Andrea Bielli, IT Architect Global Digital Solution, Enel
Davide Gimondo, Software Engineer, Enel
Enel mostra come neo4j aiuta nella gestione delle reti elettriche in 8 paesi nel mondo.
Con l’obiettivo di ottimizzare gli algoritmi di percorrenza della rete elettrica, in modo da rendere le reti sempre più efficienti e resilienti.
L’obiettivo di Enel è una gestione ottimale della topologia della rete per garantire gli obiettivi del gruppo: la transizione energetica e l’elettrificazione dei paesi in cui opera, verso l’obiettivo Net Zero, relativo alla riduzione delle emissioni nella produzione e distribuzione dell’energia elettrica.
Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Les graphes de connaissances et le Machine Learning sont les deux principales méthodes de représentation et d’exploitation des connaissances. Ce qui est intéressant et plutôt méconnu, c’est qu’ils sont hautement complémentaires. Cette présentation vous éclairera sur la manière dont ces deux domaines interagissent, mettant en avant la synergie entre eux et comment les variantes les plus récentes du ML (IA générative et LLMs) s’intègrent avec des graphes de connaissances pour construire des applications sémantiques modernes.
SERVIER Pegasus - Graphe de connaissances pour les phases primaires de recher...Neo4j
Jérémy Grignard, Data & Research Scientist, Servier
Les données que nous exploitons sont issues de domaines scientifiques variés comme les sciences omiques, structurales, cellulaires, chimiques ou phénotypiques, et correspondent à des concepts pharmaco-biologiques hétérogènes. Nous développons le graphe de connaissances Pegasus qui vise, en plus de capitaliser sur des données actuellement disponibles, à explorer l’environnement complexe des cibles thérapeutiques, à identifier des modalités de criblage pertinentes et à concevoir de nouvelles expériences.
Modern Data Challenges require Modern Graph TechnologyNeo4j
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
Building a modern data stack to maintain an efficient and safe electrical gridNeo4j
An overview of how our organization transitioned to being data-centric, through the implementation of a Enterprise data backbone, and dedicated tools using Neo4j technology, as the various challenges we faced during the process to make the dream come true.
Volvo Cars - Retrieving Safety Insights using Graphs (GraphSummit Stockholm 2...Neo4j
Volvo Cars has developed a map attributes representation as a graph in Neo4j. By including real time car data, they are able to collect insights to learn on possible accident causes based on road infrastructure.
Sopra Steria: Intelligent Network Analysis in a Telecommunications EnvironmentNeo4j
The Intelligent Network Analyzer (INA) uses the graph database by Neo4j to build a digital twin of the mobile telecommunications network. Based on this digital twin, INA can be used to efficiently perform various analyses to support network operators in their daily business. In our talk, we will show some features of INA and explain how they draw on the particular strengths of the Neo4j graph database.
Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Les graphes de connaissances et le Machine Learning sont les deux principales méthodes de représentation et d’exploitation des connaissances. Ce qui est intéressant et plutôt méconnu, c’est qu’ils sont hautement complémentaires. Cette présentation vous éclairera sur la manière dont ces deux domaines interagissent, mettant en avant la synergie entre eux et comment les variantes les plus récentes du ML (IA générative et LLMs) s’intègrent avec des graphes de connaissances pour construire des applications sémantiques modernes.
SERVIER Pegasus - Graphe de connaissances pour les phases primaires de recher...Neo4j
Jérémy Grignard, Data & Research Scientist, Servier
Les données que nous exploitons sont issues de domaines scientifiques variés comme les sciences omiques, structurales, cellulaires, chimiques ou phénotypiques, et correspondent à des concepts pharmaco-biologiques hétérogènes. Nous développons le graphe de connaissances Pegasus qui vise, en plus de capitaliser sur des données actuellement disponibles, à explorer l’environnement complexe des cibles thérapeutiques, à identifier des modalités de criblage pertinentes et à concevoir de nouvelles expériences.
Modern Data Challenges require Modern Graph TechnologyNeo4j
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
Building a modern data stack to maintain an efficient and safe electrical gridNeo4j
An overview of how our organization transitioned to being data-centric, through the implementation of a Enterprise data backbone, and dedicated tools using Neo4j technology, as the various challenges we faced during the process to make the dream come true.
Volvo Cars - Retrieving Safety Insights using Graphs (GraphSummit Stockholm 2...Neo4j
Volvo Cars has developed a map attributes representation as a graph in Neo4j. By including real time car data, they are able to collect insights to learn on possible accident causes based on road infrastructure.
Sopra Steria: Intelligent Network Analysis in a Telecommunications EnvironmentNeo4j
The Intelligent Network Analyzer (INA) uses the graph database by Neo4j to build a digital twin of the mobile telecommunications network. Based on this digital twin, INA can be used to efficiently perform various analyses to support network operators in their daily business. In our talk, we will show some features of INA and explain how they draw on the particular strengths of the Neo4j graph database.
Bertelsmann: BeTrend – Building a Trend Aggregation Machine.pdfNeo4j
Social media is the space where trends can be spotted most quickly. We will show a graph-based approach that aggregates data from different sources to better identify trends and to use them for the selection and enrichment of media content at Bertelsmann.
Neo4j GraphSummit London March 2023 Emil Eifrem Keynote.pptxNeo4j
Neo4j Founder and CEO Emil Eifrem shares his story on the origins of Neo4j and how graph technology has the potential to answer the world's most important data questions.
Technip Energies Italy: Planning is a graph matterNeo4j
Neo4j and Technip Energies Italy executed an Innovation Lab Sprint. The goal of the laboratory has been to frame, design and prototype the use case identified by their colleagues of Planning, Equipment and Construction disciplines, by applying Knowledge Graph technology, as the way to connect the data to gain information and insights as an immediate value, that is:
– capturing engineering deliverable milestone chain by gaining insights into a schedule
– performing reasoning on information, evidence and data
– extracting insights from data
SITA WorldTracer - the global Lost and Found solution built on Neo4j cuts costs and speeds delivery at airports worldwide by returning lost property to travelers.
Försäkringskassan: Neo4j as an Information Hub (GraphSummit Stockholm 2023)Neo4j
Having introduced Neo4j for specific applications over time, Försäkringskassan (Swedish Social Insurance Agency) is now leaning heavily on Neo4j as a central component in their data management platform. They are becoming data centric and increasingly centering information around the customer.
The three layers of a knowledge graph and what it means for authoring, storag...Neo4j
In this talk, Katariina Kari will discuss a framework for building a Knowledge Graph, by distinguishing between concepts, categories, and data. All three are interconnected to each other, however, they differ in their order of magnitude and the way they come about. A distinction makes sense to understand who is responsible for which part of the knowledge graph. Also, each layer should be governed differently. This framework ultimately helps to create a division of labour inside the company and helps stakeholders to understand the knowledge graph better.
Banking Circle: Money Laundering Beware: A Modern Approach to AML with Machin...Neo4j
by Ruben Menke, Lead Data Scientist at Banking Circle
In this talk, Banking Circle will show how a modern computational method is essential in the fight against money laundering.
Optimizing the Supply Chain with Knowledge Graphs, IoT and Digital Twins_Moor...Neo4j
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
Generali : SPIDER, notre produit au cœur des enjeux Generali en termes de Com...Neo4j
Sophie Sorba – Artificial Intelligence & Data Product Manager, Generali
Generali, premier assureur d’Europe, n’échappe pas aux risques de Fraude, de Blanchiment et de Financement du Terrorisme.
Pour répondre à ces enjeux de sécurisation, Generali France lance l’initiative SPIDER en 2019.
La raison d’être de SPIDER : fournir une vision 360° aux experts de la conformité afin d’investiguer des situations complexes en matière de criminalité financière.
Bertelsmann: BeTrend – Building a Trend Aggregation Machine.pdfNeo4j
Social media is the space where trends can be spotted most quickly. We will show a graph-based approach that aggregates data from different sources to better identify trends and to use them for the selection and enrichment of media content at Bertelsmann.
Neo4j GraphSummit London March 2023 Emil Eifrem Keynote.pptxNeo4j
Neo4j Founder and CEO Emil Eifrem shares his story on the origins of Neo4j and how graph technology has the potential to answer the world's most important data questions.
Technip Energies Italy: Planning is a graph matterNeo4j
Neo4j and Technip Energies Italy executed an Innovation Lab Sprint. The goal of the laboratory has been to frame, design and prototype the use case identified by their colleagues of Planning, Equipment and Construction disciplines, by applying Knowledge Graph technology, as the way to connect the data to gain information and insights as an immediate value, that is:
– capturing engineering deliverable milestone chain by gaining insights into a schedule
– performing reasoning on information, evidence and data
– extracting insights from data
SITA WorldTracer - the global Lost and Found solution built on Neo4j cuts costs and speeds delivery at airports worldwide by returning lost property to travelers.
Försäkringskassan: Neo4j as an Information Hub (GraphSummit Stockholm 2023)Neo4j
Having introduced Neo4j for specific applications over time, Försäkringskassan (Swedish Social Insurance Agency) is now leaning heavily on Neo4j as a central component in their data management platform. They are becoming data centric and increasingly centering information around the customer.
The three layers of a knowledge graph and what it means for authoring, storag...Neo4j
In this talk, Katariina Kari will discuss a framework for building a Knowledge Graph, by distinguishing between concepts, categories, and data. All three are interconnected to each other, however, they differ in their order of magnitude and the way they come about. A distinction makes sense to understand who is responsible for which part of the knowledge graph. Also, each layer should be governed differently. This framework ultimately helps to create a division of labour inside the company and helps stakeholders to understand the knowledge graph better.
Banking Circle: Money Laundering Beware: A Modern Approach to AML with Machin...Neo4j
by Ruben Menke, Lead Data Scientist at Banking Circle
In this talk, Banking Circle will show how a modern computational method is essential in the fight against money laundering.
Optimizing the Supply Chain with Knowledge Graphs, IoT and Digital Twins_Moor...Neo4j
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
Generali : SPIDER, notre produit au cœur des enjeux Generali en termes de Com...Neo4j
Sophie Sorba – Artificial Intelligence & Data Product Manager, Generali
Generali, premier assureur d’Europe, n’échappe pas aux risques de Fraude, de Blanchiment et de Financement du Terrorisme.
Pour répondre à ces enjeux de sécurisation, Generali France lance l’initiative SPIDER en 2019.
La raison d’être de SPIDER : fournir une vision 360° aux experts de la conformité afin d’investiguer des situations complexes en matière de criminalité financière.
Analyzing petabytes of smartmeter data using Cloud Bigtable, Cloud Dataflow, ...Edwin Poot
The Energy Industry is in transition due to the exponential growth of data being generated by the ever increasing number of connected devices which comprise the Smart Grid. Learn how Energyworx uses GCP to collect and ingest this IoT data with ease and is helping her customers uncover hidden value from this data, allowing them to create new business models and concepts.
Peek into Neo4j Product Strategy and Roadmap
Anurag Tandon, VP Product Management, Neo4j
Get a sneak peek into recent product enhancements and some exciting announcements. We will discuss the three main pillars of ongoing product strategy at Neo4j and briefly touch on important 2024 initiatives.
Gtechnology is Commercial Off-The-Shelf Software
GElectric is the Industry Ware (Model) built on top of Gtechnology : Metadata (Oracle) and windows DLLs
Microsoft .NET Framework
YugabyteDB - Distributed SQL Database on KubernetesDoKC
ABSTRACT OF THE TALK
Kubernetes has hit a home run for stateless workloads, but can it do the same for stateful services such as distributed databases? Before we can answer that question, we need to understand the challenges of running stateful workloads on, well anything. In this talk, we will first look at which stateful workloads, specifically databases, are ideal for running inside Kubernetes. Secondly, we will explore the various concerns around running databases in Kubernetes for production environments, such as: - The production-readiness of Kubernetes for stateful workloads in general - The pros and cons of the various deployment architectures - The failure characteristics of a distributed database inside containers In this session, we will demonstrate what Kubernetes brings to the table for stateful workloads and what database servers must provide to fit the Kubernetes model. This talk will also highlight some of the modern databases that take full advantage of Kubernetes and offer a peek into what’s possible if stateful services can meet Kubernetes halfway. We will go into the details of deployment choices, how the different cloud-vendor managed container offerings differ in what they offer, as well as compare performance and failure characteristics of a Kubernetes-based deployment with an equivalent VM-based deployment.
BIO
Amey is a VP of Data Engineering at Yugabyte with a deep passion for Data Analytics and Cloud-Native technologies. In his current role, he collaborates with Fortune 500 enterprises to architect their business applications with scalable microservices and geo-distributed, fault-tolerant data backend using YugabyteDB. Prior to joining Yugabyte, he spent 5 years at Pivotal as Platform Data Architect and has helped enterprise customers across multiple industry verticals to extend their analytical capabilities using Pivotal & OSS Big Data platforms. He is originally from Mumbai, India, and has a Master's degree in Computer Science from the University of Pennsylvania(UPenn), Philadelphia. Twitter: @ameybanarse LinkedIn: linkedin.com/in/ameybanarse/
Datacenter and cloud architectures continue to evolve to address the needs of large-scale multi-tenant data centers and clouds. These needs are centered around dimensions such as scalability in computing, storage, and bandwidth, scalability in network services, efficiency in resource utilization, agility in service creation, cost efficiency, service reliability, and security. Data centers are interconnected across the wide area network via routing and transport technologies to provide a pool of resources, known as the cloud. High-speed optical interfaces and dense wavelength-division multiplexing optical transport are used to provide for high-capacity transport intra- and inter-datacenter. This presentation will provide some brief descriptions on the working principles of Cloud & Data Center Networks.
The World of Internet
History of cloud computing
What is Cloud Computing?
Types of Cloud Computing
i. Software as a Service(SaaS)
ii. Platform as aService(PaaS)
iii. Infrastructure as a Service(IaaS)
Characteristics of Cloud Computing
Deployment model of Cloud Computing
Extending Twitter's Data Platform to Google CloudDataWorks Summit
Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.
Google Cloud Platform (GCP) is a suite of cloud computing services provided by Google. It offers a wide range of services including computing power, storage, networking, machine learning, and data analytics. GCP allows users to build, deploy, and scale applications and websites on Google's infrastructure. It provides flexibility, scalability, and reliability for businesses of all sizes, from startups to enterprises. Some of the key services offered by GCP include Compute Engine, App Engine, Kubernetes Engine, Cloud Storage, BigQuery, and TensorFlow for machine learning tasks. GCP also provides tools for managing and monitoring applications, as well as security features to protect data and applications in the cloud. Overall, Google Cloud Platform is a comprehensive solution for businesses looking to leverage cloud computing technology for their applications and services.
In today’s world the growing demand for knowledge has made cloud computing a center of attraction. Cloud computing is providing utility based services to all the users worldwide. It enables presentation of applications from consumers, scientific and business domains. However, data centers created for cloud computing applications consume huge amounts of energy, contributing to high operational costs and a large amount of carbon dioxide emission to the environment. With enhancement of data center, the power consumption is increasing at such a rate that it has become a key concern these days because it is ultimately leading to energy shortcomings and global climatic change. Therefore, we need green cloud computing solutions that can not only save energy, but also reduce operational costs.
Lift Your Legacy UNIX Applications & Databases into the Cloud Fadi Semaan
Unlock efficiency and innovation while reducing costs. In this presentation we will address:
1) Legacy pain overview
2) Dell application modernization services
3) UNIX to Linux migration
4) Case studies
Presented by Rich Cronheim
Executive Director , Dell Application Modernization
Services
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
See what's new in IBM mainframe technology through August 2018. This is the newest of the new software mainframe technology. This presentation is a teaser for additional topics presented at SHARE in St. Louis.
Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.
Re-Architect Your Legacy Environment To Enable An Agile, Future-Ready EnterpriseDell World
It’s time to re-architect your legacy environment in order to lay the foundation for an adaptive enterprise. In this session, you'll learn how to increase your business and technical agility using a fit-to-purpose .NET or Java architecture, while deploying your apps intelligently in the cloud and integrating with your complex IT environment, customers and partners.
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.
Workshop 1. Architecting Innovative Graph Applications
Join this hands-on workshop for beginners led by Neo4j experts guiding you to systematically uncover contextual intelligence. Using a real-life dataset we will build step-by-step a graph solution; from building the graph data model to running queries and data visualization. The approach will be applicable across multiple use cases and industries.
LARUS - Galileo.XAI e Gen-AI: la nuova prospettiva di LARUS per il futuro del...Neo4j
Roberto Sannino, Larus Business Automation
Nel panorama sempre più complesso dei progetti basati su grafi, LARUS ha consolidato una solida esperienza pluriennale, costruendo un rapporto di fiducia e collaborazione con Neo4j. Attraverso il LARUS Labs, ha sviluppato componenti e connettori che arricchiscono l’ecosistema Neo4j, contribuendo alla sua continua evoluzione. Tutto questo know-how è stato incanalato nell’innovativa soluzione Galileo.XAI di LARUS, un prodotto all’avanguardia che, integrato con la Generative AI, offre una nuova prospettiva nel mondo dell’Intelligenza Artificiale Spiegabile applicata ai grafi. In questo speech, si esplorerà il percorso di crescita di LARUS in questo settore, mettendo in luce le potenzialità della soluzione Galileo.XAI nel guidare l’innovazione e la trasformazione digitale.
GraphSummit Milan - Visione e roadmap del prodotto Neo4jNeo4j
van Zoratti, VP of Product Management, Neo4j
Scoprite le ultime innovazioni di Neo4j che consentono un’intelligenza guidata dalle relazioni su scala. Scoprite le più recenti integrazioni nel cloud e i miglioramenti del prodotto che rendono Neo4j una scelta essenziale per gli sviluppatori che realizzano applicazioni con dati interconnessi e IA generativa.
GraphSummit Milan & Stockholm - Neo4j: The Art of the Possible with GraphNeo4j
Dr Jesús Barrasa, Head of Solutions Architecture for EMEA, 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.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
10. INTERNAL
10
Decoupling
layer
Business services
Microservices
Solutions
Smart
Maintenance
Network
Operations
Customer
interactions
…
Network
analysis
Architectural logic structure
Asset
Inventory
… Network
topology
… Customer
& Trader
Data
Domains
Description
Solutions to provide new
business capabilities
Numerosity
̴ 40
Decoupling Layer enables full
Solution access to all Data
Domains
̴10.000
14
Domains are set of data entities
supporting a consistent set of a
business process
Publishing services on the platform
is the Domain Platformization
Implementing a unique global data
model is the Domain Convergence
ENEL Grids Digital Hub
GBS Program & Platformization
G
B
S
12. INTERNAL
12
Entity Access (typical SQL login).
Good performance in accesses based on
plant properties, in order to retrieve the
attributes of a node without
considering the relationships.
Need for a db that could easily represent the model of
the electrical network.The graphs represent this
infrastructure well.
Navigation of the relations of a graph (typical
graphDB access). High performance in accessing
network objects related to each other, in order to
extract entire power lines and all the elements that
compose them
ENEL Grids Topology: Neo4j graph DB
Project Needs
13. INTERNAL
13
3 candidates
• Neo4j
• Other graph db
• SQL DB
3 databases of
different sizes
• "Small" DB containing
the network of a country
• «Medium» DB containing
the network of 3
countries
• "Large" DB containing
the 3 country network
multiplied by 5
21 sample graphs
• Starting from the three
types of network (T1 / P2
/ S2), 21 sample graphs
representing parts of the
network were extracted,
chosen by number of
elements and depth
ENEL Grids Topology: Neo4j graph DB
Decision making process
14. INTERNAL
14
Features Neo4J Other graph DB SQL DB
Scalability Horizontal Vertical (no sharding) Vertical (no sharding)
Availability
- Master-Slave data replication
- Supports full / incremental
backups from the running cluster
- Monitoring and restoring
instances
- Supports up to 15 read replicas
ACID Compliance Yes [1]
Yes, except for some operations in
order to increase performance
Yes
Supported graph
models
Property graph [Cypher e Gremlin]
- Property graph [Gremlin]
- RDF [SPARQL]
None [2]
Data visualization
Neo4j Bloom, integrated and highly
customizable
Absent, but third-party solutions
that can be integrated
No graph data display support
Security
- User role management
(Enterprise ed.)
- Support external authentication
systems via LDAP (eg Kerberos)
- Access management to portions
of graphs
- Isolation in VPC
- Permissions managed through
AWS IAM
- Cryptable instances with AWS
KMS
- Automatic update management
Fine grained access rights
according to SQL-standard
Graph data
analysis
Neo4j Graph Analytics, library
composed of procedures that can be
called up by cypher (centrality,
clustering, pathfinding)
Not supported Not supported
Ability to edit Yes by modifying the vertex labels
Yes through workaround on the
creation of new vertexes with
updated labels and elimination of
the old ones
Yes
License Open Source/Commercial Commercial Open Source
neo4j Other graph
DB
SQL DB
* Features analyzed at the date of the POC(Q4 2020)
ENEL Grids Topology: Neo4j graph DB
Decision making process
15. INTERNAL
15
≈ 150 ms
• 2Mln queries / day
• 8 countries
8 DBs (1 per country)
• 1TB data
• 600M nodes
• 800M relations
For each environment
(Dev, UAT, Prod)
• 3 server Causal cluster
• 256GB RAM, 16 cpu
ENEL Grids Topology: Neo4j graph DB
Implementation process and results obtained
Oggi parleremo di come Enel ha adottato la tecnologia dei db a grafo con neo4j nella gestione delle reti elettriche in 8 paesi nel mondo.
Con l'obiettivo di ottimizzare gli algoritmi di percorrenza della rete elettrica, in modo da rendere le reti sempre più efficienti e resilienti.
per garantire gli obiettivi del gruppo: la transizione energetica e l'elettrificazione dei paesi in cui opera, verso l'obiettivo Net Zero, relativo alla riduzione delle emissioni nella produzione e distribuzione dell'energia elettrica.
Iniziamo introducendo un po’ cos’è Enel
non è solo L’Ente nazionale dell’energia elettrica. Ma è una multinazionale presente in oltre 20 paesi in tutti i continenti.
È il più grande operatore di distribuzione al mondo (esclusi operatori pubblici es Cina)
È il più grande operatore privato nelle rinnovabili
E l’operatore con il maggior numero di clienti al mondo
Quindi Enel porta la sua capacità di produrre, e distribuire energia in tutto il mondo ed è quindi leader e trainante nelle sfide della transizione energetica che stiamo vivendo
Enel è presente in tutto il mondo non solo come produttore e distributore di energia.
Con le sue società offre numerosi servizi.
Queste sono le società di Enel:
Global Power Generation (green power + thermal power)
Enel X way per i servizi legati alla elettrificazione e alle smart cities
Enel Grids gestisce le reti di distribuzione dalle centrali fino alla consegna
Come dicevamo prima, Enel ha un ruolo determinante nei processi di trasformazione, e transizione energetica che il mondo si sta predisponendo a fare.
Questo si ripercuote su obiettivi estremamente sfidanti per Enel, che ci portano a fare continuamente innovazione e grandi investimenti per riuscire in un obiettivo cosi importante.
Ognuna delle società con le sue peculiarità ha posto obiettivi enormi, semplicemente se confontiamo la crescita tra il 21 e il 24 e quella con il 30.
Ovviamente tutti questi obiettivi contribuiscono al più grande che è quello del Net Zero, ovvero la riduzione fino a zero delle emizssioni nella produzione di energia.
Una elettrificazione cosi forte passa da una componente estremamente importante, una rete di grande qualità che riduce gli sprechi e arriva ovunque. Che è l’obiettivo di Enel Grids
In particolare è all’interno della società Enel Grids, in cui io lavoro, che è stato scelto di utilizzare neo4j. Proprio per gestire la topologia e il grafo della rete elettrica.
Enel Grids gestisce e distribuisce energia su 2Mln di km di line elettriche in 8 paesi (alcuni di questi sono usciti da poco dalla strategia e sono in vendita)
In ognuno di questi paese è primo o secondo distributore, proprio perché l’obiettivo è migliorare drasticamente la qualità del servizio nei paesi in cui si trova, non si tratta semplicemente di entrare in un mercato.
E come dicevamo prima Enel Grids è in una posizione centrale e determinante in quel processo di elettrificazione e trasizione energetica.
Sostanzialmente perché è l’infrastruttura abilitante, che permette di distribuire energia al meglio, senza sprechi e con grande qualità l’energia.
Rendendo efficiente la produzione e raggiungendo i clienti ovunque
Questo si traduce in linee elettriche sempre più smart, telecontrollate e resilienti.
Il processo di transizione che Enel sta facendo si basa su questi pillar:
Decarbonizzazione (che è anche l’obiettivo principale del gruppo)
Digitalizzazione
Centralità dei clienti
Elettrificazione (che va di pari passo con la decarbonizzazione e la digitalizzazione)
Urbanizzazione (per rendere sempre più smart le grandi città e renderle più efficienti energeticamente)
Come vedete la digitalizzazione è uno dei pillar fondamentali ed è al centro. Anche se enel è una società di energia, utilities, perché mette al digitalizzazione al centro?
Enel è una delle pochissime aziende nel suo campo, ma anche in altri, ad aver messo al centro la digitalizzazione. Nel processo di trasformazione digitale ha avuto il coraggio di digitalizzare l’intera azienda.
Non ha fatto come molte che hanno creato degli spinoff totalmente digitali.
Questo denota la forte propensione dell’azienda all’innovazione dedicando Grandi investimenti nel piano strategico comunicato qualche anno fa.
In ambito Grids l’approccio all’innovazione è estremamente importante e lo si può vedere nei temi riportati in questa slide.
Grids sta intraprendendo un viaggio estremamente veloce e sempre in anticipo, andando a credere e sviluppare sulle tecnologie più moderne.
L’esempio più eclatante è che Enel ha deciso di migrare tutto in cloud tutto dal 2015, completando la migraazione nel 2019. Nel 2015 solo le startup erano in cloud e negli ambienti delle grande imprese, soprattutto italiane, se ne parlava solamente.
Full CloudWe are the world’s first large utility company to fully embrace the cloud model.From 2015, in april 2019 Enel became “full cloud”
Un secondo grande passo è stato il cambio totale di modello, sia in ambito tecnologico che di business. L’implementazione della platform, di cui parlo nella prossima slide, traforma totalemnte l’approccio allo sviluppo. E’ un totale passaggio verso applicazioni a microservizi. In questo ambito orami in enel si parla di sistemi legacy riferendosi a quelli in IaaS
Platform based modelThe Enel Digital Platform is a set of infrastructure, tools, manual and automated procedures that allow a faster, more reliable and more efficient management and development of digital solutions.
La platform è lo strumento che ci sta aiutando a definire un vero e proprio Digital twin della rete elettrica.
Stiamo testando e sviluppando nuove tecnologie come il Quantum Computing per migliorare la pianificazione delle attività in campo, il Quantum Edge Device per la digitalizzazione delle cabine secondarie, o il Network Digital Twin con i suoi modelli di analisi dei dati ottimizzare la pianificazione della rete, per rendere i lavori più efficienti o per la formazione sulla sicurezza.
La prossima milestone del percorso di digitalizzazione delle reti di Enel, in effetti, deriva dall'intersezione di diverse tecnologie ormai mature: il modello di piattaforma digitale, l'ecosistema dei social network e il sistema Network Digital Twin. Le opportunità che possono scaturire dalla combinazione di queste tre tecnologie si materializzano in quello che viene chiamato il Metaverso, un articolato ecosistema di tecnologie che possono essere combinate in modo originale per fornire un’esperienza di collaborazione e interazione immersiva e in tempo reale sia con altre persone che con oggetti in uno spazio virtuale che simula il contesto reale.
Per le reti il GridVerse sarà una delle tante istanze interoperabili del Metaverso che ospiterà casi d’uso relativi alla progettazione collaborativa, all’addestramento virtuale, all’assistenza remota e alla simulazione del comportamento delle reti.
IT Platformization represents a new way of developing IT Systems and revolves around a common denominator to enable new platform businesses and operating models.
Evolving towards an IT Platform Model means:
Moving away from the development of applications based on vertical systems to the development of solutions.
Being able to integrate with what is being done, and this requires introducing a decoupling layer separating data from solutions.
Sharing and democratizing data.
The adoption of an IT Platform model means:
More Speed, thanks to re-use and shared services.
Unleashed Data Potential, thanks to systems integration by design.
Enable New Business Models & Platform Operating Models, thanks to a new way of developing IT systems.
Enable Decoupling
Democratize Data
Support Productivity
Guarantee Sustainability
Build a Community
Plan For Growth
Enel Grids crede moltissimo in questo nuovo approccio allo sviluppo e sta migrando tutte le applicazioni verso le solution in platform.
L’investimento è enorme ma i risultati si vedono e qui vediamo come sta crescendo l’utilizzo della platform
Nell’ambito di Enel Grids, il dominio Topology, forse il più importante perché contiene i dati relativi alla topologia della rete elettrica, le connessioni tra i vari elementi e la configurazione della rete in tutte le country gestite.
3 anni fa, quando è stato deciso di fare questa migrazione alla platform, ci si è chiesti quale fosse il modo migliore di descrivere al topologia della rete. Fino a quel momento la rete e le relative relazioni tra i nodi erano definiti su un db sql Oracle.
Ora ci si prospettava la possibilità di scegliere un db che possa rappresentare al meglio una infrastruttura come la rete elettrica, che appunto è proprio un grafo (come vedete nell’immagine). Una serie di elementi collegati tra loro da connessioni elettriche e gerarchiche.
Inoltre c’era l’esigenza di rendere il più veloce, performante e efficiente la percorrenza della rete. Ovvero fare delle estrazioni da una cabina primaria fino ai clienti alimentati, o verificare la presenza di magliature o controalimentazioni. Questi campi di applicazione sono estremamamente importanti per garantire una rete efficiente, sicura e resiliente. Per fare calcoli di rete, curve di carico, analisi sulla qualità del servizio ecc.
La terza esigenza importante era che il nuovo db doveva garantire al tempo stesso ottime performance nel caso di accessi sql like. Ovvero non basarsi solo su etichette e relazioni, ma ricerche di entità sugli attributi e sugli indici.
Nel processo di scelta del db abbiamo fatto dei test di comparazione tra 3 differenti prodotti e tecnologie per vedere come si comportavano con le nostre esigenze.
Neo4j
Un altro graphdb (di un altro produttore)
E un sql db
Abbiamo identificato 3 set di dati di grandezza differente per eseguire i test con carichi differenti
Successivamente abbiamo definito 21 tipologie di estrazione di rete da eseguire sui 3 db e sui 3 differenti set di dati
I risultati dei test hanno evidenziato neo4j come la miglior scelta.
Come vedete ci sono stati casi di test in cui le alter tecnologie sono state più performanti, ma in generale neo4j si è rivelato più adattabile a tutti I casi e vincendo nella maggior parte dei confronti.
Inoltre anche le features offerte dai prodotti risultavano migliori per neo4j
Ad oggi neo4j è quindi il db ufficiale della topologia delle rete, e su cui si stanno migrando tutte le applicazioni (solution)
Il tasso di utilizzo è in costante aumento e ogni giorno riceve più query e il carico aumenta
Questa è la sfida su cui lo stiamo mettendo alla prova, stiamo cercando di renderlo sempre più efficente all’aumentare del carico che non riusciamo ancora a stimare ma sarà di diversi ordini di grandezza superiori a quello attuale
Ora vediamo dei casi d’uso in cui l’utilizzo di neo4j ha abilitato funzioni molto important per enel e su cui stiamo anche cercando di fare innovazione.
Nel primo caso vediamo il pannello cartografico su cui la rete viene progettata e pianificata.
Il secondo caso è un po’ un’anteprima, e rappresenta il GridVerse. Ovvero il metaverso della rete. Che permetterà ai nostri colleghi che lavorano sul campo, e che progettano la rete, di avere una versione, un digital twin della rete, il più realistico possibile. Permettendo un giorno di lavorare anche a distanza. Offrendo anche una realtà aumentata che facilita il lavoro e lo rende più sicuro.
I prossimi passi, come dicevo prima saranno quelli di rendere la nostra architettura sempre più resiliente e meglio gestita. Visto che il carico è in continuo aumento.
Per questo stiamo pensando di andare verso una soluzione ibrida, dal momento che aura db non è ancora adatto alle nostre esigenze in cui abbiamo molte apoc custom sviluppate per migliorare la percorrenza di rete. Ma andremo verso una soluzione in cui I servizi di neo4j, il CMS prenderà in carico la nostra infrastruttura e gestirà I nostril ambienti 24x7