FOR]-(colleague)-[:HAS_SKILL]->(skill)
WHERE me.name = {name}
RETURN colleague.name AS name,
count(skill) AS score,
collect(skill.name) AS skills
ORDER BY score DESC
Graph Pattern
Samstag, 31. August 13
70. Which people, who work for the same
company as me, have similar skills to me?
MATCH (company)<-[:WORKS_FOR]-(me:Person)-[:HAS_SKILL]->(skill),
(company)<-[:WORKS_FOR]-(colleague)-[:HAS_SKILL]->(skill)
WHERE me.name = {name}
RETURN colleague.name AS name,
count(skill) AS score,
collect(skill.name) AS skills
ORDER BY score DESC
Anchor Pattern in Graph
Samstag, 31. August 13
71. Which people, who work for the same
company as me, have similar skills to me?
MATCH (company)<-[:WORKS_FOR]-(me:Person)-[:HAS_SKILL]->(skill),
(company)<-[:WORKS_FOR]-(colleague)-[:HAS_SKILL]->(skill)
WHERE me.name = {name}
RETURN colleague.name AS name,
count(skill) AS score,
collect(skill.name) AS skills
ORDER BY score DESC
Create Projection of Results
Samstag, 31. August 13
76. From User Story to Model and Query
MATCH (company)<-[:WORKS_FOR]-(me:Person)-[:HAS_SKILL]->(skill),
(company)<-[:WORKS_FOR]-(colleague)-[:HAS_SKILL]->(skill)
WHERE me.name = {name}
RETURN colleague.name AS name,
count(skill) AS score,
collect(skill.name) AS skills
ORDER BY score DESC
As an employee
I want to know who in thecompany has similar skills tome
So that we can exchangeknowledge
(:Company)<-[:WORKS_FOR]-(:Person)-[:HAS_SKILL]->(:Skill)
Person WORKS_FOR Company
Person HAS_SKILL Skill
?Which people, who work for the
same company as me, have similar
skills to me?
Samstag, 31. August 13
81. Anti-Pattern: Node represents multiple
concepts
name
age
position
company
department
project
skills
Person
Samstag, 31. August 13
82. HAS_SKILL
Normalize into separate concepts
name
age
Person
name
number_of_employees
Company
WORKS_FOR
Skill
name
Samstag, 31. August 13
83. Challenge: Property or Relationship?
ą¹ Can every property be replaced by a relationship?
ā¢ Hint: triple stores. Are they easy to use?
ą¹ Should every entity with the same property values be
connected?
Samstag, 31. August 13
84. Object Mapping
ą¹ Similar to how you would map objects to a relational
database, using an ORM such as Hibernate
ą¹ Generally simpler and easier to reason about
ą¹ Examples
ā¢ Java: Spring Data Neo4j
ā¢ Ruby: Active Model
ą¹ Why Map?
ā¢ Do you use mapping because you are scared of SQL?
ā¢ Following DDD, could you write your repositories
directly against the graph API?
Samstag, 31. August 13
86. Relationships for querying
ą¹ like in other databases
ā¢ same structure for different use-cases (OLTP and
OLAP) doesnāt work
ā¢ graph allows: add more structures
ą¹ Relationships should the primary means to access
nodes in the database
ą¹ Traversing relationships is cheap ā thatās the whole
design goal of a graph database
ą¹ Use lookups only to ļ¬nd starting nodes for a query
Data Modeling examples in Manual
Samstag, 31. August 13
95. Evolution: Relationship to Node
68
Peter
SENT_EMAIL
Michael
Peter EMAIL_FROM
Michael
EMAIL_TO
Email
Emil
EMAIL_CC
Community
TAGGED
. . .
see Hyperedges
Samstag, 31. August 13
96. Combine multiple Domains in a Graph
ą¹ you start with a single domain
ą¹ add more connected domains as your system evolves
ą¹ more domains allow to ask different queries
ą¹ one domain āindexesā the other
ą¹ Example Facebook Graph Search
ā¢ social graph
ā¢ location graph
ā¢ activity graph
ā¢ favorite graph
ā¢ ...
Samstag, 31. August 13
97. Notes on the Graph Data Model
ą¹Schema free, but constraints
ą¹Model your graph with a whiteboard and a wise man
ą¹Nodes as main entities but useless without connections
ą¹Relationships are ļ¬rst level citizens in the model and database
ą¹Normalize more than in a relational database
ą¹use meaningful relationship-types, not generic ones like IS_
ą¹use in-graph structures to allow different access paths
ą¹evolve your graph to your needs, incremental growth
70
Samstag, 31. August 13
106. Need to model the relationship
language_code
language_name
word_count
Language
country_code
country_name
ļ¬ag_uri
language_code
Country
Samstag, 31. August 13
107. What if the cardinality changes?
language_code
language_name
word_count
country_code
Language
country_code
country_name
ļ¬ag_uri
Country
Samstag, 31. August 13
108. Or we go many-to-many?
language_code
language_name
word_count
Language
country_code
country_name
ļ¬ag_uri
Country
language_code
country_code
LanguageCountry
Samstag, 31. August 13
109. Or we want to qualify the relationship?
language_code
language_name
word_count
Language
country_code
country_name
ļ¬ag_uri
Country
language_code
country_code
primary
LanguageCountry
Samstag, 31. August 13
114. Whatās different?
ą¹ Implementation of maintaining relationships is left up
to the database
ą¹ Artiļ¬cial keys disappear or are unnecessary
ą¹ Relationships get an explicit name
ā¢ can be navigated in both directions
Samstag, 31. August 13
118. Keep on adding relationships
name
word_count
Language
name
ļ¬ag_uri
Country
POPULATION_SPEAKS
population_fraction
SIMILAR_TO ADJACENT_TO
Samstag, 31. August 13
131. [A] ACL from Hell
ą¹ Customer:
ā¢ leading consumer utility company with tons and
tons of users
ą¹ Goal:
ā¢ comprehensive access control administration
for customers
ą¹ Beneļ¬ts:
ā¢ Flexible and dynamic architecture
ā¢ Exceptional performance
ā¢ Extensible data model supports new
applications and features
ā¢ Low cost
95
Samstag, 31. August 13
132. [A] ACL from Hell
ą¹ Customer:
ā¢ leading consumer utility company with tons and
tons of users
ą¹ Goal:
ā¢ comprehensive access control administration
for customers
ą¹ Beneļ¬ts:
ā¢ Flexible and dynamic architecture
ā¢ Exceptional performance
ā¢ Extensible data model supports new
applications and features
ā¢ Low cost
95
ā¢ A Reliable access control administration system for
5 million customers, subscriptions and agreements
ā¢ Complex dependencies between groups, companies,
individuals, accounts, products, subscriptions, services and
agreements
ā¢ Broad and deep graphs (master customers with 1000s of
customers, subscriptions & agreements)
Samstag, 31. August 13
133. [A] ACL from Hell
ą¹ Customer:
ā¢ leading consumer utility company with tons and
tons of users
ą¹ Goal:
ā¢ comprehensive access control administration
for customers
ą¹ Beneļ¬ts:
ā¢ Flexible and dynamic architecture
ā¢ Exceptional performance
ā¢ Extensible data model supports new
applications and features
ā¢ Low cost
95
ā¢ A Reliable access control administration system for
5 million customers, subscriptions and agreements
ā¢ Complex dependencies between groups, companies,
individuals, accounts, products, subscriptions, services and
agreements
ā¢ Broad and deep graphs (master customers with 1000s of
customers, subscriptions & agreements)
name: Andreas
subscription: sports
service: NFL
account: 9758352794
agreement: ultimate
owns
subscribes to
has plan
includes
provides group: graphistas
promotion: fall
member of
offered
discounts
company: Neo
Technologyworks with
gets discount on
subscription: local
subscribes to
provides service: Ravens
includes
Samstag, 31. August 13
135. [B] Timely Recommendations
ą¹ Customer:
ā¢ a professional social network
ā¢ 35 millions users, adding 30,000+ each day
ą¹ Goal: up-to-date recommendations
ā¢ Scalable solution with real-time end-user
experience
ā¢ Low maintenance and reliable architecture
ā¢ 8-week implementation
96
Samstag, 31. August 13
136. [B] Timely Recommendations
ą¹ Customer:
ā¢ a professional social network
ā¢ 35 millions users, adding 30,000+ each day
ą¹ Goal: up-to-date recommendations
ā¢ Scalable solution with real-time end-user
experience
ā¢ Low maintenance and reliable architecture
ā¢ 8-week implementation
96
ą¹ Problem:
ā¢ Real-time recommendation imperative to attract new
users and maintain positive user retention
ā¢ Clustered MySQL solution not scalable or fast enough
to support real-time requirements
ą¹ Upgrade from running a batch job
ā¢ initial hour-long batch job
ā¢ but then success happened, and it became a day
ā¢ then two days
ą¹ With Neo4j, real time recommendations
Samstag, 31. August 13
137. [B] Timely Recommendations
ą¹ Customer:
ā¢ a professional social network
ā¢ 35 millions users, adding 30,000+ each day
ą¹ Goal: up-to-date recommendations
ā¢ Scalable solution with real-time end-user
experience
ā¢ Low maintenance and reliable architecture
ā¢ 8-week implementation
96
ą¹ Problem:
ā¢ Real-time recommendation imperative to attract new
users and maintain positive user retention
ā¢ Clustered MySQL solution not scalable or fast enough
to support real-time requirements
ą¹ Upgrade from running a batch job
ā¢ initial hour-long batch job
ā¢ but then success happened, and it became a day
ā¢ then two days
ą¹ With Neo4j, real time recommendations
name:Andreas
job: talking
name: Allison
job: plumber
name: Tobias
job: coding
knows
knows
name: Peter
job: building
name: Emil
job: plumber
knows
name: Stephen
job: DJ
knows
knows
name: Delia
job: barking
knows
knows
name: Tiberius
job: dancer
knows
knows
knows
knows
Samstag, 31. August 13
139. [C] Collaboration on Global Scale
ą¹ Customer: a worldwide software leader
ā¢ highly collaborative end-users
ą¹ Goal: offer an online platform for global collaboration
ā¢ Highly ļ¬exible data analysis
ā¢ Sub-second results for large, densely-connected data
ā¢ User experience - competitive advantage
97
Samstag, 31. August 13
140. [C] Collaboration on Global Scale
ą¹ Customer: a worldwide software leader
ā¢ highly collaborative end-users
ą¹ Goal: offer an online platform for global collaboration
ā¢ Highly ļ¬exible data analysis
ā¢ Sub-second results for large, densely-connected data
ā¢ User experience - competitive advantage
97
ā¢ Massive amounts of data tied to members, user
groups, member content, etc. all interconnected
ā¢ Infer collaborative relationships through user-
generated content
ā¢ Worldwide Availability
Samstag, 31. August 13
141. [C] Collaboration on Global Scale
ą¹ Customer: a worldwide software leader
ā¢ highly collaborative end-users
ą¹ Goal: offer an online platform for global collaboration
ā¢ Highly ļ¬exible data analysis
ā¢ Sub-second results for large, densely-connected data
ā¢ User experience - competitive advantage
97
ā¢ Massive amounts of data tied to members, user
groups, member content, etc. all interconnected
ā¢ Infer collaborative relationships through user-
generated content
ā¢ Worldwide Availability
Asia North America Europe
Samstag, 31. August 13
142. [C] Collaboration on Global Scale
ą¹ Customer: a worldwide software leader
ā¢ highly collaborative end-users
ą¹ Goal: offer an online platform for global collaboration
ā¢ Highly ļ¬exible data analysis
ā¢ Sub-second results for large, densely-connected data
ā¢ User experience - competitive advantage
97
ā¢ Massive amounts of data tied to members, user
groups, member content, etc. all interconnected
ā¢ Infer collaborative relationships through user-
generated content
ā¢ Worldwide Availability
Asia North America Europe
Asia North America Europe
Samstag, 31. August 13
158. 112
Really, once you start
thinking in graphs
it's hard to stop
Recommendations MDM
Systems
Management
Geospatial
Social computing
Business intelligence
Biotechnology
Making Sense of all that
data
your brain
access control
linguistics
catalogs
genealogyrouting
compensation market vectors
Samstag, 31. August 13
159. 112
Really, once you start
thinking in graphs
it's hard to stop
Recommendations MDM
Systems
Management
Geospatial
Social computing
Business intelligence
Biotechnology
Making Sense of all that
data
your brain
access control
linguistics
catalogs
genealogyrouting
compensation market vectors
What will you build?
Samstag, 31. August 13
166. A graph database...
117
NO: not for charts & diagrams, or vector artwork
YES: for storing data that is structured as a graph
Samstag, 31. August 13
167. A graph database...
117
NO: not for charts & diagrams, or vector artwork
YES: for storing data that is structured as a graph
remember linked lists, trees?
Samstag, 31. August 13
168. A graph database...
117
NO: not for charts & diagrams, or vector artwork
YES: for storing data that is structured as a graph
remember linked lists, trees?
graphs are the general-purpose data structure
Samstag, 31. August 13
169. A graph database...
117
NO: not for charts & diagrams, or vector artwork
YES: for storing data that is structured as a graph
remember linked lists, trees?
graphs are the general-purpose data structure
āA relational database may tell you the average age of everyone
in this place,
but a graph database will tell you who is most likely to buy you a
beer.ā
Samstag, 31. August 13
171. Why Data Modeling
119
ą¹What is modeling?
ą¹Arenāt we schema free?
ą¹How does it work in a
graph?
ą¹Where should modeling
happen? DB or Application
Samstag, 31. August 13
184. // lookup starting point in an index
START n=node:People(name = āAndreasā)
Andreas
You traverse the graph
125
Samstag, 31. August 13
185. // lookup starting point in an index
START n=node:People(name = āAndreasā)
Andreas
You traverse the graph
125
// then traverse to find results
START me=node:People(name = āAndreasā
MATCH (me)-[:FRIEND]-(friend)-[:FRIEND]-(friend2)
RETURN friend2
Samstag, 31. August 13
187. SELECT skills.*, user_skill.*
FROM users
JOIN user_skill ON users.id = user_skill.user_id
JOIN skills ON user_skill.skill_id = skill.id WHERE users.id = 1
126
START user = node(1)
MATCH user -[user_skill]-> skill
RETURN skill, user_skill
Samstag, 31. August 13