Introduction to Neo4j - June 2019
With the introduction of the Neo4j Graph Platform and increased adoption of graph database technology across all industries, now is a better time than ever to get started with graphs. Join us for this introduction to Neo4j and graph databases. We'll discuss the primary use cases for graph databases and explore the properties of Neo4j that make those use cases possible.
Graphs in Automotive and Manufacturing - Unlock New Value from Your Data
In this session, we’ll be taking a look at graphs within the Manufacturing industry generally, and more specifically in the Automotive industry. We’ll review the types of data that are typically available within a manufacturing company, illustrate some graphs which can be created from that data, and discuss the use cases those graphs can enable and transform. The use cases presented will include Bills of Materials, Supply Chain Management, Logistics, and Claims Processing. We will also discuss where graph algorithms and integration with Artificial Intelligence/Machine Learning technologies can be used along with Neo4j to unlock new value from manufacturing data.
Neo4j Bloom for Project Teams Browser Based and Multi User Enabled
Neo4j Bloom is a graph visualization and exploration product. It offers a code-less search to graph insight experience suitable for end users of a graph-powered application. This enables graph novices and experts, technology and business side to easily collaborate and communicate. In addition, Bloom interprets and runs near natural language queries. Neo4j Bloom 1.1 is the next browser-based version of Bloom. Host it centrally on a server and allow access via a web browser, without the need for a desktop installation. With this update, Bloom makes it easy for project teams to collaborate and communicate using shared views of the same graph. What’s new in version 1.1
Improve ML Predictions using Graph Algorithms
Graph enhancements to AI and ML are changing the landscape of intelligent applications. In this session, we’ll focus on how using connected features can help improve the accuracy, precision, and recall of machine learning models. You’ll learn how graph algorithms can provide more predictive features as well as aid in feature selection to reduce overfitting. We’ll look at a link prediction example to predict collaboration with measurable improvement when including graph-based features.
Graph-Based Real Time Inventory and Topology for Network Automation
When building a network automation solution, one of the first steps on your journey will certainly be the creation of a source of truth for your network inventory and topology. A graph platform like Neo4j is a great fit for the purpose, providing a rich, accurate and timely representation of your network. In this webinar, Teemu Nykänen, Service Architect at Elisa will discuss with Jesús Barrasa Elisa's SDN initiatives and their experience building a live inventory and topology store with Neo4j. Elisa, based in Finland, is one of the world leaders in network automation, offering communication services in Northern Europe as well as automation solutions to other CSPs.
Acquisition de données dans Neo4j pour le Master Data Management
Une base de données graphes convient parfaitement aux cas d'utilisation de Master Data Management, tels que la création d'une vue à 360 degrés du client. Les entités du monde réel telles que les clients, les produits et les tickets de support, ainsi que les relations entre eux, peuvent être directement modélisées dans le graphe, ce qui permet l'analyse et la visualisation de l'ensemble de données combiné. Avant que cela ne se produise, les données doivent être collectées à partir d'un ensemble varié de sources et ingérées dans la base de données de graphes. Les données source peuvent être situées dans des fichiers plats, dans des bases de données relationnelles, dans des plates-formes basées sur le cloud ou même dans une file d'attente de messages. Extraire des données de leur source, les transformer en la structure et le format requis et les charger dans la base de données graphes (processus ETL) peut être un projet majeure. L’écriture de scripts personnalisés ou l’utilisation d’outils traditionnels ETL donne lieu à une solution fragile qui échoue face à la modification des structures de données et aux exigences, telles que l’acquisition de données en continu. Au cours de cette session, vous apprendrez à créer des pipelines de données robustes pour charger des données par lots et en continu dans Neo4j. Nous examinerons les particularités de différentes sources de données et examinerons des cas d'utilisation réels, tels que l'extraction de données clients du cloud et leur combinaison avec les données de produit d'une base de données relationnelle. Au préalable, nous ferons une courte introduction à Neo4j avec Cédric Fauvet, Business Développement France chez Neo4j.
Graphs in Banking Integration with AI and Machine Learning Technologies
At Neo4j we believe that ‘Graphs Are Everywhere’. In this session, we’ll be looking specifically at graphs within the Financial Services industry. We’ll review the types of data that are typically available within a bank, illustrate the graphs can be formed from that data, and discuss the use cases that those graphs can enable and support. The use cases presented will include Anti-Money Laundering and Fraud Detection and Prevention (including integration with AI and Machine Learning technologies), Regulatory Compliance (such as BCBS 239 and GDPR), Customer 360 View, Master Data Management, and Identity and Access Management. Many players in the Financial Services industry already rely on Neo4j's graph database
Cloud OnAir: Why You Need Graph Technology on GKE
To truly understand the value of your data, you need to uncover the connections. From Customer 360 to recommendation/pricing engines to travel, financial services, telecommunications and more, graph technology is the solution to do just that. In this webinar, we’ll describe the advantages of running Neo4j in the Google Kubernetes Enterprise (GKE) environment and show you how graph technology can be used to enhance existing applications or act as the basis for new graph-driven applications.
Neo4j Licensing. Which Edition is Right for You
Neo4j has long been recognised as the world's leading graph database, and now is expanding to be a true platform for Enterprise Graph Applications. Now is a better time than ever to explore which version of Neo4j you should be using considering the product features and/or services you want to benefit from. In this webinar, we'll walk through the different versions of Neo4j, discuss and show their remits, and present what the alternative (open source or commercial) licensing options may be. Specifically, we'll dig into the free open-source, free commercial (for individuals, startups, and academics) and paid commercial options and answer any questions that you may have.
Graph Powered Digital Asset Management with Neo4j
Managing digital assets and instance-level metadata is critical to many company's business. It affects everything from content availability to analysis of customer usage behavior to the ability to get insights to monetization potential, and drive business innovation. In this session, Jesús will explain how companies are leveraging the advantages of a graph platform like Neo4j over traditional relational databases and other types of data and metadata stores for DAM and discuss the success stories of Scripps Networks and Adobe Behance.
Intelligence led Policing with Neo4j
To help you explore how to prevent and solve crimes using the power of graphs we have developed the Crime Investigation Sandbox. Data for the Crime Investigation Sandbox is organised based on the POLE data model, commonly used in policing and other security-related use cases. POLE stands for Persons, Objects, Locations, and Events. The sandbox comes pre-loaded with sample data and a step-by-step guide with queries and explanations . In addition you might watch my video explaining the concept in detail. Everything you need to get going with your Crime Investigation!
How to get started with Bloom
Neo4j Bloom is a breakthrough graph communication and visualization product that allows graph novices and experts the ability to communicate and share their work, thoughts, and plans with peers, managers, and executives. Its illustrative, codeless search to storyboard design makes it the ideal interface for non-technical project participants to share in the innovative work of their graph analytics and development teams. In this webinar, you will learn how to
Maintaining your Data Lineage in a Graph
Lju Lazarevic, Neo4j Data lineage is an important component in many projects, including master data management, customer journey tracking, and regulatory compliance. It also presents many challenges in its implementation. In this webinar we will explore how and why Neo4j is a natural fit for your data lineage challenges. #DataLineage #GraphDatabase #MDM
Future-Proof Your Risk Management and Compliance with Graph Technology
Nav Mathur, Senior Director - Global Solutions - Neo4j In the aftermath of the Lehman crisis of 2008, financial services firms face a number of new regulations and risk management challenges. One key regulation is the Fundamental Review of the Trading Book (FRTB), which is part of the upcoming Basel IV set of reforms. The new regulations require banks to reserve sufficient capital to maintain solvency through market downturns and avoid the need for government bailouts. However, in this challenge lies an opportunity
POLE Investigations with Neo4j
The POLE data model - Person, Object, Location, Event - is commonly applied to security and investigative use cases such as policing, anti-terrorism, border control, and social services. It’s also a great fit for the graph and graph algorithms. Joe Depeau demonstrates how Neo4j, graph algorithms, and the POLE data model can support police and social services investigations and generate real-time insights using the Neo4j browser as well as some sample Tableau visualisations.
Graph Solutions for Telecoms
Telecommunications is all about connections. Graph databases are, therefore, an excellent fit for modelling, storing and querying telecommunications data of all kinds. Whether managing increasingly complex network structures, ever-more-diverse product lines and bundles, or customer satisfaction and retention in today’s competitive environments, graph databases enable businesses to become more agile by leveraging their connected data.