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Powering Next-Gen Applications with Embedded Graphs

Yesterday's breakthrough applications were driven by big data. Tomorrow's innovations will be driven by connected data. Whether your application provides customer analytics, fraud detection, or risk assessment, your users need connected data insights. In this panel discussion, experts from MANTA,… Read more →

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Neo4j – Wissen aus Daten wie nie zuvor

Statt Tabellen und Feldern, Knoten und Kanten. Mit Graphalgorithmen lassen sich komplizierte Datenbankabfragen in hoher Geschwindigkeit ausführen. Wer ein Kundennetzwerk für Big Data abbilden will, Betrugsfälle und Anomalien aufdecken muss oder eine Empfehlungsmaschine bauen möchte, setzt heute auf Graphen.

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On-Demand Webinars

 

Supervised Graph Machine Learning in Neo4j

Join us to hear about new supervised machine learning (ML) capabilities in Neo4j and learn how to train and store ML models in Neo4j with the Graph Data Science library (GDS). Most data science models ignore network structure, but graph technology helps create highly predictive features to ML models, which increase accuracy and answer complex questions based on relationships. The latest GDS update (v1.5) provides a new end-to-end model-building pipeline entirely in Neo4j so you can take advantage of state-of-the-art ML techniques and continually update your graph – all without leaving Neo4j. In this session, we’ll walk through how to generate representations of your graph using graph embeddings, create ML models for link prediction or node classification, and apply these models to add missing information to an existing graph or incoming graph data. You’ll also hear about other recent updates including new graph algorithms and memory optimization. Your questions will be answered throughout the webinar! Presenters: Dr. Alicia Frame and Amy Hodler

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Fraud Detection with Graphs at Danish Business Authority

Traditional fraud prevention measures focus on discrete data points such as specific accounts, individuals, devices or IP addresses. However, today’s sophisticated fraudsters escape detection by forming fraud rings with individuals paid, lured into or unknowingly fronting these activities. To uncover such fraud rings and the people behind them, it is essential to look beyond individual data points to the connections that link them. Neo4j uncovers difficult-to-detect patterns that far outstrip the power of a relational database. Enterprise organisations use Neo4j to augment their existing fraud detection capabilities to combat a variety of financial crimes including first-party bank fraud, credit card fraud, ecommerce fraud, insurance fraud and money laundering – and all in real time. Learn more how to battle fraud with the power of graph databases during this webinar. We are pleased to invite you to hear Marius Hartmann from Danish Business Authority talking about how they are combining graph analysis with machine learning to prevent fraud. In context of the COVID-19 compensation scheme controls, he will present use cases currently in production and explain why graph is a good fit for government authorities.

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Neo4j Aura Enterprise la base de données de graphes dans le cloud pour les grandes entreprises

Neo4j Aura Enterprise est une base de données de graphes cloud (DBaaS) conçue spécialement pour les entreprises qui créent des applications stratégiques/critiques, sans avoir besoin de gérer les ressources sous-jacentes. Aura Enterprise met à disposition toute la puissance des graphes (relations entre les données) dans un environnement cloud-natif qui permet de réaliser des requêtes rapides et d’obtenir des analyses et des perspectives, en temps réel.

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Neo4j: Graph Data Science for Dummies - A Discussion with the Authors

Join the authors of Graph Data Science For Dummies, Amy Hodler and Mark Needham, as they walk you through the foundations of graph data science.

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Graph Databases For Dummies: An Interview with the Authors

Join the authors of Graph Database For Dummies, Dr. Jim Webber, Chief Scientist and Rik Van Bruggen, Regional VP, Neo4j, for this 45-minute webinar, in which they walk you through graph database basics and outline a number of compelling reasons to use graph technology.

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Neo4j: Overcoming Enterprise Challenges Panel Discussion

Join us for this 45 minute panel discussion to hear first-hand from customers and partners who have gained meaningful insights and solved enterprise challenges using the Neo4j graph data platform.

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Analyzing Data Orchestration Lineage with Neo4j

With Apache Hop and Neo4j, you can draw valuable insights into your data loading processes, identify where things went wrong, view the detailed logs, and perform root cause analyses using graph queries. In this session, using Apache Hop as a data orchestration tool, we’ll show you how to use Hop to load data into Neo4j. Alternatively, we’ll show you how to use Neo4j to see how data is being loaded into Hop, by storing all the relevant lineage information into a graph.

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Graph Embeddings for Graph Native Machine Learning

Join us to understand how you can use graph-native machine learning in Neo4j to make breakthrough predictions. Previously only accessible to researchers and a very few advanced tech companies, Neo4j has democratized graph-based ML techniques that leverage deep learning and graph convolutional neural networks. Most data science models ignore network structure while graphs add highly predictive features to ML models, increasing accuracy and enabling otherwise unattainable predictions based on relationships. With the recent update to the Neo4j Graph Data Science library, anyone can take advantage of this state-of-the-science technique to create representations of your graph’s most significant features for new and more accurate predictions with the data you already have. In this session, we’ll explain our new graph embeddings and demonstrate using the GraphSAGE embedding results with our new ML catalog. We’ll also visualize the predictions of different models using Neo4j Bloom.

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Augmenter la valeur des données connectées avec la Graph Data Science Library de Neo4j

La Graph Data Science Library est une bibliothèque d’algorithmes orientés graphes intégrée à Neo4j. Elle comprend des algorithmes de centralité pour mesurer l’influence, de détection de communautés, de recherche de chemins, de calcul de similarité et de prédiction de liens. La Graph Data Science Library intègre des algorithmes à la pointe de la recherche et simplifie leur utilisation en tirant partie de la base de données de graphes Neo4j. Intervenant: Nicolas Rouyer, Senior Field Engineer chez Neo4j [FR]

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Deep Dive Graph Analytics for Fraud Detection

Traditional fraud prevention measures focus on discrete data points such as specific accounts, individuals, devices or IP addresses. However, fraudsters today have sophisticated ways to get away with using collaborative efforts and synthetic identities. To uncover such fraud rings, it is essential to look beyond individual data points to the relationships between them. Graph data science harnesses the power of connections to analyze data relationships, detect suspicious patterns and prevent fraudulent transactions. Join us for this webinar for a deep dive into graph analytics for fraud detection where we will discuss: - How to use graph data science to prevent fraud - How to improve fraud detection with graph feature engineering - How graph analytics benefit even non-technical fraud investigators Presenters: Amy Hodler, Graph Analytics & AI Program Director, Neo4j Dave Voutila, Solutions Engineer, Neo4j

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Webinaire Dooloo & Neo4j

Douleur chronique et qualite de vie : les donnees connectees au coeur de la prise en charge presente avec Laurence Sergheraert, co-fondateur et PDG de dooloo et Nicolas Rouyer, avant-ventes senior chez Neo4j. [FR]

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Supply Chain et Neo4j - Gérer les risque d'approvisionnement avec les graphes

Webinaire: Supply Chain et Neo4j - Gerer les risques d'approvisionnement avec les graphes Intervenant: Nicolas Rouyer, Senior Pre-Sales chez Neo4j [FR]

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Graphs: The New Realm for Access Management

Learn how Nulli, working with its clients, addresses the latest challenges faced by the modern Identity Access Management world. The realm of access management is expanding to address the increasing number of relationships required to support consumer analytics, employee regulatory compliance, GDPR, ML, IoT and so much more. Nulli applies the power of graph databases to modeling and traversing these sophisticated relationships, allowing access management software to evaluate contextual policy decisions. In this session, Seyed Hossein Ahmadinejad, Senior Identity and Access Management Architect with Nulli - Identity Management, will illustrate solutions supporting this expanded realm of access management. You will learn about the application of graph algorithms to model security roles, as well as the value of graphs when making access policy decisions.

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How Graph Databases Deliver Enterprise Grade Security

A new study from IBM suggests it takes 206 days on average for companies to discover a data breach and another 73 days to fix it. The slightest lapse in data security could cripple a business. Neo4j’s developers have invested over a decade in graph technology to provide the graph community with the security features needed to protect data, achieve business goals and minimize cost. This presentation will guide you through Security best practices for your Neo4j developments, ensuring your organization correctly mitigates cybersecurity threats and prepares for incident response. We’ll cover most of the Neo4j security features including: - Leveraging Neo4j 4.1 new role-based access capabilities - Working with third party extensions in extremely sensitive deployment - Best practices using Neo4j to store secure data

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Introduction à Neo4j

Découvrez Neo4j, les principaux cas d'utilisation des bases de données de graphes (recommandations, fraude, MDM, etc.), les propriétés de Neo4j qui rendent ces cas d'utilisation possibles ou encore la visualisation des graphes avec Bloom. Webinaire du 27 Août 2020 [FR]

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Insiders Guide to Enterprise Graph Data Science

An Insider’s Guide: Tips and Tricks to Get the Most from Graph Data Science in the Enterprise - Webinar on 12 August 2020 We have just released the Neo4j Graph Data Science (GDS) library 1.3, which now leverages Neo4j 4.x, has over 50 algorithms and includes enterprise features like support for role-based access control and multi-database. With the GDS framework, you get a production-ready platform for data science at an enterprise scale. Watch the recording to learn tips and insider tricks to tune your graph data science work for production scale, consistency and efficiency. We’ll share important information and demo features such as: - Nuanced algorithm parameters like seeding, thresholds and tolerances - Getting your graph transformed into the analytics workspace - The estimate function for better memory management - Role-based access control for fine-grained security - Exporting graphs for version control - Experimenting with alpha tier functions - Implementing your own algorithm with the Pregel API

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Graphs for Cyber Security.

Find out how graphs can support solving Cyber Security problems! In the first part of this webinar, we will find out what makes graph databases so unique and powerful, and how we can use them to solve complex Cyber Security use cases, such as fake accounts, workloads hacks or application control. In the second part of the webinar, we will demonstrate live how to build a suitable data model and which algorithms best to use to solve the respective problems. And we will provide tips and tricks to answer your questions. Webinar - 6 August 2020

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Introduction to Neo4j.

Data is both our most valuable asset and our biggest ongoing challenge. As data grows in volume, variety and complexity, across applications, clouds and siloed systems, traditional ways of working with data no longer work. Unlike traditional databases, which arrange data in rows, columns and tables, Neo4j has a flexible structure defined by stored relationships between data records. We'll discuss the primary use cases for graph databases Explore the properties of Neo4j that make those use cases possible Look into the visualisation of graphs Introduce how to write queries. Webinar - 23 July 2020

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Neo4j: Graph-Powered Machine Learning: An Interview with Dr. Alessandro Negro

Modern machine learning demands new approaches. A powerful ML workflow requires more than picking the right algorithms. You also need the right tools, technology, datasets and model to brew your secret ingredient: context. In his book, Graph-Powered Machine Learning, Dr. Alessandro Negro explores the new way of applying graph-powered machine learning to recommendation engines, fraud detection systems, natural language processing. By making connections explicit, graphs harness the power of context to help you build more accurate, real-time machine learning models. In this interview with the book’s author, you’ll learn more about: - The role of graph technology in machine learning applications. - How graphs provide better context to improve your ML understanding and workflow. - How graph data science enhances four of the most common recommendation techniques: content-based, collaborative filtering, session-based, and context-aware recommendations. - Data modeling considerations for graph-based recommendation engines. - How to approach designing a hybrid recommendation engine that incorporates multiple approaches.

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The Protein Regulatory Networks of COVID 19 A Knowledge Graph Created by Elsevier

In this webinar, Helena Deus talks about the COVID-19 knowledge graph that Elsevier created by applying text mining techniques on relevant papers. She also discusses why a knowledge graph was used. Shobhna Srivastava discusses Elsevier's Research Citation network. She talks about how the journey of trying to simplify the existing data processing pipeline, to optimise costs, and choose the right solution to the problem opens the doors to other potential use cases and innovation. Graph technology has been applied to the scientific research domain to enhance content discovery.

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Harnessing Graphs in a New Business Climate - A Panel Discussion

As economies continue to reopen across much of Europe and North America, many organizations turn to graph technology to help rapidly reconfigure and reset operations. Graphs are perfectly suited for handling connected data, like tracing connections through complex networks. Graphs can also identify complex relationships faster than human-only efforts by combining multiple isolated datasets and identifying missing data points. In this panel discussion with key Neo4j partners, we discuss using graph technology to help organizations balance efficiency and innovation in today’s challenging times.

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Analytique en temps réel avec Neo4j

[FR] Vous disposez de données et souhaitez en extraire de la connaissance et de la valeur, tout cela en temps réel ? C'est tout à fait possible, mais certains défis sont à surmonter : votre base de données transactionnelle pourrait ne pas supporter l'exécution d'algorithmes consommateurs ; et d'un autre côté, si vous isolez votre intelligence artificielle sur une machine à part, vous perdrez le côté temps réel. Comment articuler ces 2 aspects ? Nous vous présenterons une architecture d’analytique orientée événement, qui couple Neo4j, Kafka et la Graph Data Science Library : vos données connectées seront alors exploitées en temps réel et les résultats seront mis à disposition de la base de données transactionnelle.

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Neo4j Online Vertical Summit Government

Today’s government agencies – both at home and abroad – are beset by increasingly complex challenges within a more interconnected and ever-shifting political landscape. The technology solutions that ultimately overcome these challenges must harness the power of data connections. Governments around the world already use graph technology to fight crime, prevent terrorism, improve fiscal responsibility and provide transparency to their citizenry. Often these solutions involve connecting data across different applications or repositories, spanning disparate processes and departments. Join our online event to discover how graph databases have been helping governments to quickly make sense of obscured, divergent data and hear about our popular use cases and customer references. Agenda 10:00 - 10:40 - Graphs in Government - Jim Webber, Chief Scientist, Neo4j 10:40 - 11:20 - POLE Investigations (Persons, Objects, Locations, and Events) for Governments - Tom Geudens, Senior Presales Consultant, Neo4j 11:20 - 12:00 - Intelligence Use Cases for Graph Data - Michal Bachman, CEO, GraphAware 12:00 - 12:15 - Q&A Session

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Identifying Graph Shaped Problems

Graph databases are an excellent choice for storing and querying your connected data. They allow you to store information in a logical and complementary way that’s reflective of the domain you’re working in. However, when faced with the questions you’re looking to answer, it may not be immediately obvious that what you’re looking at is a graph question. In this session we will show you tips and tricks for identifying graph-shaped problems. This webinar is for everybody who wants or needs to learn how to spot certain structures in their data and understand if the data you are working with lends itself to working with a graph.

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Delivering Remote Services in the New Normal

The new normal for IT professionals is working out of home offices. While Neo4j Pre-Sales and Professional Services have always provided remote services, we have recently fine-tuned our remote delivery of workshops, trainings, bootcamps, health checks, expert services and more. We have boosted functionality, with extra conferencing tools, VPN and data security features, while offering more flexible schedules and timelines. In this webinar, Stefan Kolmar will present some of the Neo4j services packages and demonstrate examples of successful implementation and deployment of Neo4j based projects. The webinar will focus on adapting Neo4j services to the needs of today's world, maintaining productivity by enabling virtual teams to implement and deliver projects remotely. Neo4j webinar on 26 May 2020, presented by Stefan Kolmar

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Parcours de soins avec Neo4j French

Notre système de santé se modernise au travers d'un projet de loi qui fait la part belle au "parcours de soins". Comment arrêter de raisonner par secteur (soins de ville, hospitaliers, médico-sociaux) ? Comment adopter une approche personnalisée et centrée sur le parcours de soins d'un patient en particulier ? Comment décloisonner les données pour une approche globale qui examine les parcours de soins des autres patients atteints des mêmes maladies ? Dans ce webinaire, nous verrons comment utiliser la base de données de graphes Neo4j et l'interface d'exploration et de visualisation Bloom pour visualiser les expériences patient individuelles et agrégées. Nous utiliserons aussi la GRANDstack pour proposer une interface Web qui donne une vision globale sur ces parcours de soins. Au cours de cette démonstration, nous montrerons qu'établir les relations entre maladies, médicaments, diagnostics, parcours de soins, protocoles, etc. grâce aux graphes et à Neo4j permet de créer de la connaissance et de la valeur, en particulier en recensant les traitements les plus efficaces et en les recommandant au moment approprié aux patients qui en ont besoin. [FR]

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Graphe et Intelligence Economic l IA au service de la conformite

[FR] Lors de webinaire en partenariat avec Mondeca, nous aborderons des problématiques autour de l'IA. Graphes et Intelligence Economique : L'IA au service de la conformité Christophe Prigent, Directeur Général, Mondeca L'intelligence économique nécessite d'analyser toujours plus de contenus et données depuis des sources hétérogènes. L'IA offre de nouvelles opportunités pour identifier et qualifier les faits, les personnes et les entreprises sur lesquels s'applique cette analyse. Nous allons voir comment la gestion intelligente du contexte (connaissance du métier) et la puissance du graphe permettent d'exploiter la richesse des contenus non structurés. Un exemple d'application métier (détection de risques d'impayés, conformité des tiers) sera proposé démontrant le processus d'extraction et qualification des données au sein de contenus hétérogènes pour alimenter un graphe intelligent moteur de l'application métier. IA en pratique : la Graph Data Science Library Nicolas Rouyer, Avant-Ventes Senior, Neo4j Les librairies d'Intelligence Artificielle et de Machine Learning ne sont pas toujours faciles d'accès ni simples d'utilisation. Les algorithmes de graphes sont disponibles au travers de diverses implémentations, notamment en Python (graph-tool, networkx, RDFLib). Nous vous proposons de découvrir la Graph Data Science Library, intégrée à la base graphe Neo4j. Son API simplifiée et standardisée permet des configurations personnalisées. Elle implémente plus de 40 algorithmes dans 5 catégories : détection de communautés, centralité, similarité, parcours, détection de lien. Les algorithmes de la Graph Data Science Library sont fortement parallélisés et applicables sur des milliards de noeuds. Nous découvrirons son utilisation dans l'analyse de réseaux sociaux.

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How a Social Knowledge Graph Improves Remote Collaboration

Neo4j webinar, 23 April 2020 Neo4j has always been a geographically distributed company. Our employee count of 300+ people is spread over more than 20 countries. Consequently, remote collaboration is in our DNA. A Social Knowledge Graph can extract topics or moods from instant messaging to improve information sharing. It can also identify “lonely nodes” in times of remote working. In our latest webinar, we will demonstrate how we at Neo4j have leveraged our own technology to improve the efficiency of remote collaboration and avoid "lonely nodes". The webinar illustrates how instant messaging conversations (in this case from Slack) are used to analyze collaboration. And, finally, we will explain how companies can roll out a similar solution in less than two months.

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How Graph Algorithms Answer your Business Questions in Banking and Beyond

Graph algorithms are powerful tools, and there’s a lot of excitement about their applications for data science. It can sometimes be difficult, however - especially for those of us who aren’t data scientists - to know how they might be applied to a particular data set or a specific business problem. There are graph algorithms for centrality and importance measurement, community detection, similarity comparison, pathfinding, and link prediction. Which ones should you use on your data, and which ones might be most useful in answering your business questions? In this presentation, we’ll look at a few examples of Neo4j graph algorithms, and see how they can be applied to data and business problems from the banking industry. We’ll discuss what kinds of data are appropriate for different types of algorithms, show how to model and structure data to work with graph algorithms, and run through some real-world scenarios demonstrating the use of graph algorithms on a sample banking data set Webinar with Joe Depeau, Neo4j, April 15, 2020

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Improve Insight into Connected Data Leveraging Linkurious Enterprise

Building a Neo4j graph database is the first step in the journey toward making sense of your connected data. The next step is discovering how to use Neo4j to make smarter decisions in your business, like stopping fraud cases, investigating a cyber threat or tracking data lineage. This is the next installment in our Neo4j Partner Webinar series, offering live demonstrations, customer use cases and real-time Q&A sessions with select Neo4j partners. This 45-minute session co-hosted by Neo4j and Linkurious explores how teams of analysts use Linkurious to detect and investigate insights hidden within Neo4j. In this session, we’ll cover key topics including: - Extracting insights from dense multi-dimensional graphs - Automating investigation workflows to speed up your investigations - Using graph analytics to detect suspicious patterns

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Neo4j: Unparalleled Graph Database Scalability Delivered by Neo4j 4.0

For more than a decade, Neo4j developers have empowered the graph community to scale for data needs and achieve business goals, all while minimizing cost. Neo4j 4.0, the latest release from the world’s leading graph database, is the next step in those efforts and delivers a whole new level of scalability. The session features Dr. Alessandro Negro, noted graph database author and Chief Scientist at GraphAware, along with Patrick Wall, Director of Product Marketing at Neo4j. During this webinar, GraphAware explores the powerful scalability features of Neo4j 4.0 in a live demo using the COVID-19 Open Research Dataset.

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Graphs: The Secret to CCPA Success

Companies that collect personal data from Californians are subject to stringent regulations and reprimand – and the rest of North America is watching. The new California Consumer Privacy Act (CCPA) is now law and imposes stiff penalties on those that misuse or resell consumers’ private information. Even victims of data leaks face a reputational and financial risk. Join Yasir Ali, CEO and Founder of Predict Data and Nav Mathur, Senior Director of Global Solutions at Neo4j, for this webinar that addresses: - Highlights of the CCPA Regulation and what it means for organizations - Regulatory-overlap between CCPA, GDPR and other regulations - Personal Identifiable Information (PII) - Data Management in the new regulatory landscape - How can you find and track personal data within your organization - What is graph technology and why is it superior for compliance solutions - Key features of a robust privacy compliance solution

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Introducing Neo4j 4.0: The Next-Gen Graph Database Built by Developers for Developers

• Neo4j is excited to announce the availability of Neo4j 4.0, the most significant release in the graph technology market to date. Built on Neo4j’s proven native foundation, 4.0 enables developers to build applications that maximize productivity and innovation by extending the scale, security and developer agility offered by the world’s most widely-used graph database. This webinar is an exciting opportunity to hear from the Neo4j experts about how this next-gen graph database is sure to transform the graph technology industry. Emil Eifrem, CEO and Co-Founder of Neo4j, Dr. Jim Webber, Chief Scientist, Ivan Zoratti, Director of Product Management and Lance Walter, CMO, will be connecting with our community in this technical session to discuss: • How Neo4j 4.0’s unlimited scalability meets ever-growing data requirements • Building multiple Neo4j databases in a single Neo4j instance • Neo4j 4.0’s new fine-grained security rules.

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Comment le contexte rend le IA plus fiable et plus efficace

L'intelligence artificielle devrait être guidée non seulement par des normes techniques solides, mais aussi par des normes éthiques solides. Le contexte est un principe clé qui se recoupe dans ces deux domaines. L'information contextuelle permet non seulement d'obtenir des systèmes d'IA plus performants et plus précis, mais aussi d'offrir une perspective éthique plus claire à ceux qui la créent et la façonnent. Les systèmes d'IA et d'apprentissage machine sont plus efficaces, fiables et robustes lorsqu'ils sont étayés par des informations contextuelles fournies par des plateformes de graphes. Nicolas vous expliquera pourquoi et comment le contexte doit être intégré dans les systèmes d'IA afin de s'assurer qu'ils sont vraiment fiables, robustes et dignes de confiance.

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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.

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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.

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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. [DE]

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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.

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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.

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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.

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Graphs for Recommendation Engines: Looking Beyond Retail Social and Media

We’re all familiar with recommendations in a number of different areas of our lives. Recommendations for social media connections, e-commerce products, or streaming media content are ubiquitous. Perhaps less well known are applications for recommendations in different contexts, like education, HR, fraud, business process management, or offender rehabilitation. In this webinar, we will discuss some of these recommendations use cases in more detail, and look at how graph data can be used to model each domain and power a recommendations engine. We’ll also see an example use case demonstrated using Neo4j.

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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.

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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.

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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.

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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.

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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): https://eng.lyft.com/amundsen-lyfts-data-discovery-metadata-engine-62d27254fbb9?gi=c1730f1508dd Open source code repo and documentation: https://github.com/lyft/amundsenfrontendlibrary

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Graph Databases for AI: Guess the Future Given the Past

Graph technology is brilliant. In terms of graph theory, graphs are really well studied and have some amazing properties, especially for predictive analytics. But then there's AI: it's super-cool and also has some amazing abilities to guess the future given the past. So, which are we meant to choose? I'd argue we should use both. In this talk we'll see how graphs give us a framework for contextualizing the world around us. We'll explore how simple rules from graph theory can evolve our model to show how it might be in the future. But that's not all, we'll also see how we can take our graphs and feed them into our ML pipelines for better scores than simple row-wise data using graph neural nets. And we'll see how to learn on graphs directly with graph convolutional networks. Finally, we'll close the loop by asking our machine learning to tell us about other queries we should be running against the input graph to find patterns in data we don’t even know are valuable today. Watch this video on Neo4j.com here: https://go.neo4j.com/graphs-for-ai-guess-the-future-given-the-past-lp.html?ref=social-youtube For more webinars, check out the Neo4j Webinar library: https://neo4j.com/webinars/?ref=social-youtube #AI #MachineLearning #GraphDatabases

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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: - Cómo los grafos pueden transformar/impactar tu negocio - Casos de uso - Beneficios de la implantación conjunta de las tecnologías de grafos y de visualización de grafos

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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.

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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.

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