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
Relationships Matter: Using Connected Data for Better Machine LearningNeo4j
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
Relationships Matter: Using Connected Data for Better Machine LearningNeo4j
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
Maximize the Value of Your Data: Neo4j Graph Data PlatformNeo4j
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.
A Connections-first Approach to Supply Chain OptimizationNeo4j
Supply chain optimization is an unusual balancing act that requires finesse, skill and timely data. Every supply chain’s the key questions to be answered are:
What to Buy? -- what are the factors in determining your optimal product mix and set of suppliers.
How much to Buy? -- what are the most and least popular items at any given time interval
When to Buy? -- long lags in delivery timing may tax limit your flexibility and influence your inventory management practices.
We will illustrate an API-based solution that utilizes a Graph database platform to add demonstrable value to Supply Planning.
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.
The Virtualization of Clouds - The New Enterprise Data Architecture OpportunityDenodo
Watch full webinar here: https://bit.ly/3x7xVuR
Organizations worldwide are adopting a variety of the public cloud service providers (i.e. AWS, Google, Microsoft) and each have a portfolio of storage, compute, network, and security options. All of which create significant challenges in managing a hybrid and multi-cloud enterprise architecture. Even worse is the impact to the governance and integration of data from the clouds and physical infrastructure to support the broad array of analytics and operational requirements.
Can one public cloud provider meet all your needs today and in the future? How do you manage across multiple public and private clouds you have today and where your data exists? And, how would you manage and operate your multi-cloud and on-premises systems to gain value from your data in any of them? The Chief Research Officer at Ventana Research, Mark Smith, will expound the challenges and path ahead for virtualization and integration of your data and the clouds, setting an architectural path for best success.
In this webinar we discuss the primary use cases for Graph Databases and explore the properties of Neo4j that make those use cases possible.
We cover the high-level steps of modeling, importing, and querying your data using Cypher and give an overview of the transition from RDBMS to Graph.
Software Analytics with Jupyter, Pandas, jQAssistant, and Neo4j [Neo4j Online...Markus Harrer
Let’s tackle problems in software development in an automated, data-driven and reproducible way!
As developers, we often feel that there might be something wrong with the way we develop software. Unfortunately, a gut feeling alone isn’t sufficient for the complex, interconnected problems in software systems.
We need solid, understandable arguments to gain budgets for improvement projects or to defend us against political decisions. Though, we can help ourselves: Every step in the development or use of software leaves valuable, digital traces. With clever analysis, these data can show us root causes of problems in our software and deliver new insights – understandable for everybody.
If concrete problems and their impact are known, developers and managers can create solutions and take sustainable actions aligned to existing business goals.
In this meetup, I talk about the analysis of software data by using a digital notebook approach. This allows you to express your gut feelings explicitly with the help of hypotheses, explorations and visualizations step by step.
I show the collaboration of open source analysis tools (Jupyter, Pandas, jQAssistant and, of course, Neo4j) to inspect problems in Java applications and their environment. We have a look at performance hotspots, knowledge loss and worthless code parts – completely automated from raw data up to visualizations for management.
Participants learn how they can translate their unsafe gut feelings into solid evidence for obtaining budgets for dedicated improvement projects with the help of data analysis.
Webinar - Fighting Bank Fraud with Real-time Graph Database DataStax
Banking organizations are having to deal with highly complex fraud rings today. Fraudsters are spreading their activities across a large number of transactions and geographical regions in a more coordinated fashion, making traditional anomaly identification methods nearly obsolete. Join us for this on-demand webinar with Jie Wu, Director of Product Marketing, DataStax and guest speaker Scott Heath, Chief Revenue Officer, Expero for a discussion on how to use real-time graph database to effectively detect fraud and reduce risk in real time.
View recording: https://youtu.be/XzpTICtJ2Fk
Explore all DataStax webinars: https://www.datastax.com/resources/webinars
Beyond Big Data: Leverage Large-Scale ConnectionsNeo4j
Today’s CIOs and CTOs don’t just need to manage larger volumes of data – they need to generate insight from their existing data. In this case, the relationships between data points matter more than the individual points themselves. In order to leverage data relationships, organizations need a database technology that stores relationship information as a first-class entity. That technology is a graph database.
Attend this webinar to hear about:
1. Why graph technologies are essential for the future of increasingly connected data
2. How enterprises such Walmart, eBay, and UBS are using Neo4j’s native-graph platform for a diverse set of use cases, including security & fraud detection, real-time recommendation engines, master data and many more
3. And how Neo4j on IBM POWER8 can scale your massive graph data with real-time graph processing that’s entirely in-memory.
An introduction to Neo4j and Graph Databases. Learn about the primary use cases for Graph Databases and explore the properties of Neo4j that make those use cases possible.
Maximize the Value of Your Data: Neo4j Graph Data PlatformNeo4j
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.
A Connections-first Approach to Supply Chain OptimizationNeo4j
Supply chain optimization is an unusual balancing act that requires finesse, skill and timely data. Every supply chain’s the key questions to be answered are:
What to Buy? -- what are the factors in determining your optimal product mix and set of suppliers.
How much to Buy? -- what are the most and least popular items at any given time interval
When to Buy? -- long lags in delivery timing may tax limit your flexibility and influence your inventory management practices.
We will illustrate an API-based solution that utilizes a Graph database platform to add demonstrable value to Supply Planning.
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.
The Virtualization of Clouds - The New Enterprise Data Architecture OpportunityDenodo
Watch full webinar here: https://bit.ly/3x7xVuR
Organizations worldwide are adopting a variety of the public cloud service providers (i.e. AWS, Google, Microsoft) and each have a portfolio of storage, compute, network, and security options. All of which create significant challenges in managing a hybrid and multi-cloud enterprise architecture. Even worse is the impact to the governance and integration of data from the clouds and physical infrastructure to support the broad array of analytics and operational requirements.
Can one public cloud provider meet all your needs today and in the future? How do you manage across multiple public and private clouds you have today and where your data exists? And, how would you manage and operate your multi-cloud and on-premises systems to gain value from your data in any of them? The Chief Research Officer at Ventana Research, Mark Smith, will expound the challenges and path ahead for virtualization and integration of your data and the clouds, setting an architectural path for best success.
In this webinar we discuss the primary use cases for Graph Databases and explore the properties of Neo4j that make those use cases possible.
We cover the high-level steps of modeling, importing, and querying your data using Cypher and give an overview of the transition from RDBMS to Graph.
Software Analytics with Jupyter, Pandas, jQAssistant, and Neo4j [Neo4j Online...Markus Harrer
Let’s tackle problems in software development in an automated, data-driven and reproducible way!
As developers, we often feel that there might be something wrong with the way we develop software. Unfortunately, a gut feeling alone isn’t sufficient for the complex, interconnected problems in software systems.
We need solid, understandable arguments to gain budgets for improvement projects or to defend us against political decisions. Though, we can help ourselves: Every step in the development or use of software leaves valuable, digital traces. With clever analysis, these data can show us root causes of problems in our software and deliver new insights – understandable for everybody.
If concrete problems and their impact are known, developers and managers can create solutions and take sustainable actions aligned to existing business goals.
In this meetup, I talk about the analysis of software data by using a digital notebook approach. This allows you to express your gut feelings explicitly with the help of hypotheses, explorations and visualizations step by step.
I show the collaboration of open source analysis tools (Jupyter, Pandas, jQAssistant and, of course, Neo4j) to inspect problems in Java applications and their environment. We have a look at performance hotspots, knowledge loss and worthless code parts – completely automated from raw data up to visualizations for management.
Participants learn how they can translate their unsafe gut feelings into solid evidence for obtaining budgets for dedicated improvement projects with the help of data analysis.
Webinar - Fighting Bank Fraud with Real-time Graph Database DataStax
Banking organizations are having to deal with highly complex fraud rings today. Fraudsters are spreading their activities across a large number of transactions and geographical regions in a more coordinated fashion, making traditional anomaly identification methods nearly obsolete. Join us for this on-demand webinar with Jie Wu, Director of Product Marketing, DataStax and guest speaker Scott Heath, Chief Revenue Officer, Expero for a discussion on how to use real-time graph database to effectively detect fraud and reduce risk in real time.
View recording: https://youtu.be/XzpTICtJ2Fk
Explore all DataStax webinars: https://www.datastax.com/resources/webinars
Beyond Big Data: Leverage Large-Scale ConnectionsNeo4j
Today’s CIOs and CTOs don’t just need to manage larger volumes of data – they need to generate insight from their existing data. In this case, the relationships between data points matter more than the individual points themselves. In order to leverage data relationships, organizations need a database technology that stores relationship information as a first-class entity. That technology is a graph database.
Attend this webinar to hear about:
1. Why graph technologies are essential for the future of increasingly connected data
2. How enterprises such Walmart, eBay, and UBS are using Neo4j’s native-graph platform for a diverse set of use cases, including security & fraud detection, real-time recommendation engines, master data and many more
3. And how Neo4j on IBM POWER8 can scale your massive graph data with real-time graph processing that’s entirely in-memory.
An introduction to Neo4j and Graph Databases. Learn about the primary use cases for Graph Databases and explore the properties of Neo4j that make those use cases possible.
Neo4j GraphTalks Oslo - Graph Your Business - Rik Van Bruggen, Neo4jNeo4j
The Neo4j graph database is the fastest growing database engine in the market and has hundreds of customer references across Europe and globally, solving significant technology problems for large Enterprises in Finance, Telco, Retail, Utilities, Logistics and Internet sectors. Typical use cases are Recommendations, Fraud Detection, MDM, Network and Software Analysis and Optimization, Identity and Access Management.
AI, ML and Graph Algorithms: Real Life Use Cases with Neo4jIvan Zoratti
I gave this presentation at DataOps 19 in Barcelona.
You will find information about Neo4j and how to use it with Graph Algorithms for Machine Learning and Artificial Intelligence.
Connecta Event: Big Query och dataanalys med Google Cloud PlatformConnectaDigital
Avancerad dataanalys och ”big data” har under de senaste åren klättrat på trendlistorna och är nu ett av de mest prioriterade områdena i utvecklingen av nya tjänster och produkter för ledarföretag i det digitala landskapet.
Informationen som byggs upp i systemen när kundmötena digitaliseras har visat sig vara guld värt. Här finns allt vi behöver veta för att göra våra affärer mer effektiva.
Sedan sommaren 2013 har Connecta tillsammans med Google ett etablerat samarbete för att hjälpa våra kunder med övergången till moln-tjänster för bland annat avancerad dataanalys. För att göra oss själva redo att hjälpa våra kunder har vi under ett antal år utvecklat såväl kunskaper som skaffat oss erfarenheter kring Googles olika moln-produkter, som exempelvis ”Big Query”.
Big Query är ett molnbaserat analysverktyg och en del av Google Cloud Platform. Big Query gör det möjligt att ställa snabba frågor mot enorma dataset på bara någon sekund. Big Query och Google Cloud Platform erbjuder färdiga lösningar för att sätta upp och underhålla en infrastruktur som med enkla medel gör allt detta möjligt.
På Connecta Digital Consultings tredje event för våren introducerade vi våra kunder och partners i koncepten dataanalys och Big Query.
Under eventet berördes följande punkter:
- Big Data och Business Intelligence (BI)
- “The Google Big Data tools” – framgångsfaktorer och hur man kommer igång
- Google Cloud Platform och hur man genomför en framgångsrik molnsatsning
Vi presenterade case och berättade om viktiga lärdomar vi dragit i samarbetet med Google och våra kunder.
As many industries, banking is undergoing a fundamental change because of the software revolution. No longer are banks competing only on interest rates and having the best traders, these days customer experience and having the best engineers are the focus. In this changing world, banks compete with new start-ups, the so-called Fintechs, and with large platform organisations such as Google, Facebook and Apple. At ING, we believe that staying ahead of the game means changing how we interact with our customers, no longer a traditional model of waiting for the customers to come to the bank through our website or apps, but to actively reach out to the customer with information that is relevant to him or her in order to make their financial life frictionless. Many of these changes are driven by reacting to all events that are relevant to the customer, and using streaming analytics to be able to reach out to the customer in milliseconds after the event occurs. Apache Flink is key for ING to achieve this. This presentation addresses how ING approaches the challenge, the role that Apache Flink plays, and the consequences regulations have on how we work with Open Source in general, and with Apache Flink (and data Artisans) in particular. This keynote takes place at Kino 3.
The Data Platform for Today's Intelligent Applications.pdfNeo4j
Do you know how graph technology is used in today’s data-driven applications? We’ll get you up to speed and introduce you to the Neo4j product portfolio.
Apidays Paris 2023 - Building APIs At Scale, Ado Trakic, Capital Oneapidays
Apidays Paris 2023 - Software and APIs for Smart, Sustainable and Sovereign Societies
December 6, 7 & 8, 2023
Building APIs At Scale: Delivering Products Faster
Ado Trakic, Enterprise Architect - API CoE at Capital One
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Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
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Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
Getting started with Hadoop on the Cloud with BluemixNicolas Morales
Silicon Valley Code Camp -- October 11, 2014.
Session: Getting started with Hadoop on the Cloud.
Hadoop and Cloud is an almost perfect marriage. Hadoop is a distributed computing framework that leverages a cluster built on commodity hardware. The Cloud simplifies provisioning of machines and software. Getting started with Hadoop on the Cloud makes it simple to provision your environment quickly and actually get started using Hadoop. IBM Bluemix has democratized Hadoop for the masses! This session will provide a brief introduction to what Hadoop is, how does cloud work and will then focus on how to get started via a series of demos. We will conclude with a discussion around the tutorials and public datasets - all of the tools needed to get you started quickly.
Learn more about BigInsights for Hadoop: https://developer.ibm.com/hadoop/
apidays LIVE Hong Kong - The Future of Legacy - How to leverage legacy and on...apidays
apidays LIVE Hong Kong - The Open API Economy: Finance-as-a-Service & API Ecosystems
The Future of Legacy - How to leverage legacy and on-prem assets in your digital transformation with Digital-Driven Integration
Zeev Avidan, Chief Product Officer of OpenLegacy
z Systems redefining Enterprise IT for digital business - Alain PoquillonNRB
IBM z Systems with the new z13 is the backbone infrastructure for the evolving digital era. Built on over 50 years of experience and billions of dollars in developing leading-edge technology, it is at the forefront of modern Information Technology. On different domains. Mr. Poquillon illustrates IBMs’ z13 pre-eminence by highlighting its assets such as its shared-everything approach and centralized management of resources that make it naturally fit for cloud; its hybrid transaction/analytics processing capabilities that provide real-time analytics more efficiently to in-process transactional data, and finally its ability to provide the scale and performance a business needs to survive the mobile and social onslaught.
A webinar on how Neo4j customers like Nasa, AirBnB, eBay, government agencies, investigative journalists and others are building Knowledge Graphs to inform today and tomorrow’s solutions.
Network and IT Ops Series: Build Production Solutions Neo4j
Jeff Morris, Director, Neo4j:Are you building a breakthrough product or extending an existing one? Do you need introduce new capabilities based on insights from data relationships? If so, you should consider embedding a graph database.
For software providers building products to assure quality network operations or security, using an embedded graph database may open new customer opportunities. Watch this webinar to learn how you can easily differentiate your applications and take your solutions to market faster with a native graph database like Neo4j.
Atelier - Architecture d’applications de Graphes - GraphSummit ParisNeo4j
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 connaissancesNeo4j
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 ParisNeo4j
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 ParisNeo4j
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.
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...Neo4j
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 ...Neo4j
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 TechnologyNeo4j
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.
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...Neo4j
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.
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 2024Neo4j
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.
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 analysisNeo4j
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 UpdatesNeo4j
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.
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 Neo4jNeo4j
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.pptxNeo4j
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 MilanNeo4j
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.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
2. 7/10
20/25
7/10
Top Retail Firms
Top Financial Firms
Top Software Vendors
Anyway You Like It
Neo4j - The Graph Company
The Industry’s Largest Dedicated Investment in Graphs
2
Creator of the Label
Property Graph and
Cypher language at the
core of the GQL ISO project
Thousands of Customers
World-Wide
HQ in Silicon Valley, offices
include London, Munich,
Paris & Malmö
Industry Leaders use Neo4j
On-Prem
DB-as-a-Service
In the Cloud
3. Connections in Data are as
valuable as the Data itself
Networks of People Transaction Networks
Bought
Bought
Viewed
Returned
Bought
Knowledge Networks
Plays
Lives_in
In_sport
Likes
Fan_of
Plays_for
E.g., Risk management, Supply
chain, Payments
E.g., Employees, Customers,
Suppliers, Partners,
Influencers
E.g., Enterprise content,
Domain specific content,
eCommerce content
Knows
Knows
Knows
Knows
4. 4
Harnessing Connections Drives Business Value
Enhanced Decision
Making
Hyper
Personalization
Massive Data
Integration
Data Driven
Discovery & Innovation
Product Recommendations
Personalized Health Care
Media and Advertising
Fraud Prevention
Network Analysis
Law Enforcement
Drug Discovery
Intelligence and Crime
Detection Product
& Process Innovation
360º view of customer
Compliance
Optimize Operations
Data Science
AI & ML
Fraud Prediction
Patient Journey
Customer Disambiguation
Transforming Industries
5. Neo4j is an enterprise-grade native graph database and associated tools:
• Store, reveal and query data and data relationships
• Traverse and analyze data to many levels of depth in real-time
• Add context to AI systems and network structures to data science
5
Native Graph Technology
• Performance
• ACID Transactions
• Schema-free Agility
• Graph Algorithms
Designed, built and tested natively
for graphs from the start for:
• Developer Productivity
• Hardware Efficiency
• Enterprise Scale
• Graph Adoption
Analytics
Tooling
Graph Transactions
Data Integration
Dev.
& Admin
Drivers & APIs Discovery & Visualization
Graph Analytics
6. 6
• Record “Cyber Monday” sales
• About 35M daily transactions
• Each transaction is 3-22 hops
• Queries executed in 4ms or less
• Replaced IBM Websphere commerce
• 300M pricing operations per day
• 10x transaction throughput on half
the hardware compared to Oracle, which
Neo4j replaced
• Large postal service with over 500k
employees
• Neo4j routes 7M+ packages daily at peak,
with peaks of 5,000+ routing operations
per second.
Handling Large Graph Work Loads for Enterprises
Real-time promotion
recommendations
Marriott’s Real-time
Pricing Engine
Handling Package
Routing in Real-Time
7. 7
• The media conglomerate Meredith uses
Neo4j to turn data about its largely
anonymous website visitors into customer
profiles by graphing the data into billions of
nodes and then applying machine learning to
it.
• Almost 70% of Credit Card fraud was missed
• +1B Nodes and +1B Relationships to analyse
• Graph analytics with queries & algorithms
help find $10’s of millions of fraud in 1st year
Improving Analytics, ML & AI Across Industries
Meredith Marketing
to the Anonymous
Financial Fraud
Detection & Recovery Top 10 Bank
• Early intervention project with 3 years of
visits, tests & diagnosis with 10’s of Billions
of records
• Finding similarities in patient journeys
• Graph algorithms for identifying
communities & best intervention points
AstraZeneca
Patient Journeys
10. CAR
DRIVES
name: “Dan”
born: May 29, 1970
twitter: “@dan”
name: “Ann”
born: Dec 5, 1975
since:
Jan 10, 2011
brand: “Volvo”
model: “V70”
Latitude: 37.5629900°
Longitude: -122.3255300°
Nodes
• Can have Labels to classify nodes
• Labels have native indexes
Relationships
• Relate nodes by type and direction
Properties
• Attributes of Nodes & Relationships
• Stored as Name/Value pairs
• Can have indexes and composite indexes
• Visibility security by user/role
Neo4j Invented the Labeled Property Graph Model
MARRIED TO
LIVES WITH
OW
NS
PERSON PERSON
10
11. Cypher: Powerful & Expressive Query Language
MATCH (:Person { name:“Dan”} ) -[:MARRIED_TO]-> (spouse)
MARRIED_TO
Dan Ann
NODE RELATIONSHIP TYPE
LABEL PROPERTY VARIABLE
13. Relational Versus Graph Models
Relational Model Graph Model
KNOWS
KNOWS
KNOWS
ANDREAS
TOBIAS
MICA
DELIA
Person FriendPerson-Friend
ANDREAS
DELIA
TOBIAS
MICA
14. Analytics
Tooling
Graph Transactions
Data Integration
Dev.
& Admin
Drivers & APIs Discovery & Visualization
Graph Analytics
Developers
Admins
Applications Business Users
Data Analysts
Data Scientists
Enterprise Data Hub
Native Graph Technology for Applications & Analytics
16. Robust Graph Algorithms
• Run on the loaded graph to compute metrics about the topology
and connectivity
• Highly parallelized and scale to 10’s of billions of nodes
16
The Neo4j GDS Library
Mutable In-Memory
Workspace
Computational Graph
Native Graph Store
Efficient & Flexible Analytics
Workspace
• Automatically reshapes transactional graphs
into an in-memory analytics graph
• Optimized for analytics with global traversals
and aggregation
• Create workflows and layer algorithms
17. +50 Algorithms in the Neo4j GDS Library
• Shortest Path
• Single-Source Shortest Path
• All Pairs Shortest Path
• A* Shortest Path
• Yen’s K Shortest Path
• Minimum Weight Spanning Tree
• K-Spanning Tree (MST)
• Random Walk
• Degree Centrality
• Closeness Centrality
• CC Variations: Harmonic, Dangalchev,
Wasserman & Faust
• Betweenness Centrality & Approximate
• PageRank
• Personalized PageRank
• ArticleRank
• Eigenvector Centrality
• Triangle Count
• Clustering Coefficients
• Connected Components (Union Find)
• Strongly Connected Components
• Label Propagation
• Louvain Modularity
• K-1 Coloring
• Euclidean Distance
• Cosine Similarity
• Node Similarity (Jaccard)
• Overlap Similarity
• Pearson Similarity
• Approximate KNN
Pathfinding
& Search
Centrality /
Importance
Community
Detection
Similarity
Link
Prediction
• Adamic Adar
• Common Neighbors
• Preferential Attachment
• Resource Allocations
• Same Community
• Total Neighbors
...and also Auxiliary Functions:
• Random graph generation
• Encoding
• Distributions & metrics
17
20. Neo4j Bloom’s
Intuitive User Interface
20
Search with type-ahead
suggestions
Flexible Color, Size and Icon
schemes
Visualize, Explore and Discover
Pan, Zoom and Select
Property Browser and editor
22. Neo4j Cloud offerings to suit every need
22
Database-as-a-service Self-hosted Cloud Managed Services (CMS)
Cloud-native service
Zero administration Pay-as-
you-go
Self-service deployment
Cloud-native stack
No access to underlying infra
and systems.
Self hosted and managed
Any cloud (AWS, GCP, Azure)
Bring-your-own-license
Self-manage software, infra
in own private cloud
Own data, tenant, security
>50% deploy this way
White-glove fully managed
service by Neo4j experts
Fully customizable deployment
model and service levels
Operate In own data centers
or Virtual Private Cloud
23. Neo4j Aura: Built for the best developer experience
Neo4j’s open source roots backed by the strongest graph community helps deliver the best developer experience to rapidly build
rich graph-powered applications
23
Easy
Start in minutes
Automatic upgrades, patches
Scale on-demand instantly
Zero downtime
Powerful
Lightning-fast queries with
Native graph engine
Flexible “whiteboard”
data model
Cypher - expressive, efficient
and easy!
Broad language driver support
Reliable
End-to-end encrypted
Always ON
Globally available on world-class
infrastructure
Self-healing, durable
ACID compliant
Affordable
Pay-as-you-go
Capacity based pricing
Billing by the hour, starting
as low as 9¢/hr
Simple and predictable bills
24. Neo4j Cloud Managed Services (CMS)
Enterprise-class, white-glove managed services for day-to-day operations,
service and support of your Neo4j environment
Dedicated team,
always on-call
Advanced monitoring and
preventative maintenance
Enterprise-grade security
and compliance
24x7x365 remote services
and support
Big Three clouds, private
cloud, or on-premises
Your data in your
infrastructure, fully
controlled versioning
25. The CMS Advantage
Focus on
Innovation
… while we manage
your day-to-day
infrastructure
operations
Achieve Faster
Time-to-value
… with experts
to manage your
environment from day
one. Minimize hiring, in-
house training, and ramp-
up.
Reduce your
Risk
… and meet your
security, compliance
and business continuity
needs with proven best
practices.
Accelerate your
Cloud Journey
… by enabling a fully
managed enterprise
cloud environment and
moving your production
Neo4j environment
within days.
27. Recommendations Dynamic Pricing IoT-applicationsFraud Detection
Real-Time Transaction Applications
Generate and
Protect Revenue
Customer
Engagement
Metadata and Advanced Analytics
Data Lake
Integration
Knowledge
Graphs for AI
Risk
Mitigation
Generate
Actionable Insights
Network
Management
Supply Chain
Efficiency
Identity and Access
Management
Internal Business Processes
Improve Efficiency
and Cut Costs
27
Graph Use Cases by Value Proposition
28. Dun & Bradstreet
Neo4j for Tracking Beneficial
Ownership
Background
● Regulations and requirements around beneficial
ownership
● Needed to let B2B clients book new business promptly
via accelerated due diligence investigations
Business Problem
● Investigations call for highly trained staff, and this activity is
hard to scale. A single query might tie up key people for 10-15
days, resulting in lost revenue
Solution and Benefits
● Use Neo4j to quickly query historic relationships between
business owners and companies
● Query responses take milliseconds versus days of skilled
manual research
29. Adobe Behance
Social Network of 10M
Graphic Artists
Background
● Social network of 10M graphic artists
● Peer-to-peer evaluation of art and works-in-progress
● Job sourcing site for creatives
● Massive, millions of updates (reads & writes) to Activity Feed
● 150 Mongos to 48 Cassandras to 3 Neo4j’s!
Business Problem
● Artists subscribe, appreciate and curate “galleries” of works of their own
and from other artists
● Activities Feed is how everyone receives updates
● 1st implementation was 150 MongoDB instances
● 2nd implementation shrunk to 48 Cassandras, but it was still too slow and
required heavy IT overhead
Solution and Benefits
● 3rd implementation shrunk to 3 Neo4j instances
● Saved over $500k in annual AWS fees
● Reduced data footprint from 50TB to 40GB
● Significantly easier to introduce new features like, “New projects in your
Network”
30. US Army / Calibre
Systems
Equipment Logistics
Background
● US IT consulting firm helped US Army streamline equipment
deployments and maintenance spending
● Saving lives by improving the operational readiness of Army
equipment like tanks, radios, transports, aircraft, weaponry, etc.
Business Problem
● Needed to modernize procurement, budget and logistics processes for
equipment & spare parts
● Millions of connections among a tank’s bill-of-materials, for example
● Improve “what if” cost calculations when planning missions and troop
deployments
● Mainframe systems required over 60 man-hrs to calculate changes…
planning took too long.
Solution and Benefits
● 118M nodes & 185M relationships
● Shed cost estimation times by 88%
● Improved parts delivery timing and accuracy
● DBA labor required dropped by 77%
● Equipment TCO more predictable
● Safer soldiers
31. Caterpillar
Heavy Equipment
Manufacturing
Background
● Fortune 100 heavy equipment manufacturer
● 27 Million warranty & service documents parsed
● Foundation for AI-based supply chain management
Business Problem
● Improve maintenance predictability
● Need a knowledge base for 27 million warranty documents and
maintenance orders
● Graphs gather context for AI to identify ‘prime examples’ of connections
among parts, suppliers, customers and their mechanics anticipate when
equipment will need servicing and by whom.
Solution and Benefits
● Text to knowledge graph
● Common ontology for complaints, symptoms & parts
● Anticipates when equipment will need servicing
● Improves customer and brand satisfaction
● Maximizes lifespan and value of equipment
32. Improving Patient Outcomes
Global pharmaceutical with
$22.1Billion revenue
Focus on oncology,
cardiovascular, renal,
metabolism, & respiratory
32
Neo4j GDS to Map & Predict Patient Journeys
• Kidney disease intervention project
• 3 yrs of visits, tests & diagnosis with 10’s of Bn of records
• Knowledge Graph, graph queries & algorithms
• Community detection to help find similarities over time
• Finding influence points where experienced physicians may be
able to guide and assist
• Looking forward to path based embeddings
Challenge: Better intervention for complex diseases
• Complex diseases develop over years with many, many doctor
visits, tests and evolving diagnosis
• How to identify early warnings, intervene faster & improve
outcomes?
• No two patients are the same, so how are similarities found?
33. Let’s Do Something Amazing
Together…
Try Neo4j today: https://neo4j.com/sandbox/
Free training and education: https://neo4j.com/graphacademy/
Contact us: https://neo4j.com/contact-us/