L'émergence du web 2.0 et des réseaux sociaux a vu l’utilisateur devenir non seulement un consommateur, mais aussi un créateur de contenu. C’est donc naturellement par la modélisation en graphe, et en considérant l’utilisateur comme l’un des sommets du graphe induit par les relations qu’il entretient avec ses semblables, que l’on peut tirer le meilleur parti de ces données. Les méthodes d’analyse des réseaux sociaux, situées au confluent de la sociologie, du data mining et de la théorie des graphes, sont les principaux outils à notre disposition. Neo4j, base de données graphe native, munie de son écosystème analytique et de sa bibliothèque Graph Data Science (GDS), sont le choix incontournable de tout professionnel désirant réaliser une telle tâche de manière productive et scalable. Nous visiterons, au sein de ce webinaire, les différents cas d’usage, approches et méthodes de l’analyse des réseaux sociaux. Puis, via une démonstration basée sur un corpus de tweets, nous montrerons la pertinence de Neo4j/GDS pour détecter l’émergence d’une tendance ou d’un fait à partir de signaux faibles issus des médias sociaux.
Using data to make the most accurate, advantageous decisions has long been a critical part of any organisation’s business strategy. However, when the amount of data has grown to be almost unmanageable, is kept in multiple silos, and is subject to complex regulation, gaining competitive advantage becomes incredibly difficult. Combining graph databases and artificial intelligence seems like a way forward. In this webinar, we will take a look at what a graph database is, why it is a key part of a highly connected data world and how you can use technologies & services from Google Cloud and partners to make sense of this huge amount of data.
Ensuring compliance with laws, regulations, and policies is crucial for many heavily regulated institutions including banks, insurance companies, and government agencies. By efficiently combining different data sources and increasing data quality, knowledge graphs surface critical, actionable insights from complex connections and patterns that naturally occur in regulatory compliance data. The extracted insights have the potential of significantly improving, simplifying, and speeding up compliance efforts in any regulated organisation. In this presentation, we briefly introduce knowledge graphs and explain why they are relevant to many compliance issues. We then show a real-world example of a governance knowledge graph for a regulated government institution, explaining how it has been built and what value it has provided to the organization. We will discuss the challenges encountered along the way and how they have been tackled using Neo4j and GraphAware Hume.
In this 60-minute conversation with IDC, we will highlight the momentum and reasons why a graph data platform is a breakthrough solution for businesses in need of a flexible data model. Please join Mohit Sagar, Group Managing Director of CIO Network, as he hosts the conversation with Dr. Christopher Lee Marshall, Associate VP at IDC, and Nik Vora, Vice President of APAC at Neo4. During this very exciting discussion, you'll discover the insights and knowledge unlocked with the graph data platform.
Loading data into Neo4j often starts with a number of simple scripts. As your project grows, so does the number and size of your scripts: they quickly become hard to scale, hard to manage, and hard to maintain. Apache Hop is a new open-source data orchestration platform that was designed from the ground up to tackle these problems. In Hop, data developers visually design data pipelines that can run on the Hop native engine, or on Spark, Flink, Google Dataflow over Apache Beam. Hop has built-in life cycle management functionality to help you manage your projects and environments, version control with git, run unit tests, and more. Apache Hop is unparalleled in its support for Neo4j with over 20 plugins to design, write, and read from your graphs. What you’ll learn in this webinar: What is Apache Hop and how it works How to load data into and extract data from Neo4j with Apache Hop How Apache Hop pipelines scale with any workload and volume of data How Apache Hop handle data lineage, unit testing, version control, deployment life cycle, and much more
This 45-minute webinar will show how the partnership between Neo4j and Google Cloud provides best-in-class technology to address data challenges in the financial services industry. Hosted by Dr. Jim Webber, Chief Scientist and CTO, Field Ops at Neo4j, this session features Antoine Larmanjat, Technical Director, Office of the CTO, at Google Cloud on the importance of cloud-based, graph-powered AI and key considerations for data strategy.
Heard about graph databases? Curious about what they are and how they work? Want to know where they’re best used? Then this is the session for you! In this workshop we will: - Introduce you to graph databases - Cover approaches for identifying graph-shaped problems - Get our hands on our very first graph database experience where we will load and query data, using Neo4j Aura Free Useful links: - Neo4j Aura Free: https://dev.neo4j.com/aura-login - Slides: https://www.slideshare.net/neo4j/training-week-introduction-to-neo4j - Article on relational versus graph databases: https://dev.neo4j.com/rdbms-gdb
Neo4j, the leading enterprise graph platform, is now globally available on Amazon Web Services (AWS) as a fully managed, always-on database service. Neo4j Aura Enterprise on AWS empowers organizations to rapidly build mission-critical, intelligent cloud-based applications backed by the performance, scale, security, and reliability that only the most deployed and most trusted graph technology can provide. Customers like Levi Strauss & Co., Sainsbury’s, Siemens, The Orchard and Tourism Media are already using Aura Enterprise on AWS for fraud detection, regulatory compliance, recommendation engines, supply chain analysis, and much more. Watch this exclusive digital event to learn more about Neo4j Aura Enterprise on AWS. 00:00 Welcome & Introductions 02:16 The State of the Data and Analytics Market 15:13 Customer Panel Discussion: Unlocking the Power of Graph Databases on AWS 44:00 Building Modern Graph Applications with Neo4j Aura in the Cloud
Today’s modern, intelligent applications require tools that enable rapid application development and tight integration between the front and backend. At this 45-minute session led by Gregory King, Product Manager at Neo4j, you’ll learn about two key tools: Neo4j Desktop and Neo4j Browser. Join us for an overview of these tools and how to use them, the latest features, and new release information.
This 60-minute conversation with Forrester will unpack their decision to highlight graph data platforms in The Forrester Wave. We’ll also discuss why companies and governments worldwide are rapidly adopting graph data platforms and how they’re being used to drive growth and uncover hidden value in data.
Neo4j 4.3 is the latest update to Neo4j, the leading graph database for deployment in any production environment – on-premises, hybrid, or in the cloud. Join us for a special webinar exploring what’s new with Neo4j 4.3 and explaining how it builds on the most trusted, scalable, secure database for performance and data integrity. In this 45-minute session, Ivan Zoratti, Director of Product Management at Neo4j, will show you a demo of 4.3 and answer your questions.
This exciting 60-minute webinar features graph experts discussing the past, present, and future of artificial intelligence (AI). Neo4j Chief Scientist Dr. Jim Webber will be hosting Dr. Jianshu Weng, Head of SecureAI at AI Singapore, and David Berend, Google scholar and doctoral candidate at the Nanyang Technological University of Singapore, in an eye-opening discussion of AI and the Graph Data Platform.
Avec l’adoption croissante de la technologie des bases de données de graphes dans tous les secteurs d’activité, il est fondamental de comprendre les avantages de cette technologie afin de vous lancer avec les graphes. Dans ce webinaire d’introduction, nous présenterons la plateforme de graphes Neo4j, une suite d’applications et d’outils qui permettent de tirer parti des données, en mettant l’accent sur les outils suivants : La base de données de graphes Neo4j, une base de données de graphes native qui permet d’exploiter les données ainsi que leurs relations Neo4j Bloom, une application de visualisation facile à utiliser pour explorer et analyser les données de graphes dans différentes perspectives Dans ce webinaire de 45 minutes, vous découvrirez : La technologie Neo4j - le langage Cypher Les cas d’usage Neo4j - ou comment les relations créent de la valeur La visualisation de graphes Neo4j avec Bloom Une démonstration autour d’un graphe de connaissances
This 45-minute webinar will look into how the COVID-19 pandemic has caused fundamental changes in consumer behavior, supply chains, and routes to markets. You’ll learn that with a combination of knowledge graphs and graph-based analytics, supply chain companies can bring complex products to market on schedule, proactively take action to remediate potential issues, and mitigate risks through greater end-to-end visibility.
Graphs are everywhere. From marketing to operations to healthcare, the graph market is expected to boom to ~$2,5 billion by 2024. It is not a question of if graphs have a use case within your company but how to successfully deploy, govern and maintain your graph solution or application. Often beginning with a flagship use case that solves a business challenge, we will share our experience on how to demonstrate the value of graph technology within your company, define a clear strategy towards incorporating graphs into your data landscape, and being confident of the results your graph produces. Join Elaine Fannoÿ and Magali Thésias from Deloitte Belgium to dive into how best practices of design thinking and agile methodologies find their way into graph technologies.
Neo4j Bloom streamlines conversations and projects across teams. Its illustrative, codeless search-to-storyboard design makes Neo4j Bloom the ideal interface for non-technical project participants to share in the innovative work of their graph analytics and development teams. In this 45-minute session, you will learn about Neo4j Bloom from Anurag Tandon, Director of Product Management at Neo4j. Anurag will take you through the mechanics of the Neo4j Bloom, showing how easy it is for businesses to explore their applications through visual interaction with graphs.
Relationships are highly predictive of behavior, yet most data science models overlook this information because it's difficult to extract network structure for use in machine learning (ML). With graphs, relationships are embedded in the data itself, making it practical to add these predictive capabilities to your existing practices. That’s why we’re presenting and demoing the use of graph-native ML to make breakthrough predictions. This will cover: Different approaches to graph feature engineering, from queries and algorithms to embeddings How ML techniques leverage everything from classical network science to deep learning and graph convolutional neural networks 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/incoming data Why no-code visualization and prototyping is important
This 45-minute webinar will highlight how Neo4j Aura on Google Cloud is the perfect solution for cloud developers who want to gain insight from their data, regardless of location. We’ll hear from Kelly Stirman, Director, Outbound PM for Databases at Google Cloud, who will explain the importance of integration and connectivity with Google Cloud architecture, covering key considerations for data strategy. He’ll be joined by David Allen, Partner Solution Architect at Neo4j, who will discuss the top use cases and Neo4j’s cloud strategy as it pertains to the product roadmap.
Hosted by Neo4j Chief Scientist, Dr. Jim Webber, this event features two VPs of the APAC region – Richard Jones from Dataiku and Nik Vora from Neo4j – as they share insights from their experience using graphs with digital twins.
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
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.
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.
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.
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]
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
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.
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
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
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
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
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.
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.
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.
[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.
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
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.
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
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]