10 Tips for Responsible AI
As creators and users of artificial intelligence (AI), we have a duty to guide its development and application to increase accountability, fairness and public trust. AI systems require context and connections to have more responsible outcomes and make decisions similar to the way humans do. In this webinar, Amy Hodler, Analytics & AI Program Manager at Neo4j, will take us through 10 Tips for Responsible AI and how artificial intelligence (AI) can be more situationally appropriate and “learn” in a way that leverages adjacency to understand and refine outputs, using peripheral information and connections. Amy will discuss how graphs add essential context to guide more responsible AI that is more robust, reliable and trustworthy.
Graphs4Good: Identifying Human Trafficking and Money Laundering
January is National Human Trafficking Awareness Month. While you might be wondering what graph technology has to do with trafficking, we’ve found that similar to examining data through the lens of fraud prevention, data trends may also show evidence of criminal behaviors like money laundering or even human trafficking. Join Kathryn O’Donnell, CEO and Founder of Clovis Technologies, and Navneet Mathur, Senior Director of Global Solutions at Neo4j, for this Graphs4Good webinar examining how graph database technology helps identify such activity and bring perpetrators to justice.
Graphdatenbanken: die Macht der Beziehungen
Graphdatenbanken eignen sich ideal, um sowohl einzelne Datensätze als auch die Zusammenhänge zwischen den Daten - Menschen, Dokumente, Maschinen etc. - zu visualisieren und stark vernetzte Informationen abzufragen. Auf diese Weise kann ein Kontext hergestellt und Verbindungen dargestellt werden. Knowledge Graph ist einer der wichtigsten Use Cases von Graphdatenbanken, denn sie eignen sich ideal, um sowohl einzelne Datensätze als auch die Zusammenhänge zwischen den Daten zu visualisieren und stark vernetzte Informationen abzufragen. Ausschließlich Graphen ermöglichen near real-time Abfragen über bisher isolierte Datensilos hinweg. Je mehr Daten miteinander verknüpft werden, desto mehr Kontext entsteht, den die Graph-Algorithmen abfragen und für Entscheidungen heranziehen können. Dr. Alexander Jarasch, Head of Data and Knowledge Management beim Deutschen Zentrum für Diabetesforschung (DZD) erläutert wie Graphdatenbanken in Kombination mit relationalen Datenbanken genutzt werden können. Ziel der übergeordneten Datenbank, DZDconnect, ist die Entstehung eines Informationskontextes, in dem Wissenschaftler gemeinsam forschen, ohne Tests wiederholen zu müssen. DZDconnect liegt als Layer über den relationalen Datenbanken, um bestehende Systeme und Datensilos im DZD zu verknüpfen. „Die Technologien erleichtern es, medizinische Fragen aus unterschiedlichen Blickwinkeln und indikationsübergreifend zu betrachten“, erklärt Dr. Alexander Jarasch.
Graphs in Government: Achieving Total Cost Visibility for the U.S. Army
Maximizing cost visibility is a common goal for large organizations, including the United States Department of Defense. The United States Army is required to track operating and support costs for weapon systems including weapons definition, force structure, inventory, requisitions, maintenance, ammunition and more. That equates to a vast amount of data and growing data management complexities. To address these challenges and achieve total cost visibility, the Army adopted the Neo4j graph database to create, manage, and analyze data relationships across the logistics community. In this webinar, Jason Zagalsky, Federal Account Manager at Neo4j, will provide a brief overview of Neo4j and Graphs In Government, followed by Preston Hendrickson, Principal Systems Analyst at CALIBRE Systems, describing how modernizing its cost tracking system gave the Army the cost visibility needed to meet DoD objectives.
How Graph Technology Powers Chan Zuckerberg Initiative's Scientific Research Project
Meta, a Chan Zuckerberg Science Initiative project, is designed to help users explore scientific research as it evolves in real-time. Using machine learning, Meta quickly analyzes, connects and organizes millions of scientific articles and preprints to build a live map of scientific developments. In this webinar, you’ll learn more about the knowledge graph powering Meta, and how this technology connects data for groundbreaking scientific research.
Neo4j LIVE INTERVIEW All Graphs Are Not Created Equal 102919
The graph database market is heating up and a wide variety of database and analytics vendors large and small are entering the space. Since not all graph databases are created equal, how do you know where to start when investigating a potential solution? While Gartner recently called out graph databases as a Top 10 Technology Trend for 2019, many organizations have never purchased one. Consequently, most people don’t understand what to look for, or what’s different about buying a graph database compared to traditional alternatives. In this session, we will ask a panel of experts to help identify the questions to ask when evaluating a graph database as well as the decisions you’ll need to make to ensure the right solution is purchased and implemented.
Accelerate Innovation and Digital Transformation: How Neo4j Can Help
What’s the best way to understand your business challenge and accelerate innovation – Innovation Lab or Bootcamp? We would like to argue – it depends! The Innovation Lab is an onsite Design Sprint, where we educate business and technical users on the potential of graph technology and explore use cases by prototyping graph projects together with our customers. They gain a deep understanding of Graph Thinking and the possibilities in innovation and digital transformation within their organization. The Bootcamp is a more technical exercise where we educate the development team and co-create a small Proof of Concept based on a real-life dataset for a clearly identified use case. So, it depends on the customer’s specific needs and stage from what they benefit most – designing and prototyping versus creating.
Knowledge Graphs in Financial Services: How to Handle Disparate Data
Knowledge graphs are driving industry disruption and business transformation by bringing together previously disparate data, using connections for superior decision support, and adding context for more intelligent applications (including AI). In this session, we’ll walk through the fundamental elements of knowledge graphs including contextual relevance, dynamic self-updating, understandability with intelligent metadata, and the combination of heterogeneous data. Nav Mathur will discuss the 3 main types of knowledge graphs (context-rich search, external insights sensing, and enterprise NLP) that build on each other, and how and why real-world organizations are utilizing graphs as the building blocks for their applications. Also covered will be ideas on how to start building analytical applications on top of your knowledge graph using Neo4j Solution Frameworks quickly and easily.
Graphs in Retail Know Your Customers and Make Your Recommendation Engine Learn
t Neo4j we believe that “Graphs Are Everywhere”. In this session, we’ll be exploring graphs within the Retail industry. We’ll discuss a range of data that are commonly available within a retail organisation, both online and “brick and mortar". We’ll illustrate some graphs which can be created by linking together different elements of that data and discuss the retail use cases those graphs can enable and transform. We’ll specifically focus on use cases like Personalised Recommendations (with a live demo), Supply Chain Management, Logistics, and Customer 360. We'll also look at some relevant graph algorithms and talk about opportunities for integration with Artificial Intelligence/Machine Learning technologies, which can be used along with Neo4j to generate new value using retail data. eBay, Walmart, Wobi, and others already deploy Neo4j for use cases like price comparison or real-time contextual and learning recommendation engines.
Improve Machine Learning Predictions using Graph Algorithms
Graph enhancements to AI and ML are changing the landscape of intelligent applications. In this webinar, we’ll focus on using graph feature engineering to 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 illustrate a link prediction workflow using Spark and Neo4j to predict collaboration and discuss our missteps and tips to get to measurable improvements.
Live from Lyft HQ: How Lyft Drives Data Discovery
Live interview from July 9th with Mark Grover, Product Manager at Lyft. Mark is working on Lyft's data discovery and metadata engine, Amundsen, which uses Neo4j as the backend data source for data discovery. During this 25-min session, Mark takes a deep dive into Amundsen's architecture which leverages a comprehensive Neo4j knowledge graph, centralized metadata and PageRank to achieve amazing results by reducing the time it takes to discover data by 10x! ### Blog post from Lyft describing the problem and solution (Amundsen)
La potencia de la analítica y la visualización de datos
En esta webinar, nuestros destacados partners GraphEverywhere y Linkurious presentarán la base de datos Neo4j y la visualización de grafos de Linkurious junto con la perfecta integración de ambos. Además, también mostrarán casos de uso junto con una demo que integra a Neo4j con Linkurious. En resúmen, lo que aprenderás es
Professional Services - Fast Tracking your Neo4j Projects with Professional Services
As the graph industry has matured, many of Neo4j's customers are managing large-scale graph solution implementations. Every day the Neo4j Professional Services team helps customers make large and complex graph technology-based projects successful and deploy faster. In this webinar you will learn how the Neo4j Professional Services team is supporting customers. You will find out which activity suits your current project phase – from Innovation Lab or boot camp in the planning phase to pilots for testing and Managed Services for deploying and running graph databases.
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