Master Data Management & Vin
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Master Data Management & Vin - Aymeric Fournier - Wine Data System
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Recommandé
Ouverture des 5e Rencontres nationales du marketing territorial
Décliner sa marque pour l'adapter à la diversité des cibles
Comment gérer et décliner ses marques au niveau national mais aussi international ? Quel rayonnement et quelles retombées pour le territoire ? Présentation du cas de la marque Salons de GL Events.
Marie-Odile Fondeur, directrice générale du Sirha
Neo4j - Cas d'usages pour votre métier
Neo4j - Cas d'usages pour votre métier - Cédric Fauvet
GraphDay Paris
Neo4j Makes Graphs Easy? - GraphDay AmandaLaucher
This document discusses using Neo4j to model road safety data such as alcohol breath testing, vehicle data, and driver data as a graph for easier querying. It suggests importing this data into Neo4j and provides a demo and information on tooling and drivers. The presenter, Amanda Laucher, takes questions on the topic.
Neo4j Makes Graphs Easy
This document provides a 3-step process for modeling data as a graph using Neo4j:
1. Define the data and relationships
2. Load the data into Neo4j from CSV files
3. Query the data using the Cypher query language
It includes examples of loading customer, product, order and other data from the Northwind sample database into a Neo4j graph and defining relationships between nodes. Queries are demonstrated to find employees and their managers and products supplied by different suppliers.
Recommandé
Ouverture des 5e Rencontres nationales du marketing territorial
Décliner sa marque pour l'adapter à la diversité des cibles
Comment gérer et décliner ses marques au niveau national mais aussi international ? Quel rayonnement et quelles retombées pour le territoire ? Présentation du cas de la marque Salons de GL Events.
Marie-Odile Fondeur, directrice générale du Sirha
Neo4j - Cas d'usages pour votre métier
Neo4j - Cas d'usages pour votre métier - Cédric Fauvet
GraphDay Paris
Neo4j Makes Graphs Easy? - GraphDay AmandaLaucher
This document discusses using Neo4j to model road safety data such as alcohol breath testing, vehicle data, and driver data as a graph for easier querying. It suggests importing this data into Neo4j and provides a demo and information on tooling and drivers. The presenter, Amanda Laucher, takes questions on the topic.
Neo4j Makes Graphs Easy
This document provides a 3-step process for modeling data as a graph using Neo4j:
1. Define the data and relationships
2. Load the data into Neo4j from CSV files
3. Query the data using the Cypher query language
It includes examples of loading customer, product, order and other data from the Northwind sample database into a Neo4j graph and defining relationships between nodes. Queries are demonstrated to find employees and their managers and products supplied by different suppliers.
GraphConnect 2014 SF: Betting the Company on a Graph Database - Part 2
GraphConnect 2014 SF: Betting the Company on a Graph Database - Part 2
presented by Aseem Kishore, FiftyThree
GraphConnect 2014 SF: The Business Graph
GraphConnect 2014 SF: The Business Graph
presented by Kurt Freytag, Head of Product and Engineering, CrunchBase
Graph all the things
This document discusses using graph databases to model relationships between data. It provides examples of using a graph database called Neo4j to model relationships between people and groups they belong to. It also discusses how a graph database can be used to model the complex relationships between wolves, other animals, and how their interactions affect river stability by following paths in the graph.
Metadata and Access Control
Tracking assets is about more than just the assets themselves, but also the related metadata: the properties of the assets and the relationships between them.
For example, if you have a number of retail stores, you need to track properties like addresses, square footage and total annual sales per store. However, some of that data is highly sensitive (like sales figures), and you need a system to control which users have access to which data categories and assets.
In this webinar, Brian Underwood shows you how to create simple and reusable code to build an access control system using Ruby on Rails similar to the one described above, including a consistent UI and the ability to grant or deny access to users – all powered by Neo4j.
GraphTalk Frankfurt - Master Data Management bei der Bayerischen Versicherung
Master Data Management bei der Bayerischen Versicherung
Graph your business
This document discusses graphing businesses using property graph models and Neo4j. It begins with an overview of where graph databases currently are and how data used to be stored in relational databases. It then introduces the labeled property graph model and how connected data allows for new types of queries. Examples are provided of graph queries and use cases across different industries. Resources for learning more about graph databases and Neo4j are listed at the end.
GraphConnect 2014 SF: Neo4j at Scale using Enterprise Integration Patterns
The document discusses scaling a graph database for a media recommendation application. It describes using Neo4j to build a comprehensive entertainment graph to power recommendations. It also discusses various techniques for optimizing batch writes, transaction handling, and running custom algorithms at scale including using AWS spot instances and caching strategies.
Graph Search and Discovery for your Dark Data
To achieve new insights within your company you don't necessarily have to accumulate and crunch a lot of new tracking and tracing data - much key information already exists in disconnected data silos, stored in different databases and systems. By bringing the relevant pieces together in a single place and connecting the pieces of the puzzle at the right points you can gain new insights into your existing data and business.
Graph databases offer the unique opportunity to easily cross-reference and connect disparate, variably structured datasets from many sources in a single place.
It is then available for ad-hoc querying and exploration as well as strategic decision making.
As a graph is a flexible and malleable data structure, it can evolve as you add new datasets or draw new connections from the insights or assumptions gained.
This source of decision making will not be your old-school MDM data cemetery, but a flexible instrument that you can use, reuse, discard and recreate for different goals and purposes.
Getting the data into the graph is only one interesting side of the coin, the other similarly challenging one is how to make the freshly spun web of knowledge available for different types of users.
Technical users have it easy - they can yield Cypher, a powerful graph query language for arbitrary graph pattern matching, filtering, projection and aggregation of relevant data.
For non-technical users a variety of tools and toolkits are available that use the inherent structures and meta information of the graph (labels, types, relationships and properties) to provide visual or natural language interaction to drill down, look at interesting facets, connect and correlate interesting information.
Under the hood those tools use the same graph query language to do their job.
This talk will cover both aspects of this exciting opportunity.
We'll look at how to aggregate, import and connect information from disparate data-sources into a dynamic, flexible graph model and the means, tools and techniques for graph search and discovery on top of that fabric.
The tools we use are the graph database Neo4j, its query language Cypher which also provides comprehensive import and data transformation abilities, as well as several tools and libraries from the Neo4j ecosystem to provide visual and textual graph search and exploration.
Storyline Draft: http://gist.asciidoctor.org/?dropbox-14493611%2Ftalk_javazone.txt
Transparency One : La (re)découverte de la chaîne d'approvisionnement
Transparency One : La (re)découverte de la chaîne d'approvisionnement - Julien Durand
GraphDay Paris
Graph Your Business - GraphDay JimWebber
This document discusses using graph databases to represent business data and provides examples of how different industries are using graph databases. It introduces the labeled property graph data model and provides examples of graph queries. It also lists some common uses of graph databases and resources for learning more about graph databases and Neo4j.
GraphDay Noble/Coolio
GraphDay London 2014
Graphs in the Real World: Top Use Cases for Graph Databases With Aleksa Vukotic of Noble Energy and Paul Gilligan of Coolio Labs
Meetup Analytics with R and Neo4j
The majority of NoSQL meetups in London are hosted on meetup.com and luckily for us meetup.com has an API that allows us to extract all the corresponding data - groups, events, venues, members and RSVPs.
In this talk Mark will show how we can use R to gain quick insights into the data using tools like dplyr and ggplot2. We'll also do some social network analysis of the attendees of London's meetup scene using igraph.
Finally we'll look at how we could bring together all these insights into a brand new Clojure front end for the meetup website.
Graphs fun vjug2
This document summarizes Michael Hunger's presentation on how graphs make databases fun again. Some key points:
- Traditional relational databases have issues modeling connected data and performing complex queries over relationships. Graph databases like Neo4j can more naturally represent connected data as nodes and relationships.
- Neo4j was originally created to solve issues modeling connected data for a digital asset management system. It uses a graph data model and allows complex relationship queries through its Cypher query language.
- The document demonstrates importing meetup data into Neo4j and running queries to find connections between users, groups, and topics. It also shows examples of querying actor relationships and movie data.
- Tools are presented
GraphTalk Frankfurt - Einführung in Graphdatenbanken
This document provides an agenda for the Neo4j GraphTalks event in June 2015. The agenda includes: breakfast and networking, an introduction to graph databases and Neo4j, a presentation on digital asset management at Lufthansa, a presentation on master data management at Bayerische Versicherung, and an open discussion period. The document also includes examples of using Neo4j for applications such as logistics processing, recommendations, and network management.
Graph all the things - PRathle
This document provides an introduction to graph databases and their advantages over relational and NoSQL databases for modeling connected data. It discusses how graph databases can unlock value from data relationships in areas like recommendations, fraud detection, and identity management. The document explains that graph databases allow flexible modeling of nodes and relationships, powerful graph queries, and the ability to easily add new types of data over time. It presents the example of Neo4j as the leading graph database and discusses how early adopters were able to gain competitive advantages through new applications and insights leveraging their connected data in a graph model.
GraphTalks - Einführung
The document announces a Neo4j GraphTalks event in February 2016 focusing on semantic networks. The agenda includes an introduction to graph databases and Neo4j, a presentation on semantic product data management at Schleich, and a talk on building semantic networks quickly with Structr and Neo4j. An open discussion period will follow with additional speakers.
RDBMS to Graphs
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
Neo4j Makes Graphs Easy: Nicole White
This document provides an overview of using Neo4j to model graph data from the Northwind database and compare it to using SQL. It demonstrates how Neo4j allows for more intuitive graph queries compared to SQL through examples of finding employee reporting structures and a top salesperson's products. The document also briefly mentions how to import data from SQL into Neo4j and connect to Neo4j through REST drivers to build applications.
Atelier - Architecture d’applications de Graphes - GraphSummit Paris
Atelier - Architecture d’applications de Graphes
Participez à cet atelier pratique animé par des experts de Neo4j qui vous guideront pour découvrir l’intelligence contextuelle. En utilisant un jeu de données réel, nous construirons étape par étape une solution de graphes ; de la construction du modèle de données de graphes à l’exécution de requêtes et à la visualisation des données. L’approche sera applicable à de multiples cas d’usages et industries.
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Contenu connexe
En vedette
GraphConnect 2014 SF: Betting the Company on a Graph Database - Part 2
GraphConnect 2014 SF: Betting the Company on a Graph Database - Part 2
presented by Aseem Kishore, FiftyThree
GraphConnect 2014 SF: The Business Graph
GraphConnect 2014 SF: The Business Graph
presented by Kurt Freytag, Head of Product and Engineering, CrunchBase
Graph all the things
This document discusses using graph databases to model relationships between data. It provides examples of using a graph database called Neo4j to model relationships between people and groups they belong to. It also discusses how a graph database can be used to model the complex relationships between wolves, other animals, and how their interactions affect river stability by following paths in the graph.
Metadata and Access Control
Tracking assets is about more than just the assets themselves, but also the related metadata: the properties of the assets and the relationships between them.
For example, if you have a number of retail stores, you need to track properties like addresses, square footage and total annual sales per store. However, some of that data is highly sensitive (like sales figures), and you need a system to control which users have access to which data categories and assets.
In this webinar, Brian Underwood shows you how to create simple and reusable code to build an access control system using Ruby on Rails similar to the one described above, including a consistent UI and the ability to grant or deny access to users – all powered by Neo4j.
GraphTalk Frankfurt - Master Data Management bei der Bayerischen Versicherung
Master Data Management bei der Bayerischen Versicherung
Graph your business
This document discusses graphing businesses using property graph models and Neo4j. It begins with an overview of where graph databases currently are and how data used to be stored in relational databases. It then introduces the labeled property graph model and how connected data allows for new types of queries. Examples are provided of graph queries and use cases across different industries. Resources for learning more about graph databases and Neo4j are listed at the end.
GraphConnect 2014 SF: Neo4j at Scale using Enterprise Integration Patterns
The document discusses scaling a graph database for a media recommendation application. It describes using Neo4j to build a comprehensive entertainment graph to power recommendations. It also discusses various techniques for optimizing batch writes, transaction handling, and running custom algorithms at scale including using AWS spot instances and caching strategies.
Graph Search and Discovery for your Dark Data
To achieve new insights within your company you don't necessarily have to accumulate and crunch a lot of new tracking and tracing data - much key information already exists in disconnected data silos, stored in different databases and systems. By bringing the relevant pieces together in a single place and connecting the pieces of the puzzle at the right points you can gain new insights into your existing data and business.
Graph databases offer the unique opportunity to easily cross-reference and connect disparate, variably structured datasets from many sources in a single place.
It is then available for ad-hoc querying and exploration as well as strategic decision making.
As a graph is a flexible and malleable data structure, it can evolve as you add new datasets or draw new connections from the insights or assumptions gained.
This source of decision making will not be your old-school MDM data cemetery, but a flexible instrument that you can use, reuse, discard and recreate for different goals and purposes.
Getting the data into the graph is only one interesting side of the coin, the other similarly challenging one is how to make the freshly spun web of knowledge available for different types of users.
Technical users have it easy - they can yield Cypher, a powerful graph query language for arbitrary graph pattern matching, filtering, projection and aggregation of relevant data.
For non-technical users a variety of tools and toolkits are available that use the inherent structures and meta information of the graph (labels, types, relationships and properties) to provide visual or natural language interaction to drill down, look at interesting facets, connect and correlate interesting information.
Under the hood those tools use the same graph query language to do their job.
This talk will cover both aspects of this exciting opportunity.
We'll look at how to aggregate, import and connect information from disparate data-sources into a dynamic, flexible graph model and the means, tools and techniques for graph search and discovery on top of that fabric.
The tools we use are the graph database Neo4j, its query language Cypher which also provides comprehensive import and data transformation abilities, as well as several tools and libraries from the Neo4j ecosystem to provide visual and textual graph search and exploration.
Storyline Draft: http://gist.asciidoctor.org/?dropbox-14493611%2Ftalk_javazone.txt
Transparency One : La (re)découverte de la chaîne d'approvisionnement
Transparency One : La (re)découverte de la chaîne d'approvisionnement - Julien Durand
GraphDay Paris
Graph Your Business - GraphDay JimWebber
This document discusses using graph databases to represent business data and provides examples of how different industries are using graph databases. It introduces the labeled property graph data model and provides examples of graph queries. It also lists some common uses of graph databases and resources for learning more about graph databases and Neo4j.
GraphDay Noble/Coolio
GraphDay London 2014
Graphs in the Real World: Top Use Cases for Graph Databases With Aleksa Vukotic of Noble Energy and Paul Gilligan of Coolio Labs
Meetup Analytics with R and Neo4j
The majority of NoSQL meetups in London are hosted on meetup.com and luckily for us meetup.com has an API that allows us to extract all the corresponding data - groups, events, venues, members and RSVPs.
In this talk Mark will show how we can use R to gain quick insights into the data using tools like dplyr and ggplot2. We'll also do some social network analysis of the attendees of London's meetup scene using igraph.
Finally we'll look at how we could bring together all these insights into a brand new Clojure front end for the meetup website.
Graphs fun vjug2
This document summarizes Michael Hunger's presentation on how graphs make databases fun again. Some key points:
- Traditional relational databases have issues modeling connected data and performing complex queries over relationships. Graph databases like Neo4j can more naturally represent connected data as nodes and relationships.
- Neo4j was originally created to solve issues modeling connected data for a digital asset management system. It uses a graph data model and allows complex relationship queries through its Cypher query language.
- The document demonstrates importing meetup data into Neo4j and running queries to find connections between users, groups, and topics. It also shows examples of querying actor relationships and movie data.
- Tools are presented
GraphTalk Frankfurt - Einführung in Graphdatenbanken
This document provides an agenda for the Neo4j GraphTalks event in June 2015. The agenda includes: breakfast and networking, an introduction to graph databases and Neo4j, a presentation on digital asset management at Lufthansa, a presentation on master data management at Bayerische Versicherung, and an open discussion period. The document also includes examples of using Neo4j for applications such as logistics processing, recommendations, and network management.
Graph all the things - PRathle
This document provides an introduction to graph databases and their advantages over relational and NoSQL databases for modeling connected data. It discusses how graph databases can unlock value from data relationships in areas like recommendations, fraud detection, and identity management. The document explains that graph databases allow flexible modeling of nodes and relationships, powerful graph queries, and the ability to easily add new types of data over time. It presents the example of Neo4j as the leading graph database and discusses how early adopters were able to gain competitive advantages through new applications and insights leveraging their connected data in a graph model.
GraphTalks - Einführung
The document announces a Neo4j GraphTalks event in February 2016 focusing on semantic networks. The agenda includes an introduction to graph databases and Neo4j, a presentation on semantic product data management at Schleich, and a talk on building semantic networks quickly with Structr and Neo4j. An open discussion period will follow with additional speakers.
RDBMS to Graphs
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
Neo4j Makes Graphs Easy: Nicole White
This document provides an overview of using Neo4j to model graph data from the Northwind database and compare it to using SQL. It demonstrates how Neo4j allows for more intuitive graph queries compared to SQL through examples of finding employee reporting structures and a top salesperson's products. The document also briefly mentions how to import data from SQL into Neo4j and connect to Neo4j through REST drivers to build applications.
En vedette (20)
GraphConnect 2014 SF: Betting the Company on a Graph Database - Part 2
GraphConnect 2014 SF: Betting the Company on a Graph Database - Part 2
GraphTalk Frankfurt - Master Data Management bei der Bayerischen Versicherung
GraphTalk Frankfurt - Master Data Management bei der Bayerischen Versicherung
GraphConnect 2014 SF: Neo4j at Scale using Enterprise Integration Patterns
GraphConnect 2014 SF: Neo4j at Scale using Enterprise Integration Patterns
Transparency One : La (re)découverte de la chaîne d'approvisionnement
Transparency One : La (re)découverte de la chaîne d'approvisionnement
GraphTalk Frankfurt - Einführung in Graphdatenbanken
GraphTalk Frankfurt - Einführung in Graphdatenbanken
Plus de Neo4j
Atelier - Architecture d’applications de Graphes - GraphSummit Paris
Atelier - Architecture d’applications de Graphes
Participez à cet atelier pratique animé par des experts de Neo4j qui vous guideront pour découvrir l’intelligence contextuelle. En utilisant un jeu de données réel, nous construirons étape par étape une solution de graphes ; de la construction du modèle de données de graphes à l’exécution de requêtes et à la visualisation des données. L’approche sera applicable à de multiples cas d’usages et industries.
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
FLOA - Détection de Fraude - GraphSummit Paris
Thibault Gossé, Chief Data & AI Officer, FLOA
Martin Schubenel, Fraud Project Manager, FLOA
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...
Romain CAMPOURCY – Architecte Solution, Sopra Steria
Patrick MEYER – Architecte IA Groupe, Sopra Steria
La Génération de Récupération Augmentée (RAG) permet la réponse à des questions d’utilisateur sur un domaine métier à l’aide de grands modèles de langage. Cette technique fonctionne correctement lorsque la documentation est simple mais trouve des limitations dès que les sources sont complexes. Au travers d’un projet que nous avons réalisé, nous vous présenterons l’approche GraphRAG, une nouvelle approche qui utilise une base Neo4j générée pour améliorer la compréhension des documents et la synthèse d’informations. Cette méthode surpasse l’approche RAG en fournissant des réponses plus holistiques et précises.
ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...
Charles Gouwy, Business Product Leader, Adeo Services (Groupe Leroy Merlin)
Alors que leur Knowledge Graph est déjà intégré sur l’ensemble des expériences d’achat de leur plateforme e-commerce depuis plus de 3 ans, nous verrons quelles sont les nouvelles opportunités et challenges qui s’ouvrent encore à eux grâce à leur utilisation d’une base de donnée de graphes et l’émergence de l’IA.
GraphSummit Paris - The art of the possible with Graph Technology
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GraphAware - Transforming policing with graph-based intelligence analysis
Petr Matuska, Sales & Sales Engineering Lead, GraphAware
Western Australia Police Force’s adoption of Neo4j and the GraphAware Hume graph analytics platform marks a significant advancement in data-driven policing. Facing the challenges of growing volumes of valuable data scattered in disconnected silos, the organisation successfully implemented Neo4j database and Hume, consolidating data from various sources into a dynamic knowledge graph. The result was a connected view of intelligence, making it easier for analysts to solve crime faster. The partnership between Neo4j and GraphAware in this project demonstrates the transformative impact of graph technology on law enforcement’s ability to leverage growing volumes of valuable data to prevent crime and protect communities.
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product Updates
David Pond, Lead Product Manager, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
KLARNA - Language Models and Knowledge Graphs: A Systems Approach
Lucas Smedley, Product Manager, Klarna
Tales from explorations into the synergies of LLMs and Knowledge Graphs.
INGKA DIGITAL: Linked Metadata by Design
Shirley Bacso, Data Architect, Ingka Digital
“Linked Metadata by Design” represents the integration of the outcomes from human collaboration, starting from the design phase of data product development. This knowledge is captured in the Data Knowledge Graph. It not only enables data products to be robust and compliant but also well-understood and effectively utilized.
Your enemies use GenAI too - staying ahead of fraud with Neo4j
Delivered by Michael Down at Gartner Data & Analytics Summit London 2024 - Your enemies use GenAI too: Staying ahead of fraud with Neo4j.
Fraudsters exploit the latest technologies like generative AI to stay undetected. Static applications can’t adapt quickly enough. Learn why you should build flexible fraud detection apps on Neo4j’s native graph database combined with advanced data science algorithms. Uncover complex fraud patterns in real-time and shut down schemes before they cause damage.
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptx
Delivered by Sreenath Gopalakrishna, Director of Software Engineering at BT, and Dr Jim Webber, Chief Scientist at Neo4j, at Gartner Data & Analytics Summit London 2024 this presentation examines how knowledge graphs and GenAI combine in real-world solutions.
BT Group has used the Neo4j Graph Database to enable impressive digital transformation programs over the last 6 years. By re-imagining their operational support systems to adopt self-serve and data lead principles they have substantially reduced the number of applications and complexity of their operations. The result has been a substantial reduction in risk and costs while improving time to value, innovation, and process automation. Future innovation plans include the exploration of uses of EKG + Generative AI.
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan
Look beyond the hype and unlock practical techniques to responsibly activate intelligence across your organization’s data with GenAI. Explore how to use knowledge graphs to increase accuracy, transparency, and explainability within generative AI systems. You’ll depart with hands-on experience combining relationships and LLMs for increased domain-specific context and enhanced reasoning.
Plus de Neo4j (20)
Atelier - Architecture d’applications de Graphes - GraphSummit Paris
Atelier - Architecture d’applications de Graphes - GraphSummit Paris
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...
ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...
ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...
GraphSummit Paris - The art of the possible with Graph Technology
GraphSummit Paris - The art of the possible with Graph Technology
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphAware - Transforming policing with graph-based intelligence analysis
GraphAware - Transforming policing with graph-based intelligence analysis
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product Updates
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product Updates
KLARNA - Language Models and Knowledge Graphs: A Systems Approach
KLARNA - Language Models and Knowledge Graphs: A Systems Approach
Your enemies use GenAI too - staying ahead of fraud with Neo4j
Your enemies use GenAI too - staying ahead of fraud with Neo4j
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptx
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptx
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan
Master Data Management & Vin
- 2. Problème
- 3. Solution
- 6. 4 expertises dédiées au monde du vin Développement WEB Développement Mobile Données vin Intégrateur Odoo
- 7. Clients cible: Professionnels du vin Négociants - Courtiers - Cavistes - Propriétaires - Distributeurs
- 8. Tech & Data Aymeric Anne Cécile Augustin Fanny Florent Enzo Fabien Guillaume Tech. Data Sales
- 11. ○ Manque de performance ○ Urbanisation rigide ○ Chargement des données peu aisé ○ Faible possibilité d’évolution Problème
- 18. ✓ Performance ✓ Facile à implémenter ✓ Souple d’utilisation ✓ Chargement facile des données ✓ Possibilité d’évolution Critères de choix
- 19. 4 rue Ferrère 33000 Bordeaux - winedatasystem.com