This presentation introduces the graph model as obvious choice for rich and connected data. Graph Databases are a category of open-source NoSQL datastores which are specialized in storing, handling and querying graph structures efficiently.
Use cases represent the applicability of the graph model across many domains.
Neo4j as the most widely used graph database supports the property graph model, which is explained in detail.
To query a graph database a powerful and expressive but also friendly and easily understandable query language that is tailored for graph patterns is key. Neo4j's Cypher is such a query language developed from the ground up to support expressing challenging use-cases in a comprehensive way.
A series of examples rounds up the presentation to apply the lessons learned.
Neo4j is a powerful and expressive tool for storing, querying and manipulating data. However modeling data as graphs is quite different from modeling data under a relational database. In this talk, Michael Hunger will cover modeling business domains using graphs and show how they can be persisted and queried in Neo4j. We'll contrast this approach with the relational model, and discuss the impact on complexity, flexibility and performance.
Neo4j is a powerful and expressive tool for storing, querying and manipulating data. However modeling data as graphs is quite different from modeling data under a relational database. In this talk, Michael Hunger will cover modeling business domains using graphs and show how they can be persisted and queried in Neo4j. We'll contrast this approach with the relational model, and discuss the impact on complexity, flexibility and performance.
GPT and Graph Data Science to power your Knowledge GraphNeo4j
In this workshop at Data Innovation Summit 2023, we demonstrated how you could learn from the network structure of a Knowledge Graph and use OpenAI’s GPT engine to populate and enhance your Knowledge Graph.
Key takeaways:
1. How Knowledge Graphs grow organically
2. How to deploy Graph Algorithms to learn from the topology of a graph
3. Integrate a Knowledge Graph with OpenAI’s GPT
4. Use Graph Node embeddings to feed Machine Learning workflow
This developer-focused webinar will explain how to use the Cypher graph query language. Cypher, a query language designed specifically for graphs, allows for expressing complex graph patterns using simple ASCII art-like notation and offers a simple but expressive approach for working with graph data.
During this webinar you'll learn:
-Basic Cypher syntax
-How to construct graph patterns using Cypher
-Querying existing data
-Data import with Cypher
-Using aggregations such as statistical functions
-Extending the power of Cypher using procedures and functions
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.
This presentation covers several aspects of modeling data and domains with a graph database like Neo4j. The graph data model allows high fidelity modeling. Using the first class relationships of the graph model allow to use much higher forms of normalization than you would use in a relational database.
Video here: https://vimeo.com/67371996
Using an employee knowledge graph for employee engagement and career mobilityNeo4j
Learn what a knowledge graph is and how it plays a salient role in enterprises and how to apply knowledge graphs for various business use cases across the data spectrum – from management to analytics and machine learning.
Graph Databases can solve problems that your normal database struggle with. These can be hard problems, and not all of them were mentioned as graph problems in your CS classes. Using a Graph Database correctly can save the day, and make you look like a super star among your peers.
Find out what a Graph Database can do for you. You might not have a classic "graph problem," but a Graph Database might still be a good solution for your data. Learn what problems Graph Databases solve, how to use one, and most importantly when to use a Graph Database. All of this with concrete examples using the Neo4j Graph Database.
With this talk, we want to:
* Show you what kinds of data are at the sweet spot of Graph Databases.
* How to structure your database to reap the benefits of graphy data
* Teach you how to know when to use a Graph Database, and the fundaments of how to do so
* Show you real application examples on the Neo4j Graph Database
GPT and Graph Data Science to power your Knowledge GraphNeo4j
In this workshop at Data Innovation Summit 2023, we demonstrated how you could learn from the network structure of a Knowledge Graph and use OpenAI’s GPT engine to populate and enhance your Knowledge Graph.
Key takeaways:
1. How Knowledge Graphs grow organically
2. How to deploy Graph Algorithms to learn from the topology of a graph
3. Integrate a Knowledge Graph with OpenAI’s GPT
4. Use Graph Node embeddings to feed Machine Learning workflow
This developer-focused webinar will explain how to use the Cypher graph query language. Cypher, a query language designed specifically for graphs, allows for expressing complex graph patterns using simple ASCII art-like notation and offers a simple but expressive approach for working with graph data.
During this webinar you'll learn:
-Basic Cypher syntax
-How to construct graph patterns using Cypher
-Querying existing data
-Data import with Cypher
-Using aggregations such as statistical functions
-Extending the power of Cypher using procedures and functions
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.
This presentation covers several aspects of modeling data and domains with a graph database like Neo4j. The graph data model allows high fidelity modeling. Using the first class relationships of the graph model allow to use much higher forms of normalization than you would use in a relational database.
Video here: https://vimeo.com/67371996
Using an employee knowledge graph for employee engagement and career mobilityNeo4j
Learn what a knowledge graph is and how it plays a salient role in enterprises and how to apply knowledge graphs for various business use cases across the data spectrum – from management to analytics and machine learning.
Graph Databases can solve problems that your normal database struggle with. These can be hard problems, and not all of them were mentioned as graph problems in your CS classes. Using a Graph Database correctly can save the day, and make you look like a super star among your peers.
Find out what a Graph Database can do for you. You might not have a classic "graph problem," but a Graph Database might still be a good solution for your data. Learn what problems Graph Databases solve, how to use one, and most importantly when to use a Graph Database. All of this with concrete examples using the Neo4j Graph Database.
With this talk, we want to:
* Show you what kinds of data are at the sweet spot of Graph Databases.
* How to structure your database to reap the benefits of graphy data
* Teach you how to know when to use a Graph Database, and the fundaments of how to do so
* Show you real application examples on the Neo4j Graph Database
Why relationships are cool but "join" sucksLuca Garulli
Relational DBMS and Document Databases use the "JOIN" operation to connect records and documents. Is there a better way to connect things? This presentation illustrates how OrientDB manages relationships by using the same technique of Graph Databases for super fast traversal.
Webinar: Large Scale Graph Processing with IBM Power Systems & Neo4jNeo4j
We live in a profoundly connected world. From supply chains to payment networks to digital business and complex portfolios, our ability to understand and navigate not just data, but relationships inside the data, play an increasingly important role in all aspects of business. Highly connected value chains that generate massive volumes of connected data create an opportunity for graph analysis, which Gartner describes as "the single most single most effective competitive differentiator for organizations pursuing data-driven operations and decisions." This talk will introduce the power of graph databases and share how the latest IBM Power Systems offerings featuring the POWER8 processor and CAPI-attached Flash enable unique scaling, performance and price-performance advantages for Neo4j workloads.
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
Graph Database Management Systems provide an effective
and efficient solution to data storage in current scenarios
where data are more and more connected, graph models are
widely used, and systems need to scale to large data sets.
In this framework, the conversion of the persistent layer of
an application from a relational to a graph data store can
be convenient but it is usually an hard task for database
administrators. In this paper we propose a methodology
to convert a relational to a graph database by exploiting
the schema and the constraints of the source. The approach
supports the translation of conjunctive SQL queries over the
source into graph traversal operations over the target. We
provide experimental results that show the feasibility of our
solution and the efficiency of query answering over the target
database.
Big Graph Analytics on Neo4j with Apache SparkKenny Bastani
In this talk I will introduce you to a Docker container that provides you an easy way to do distributed graph processing using Apache Spark GraphX and a Neo4j graph database. You'll learn how to analyze big data graphs that are exported from Neo4j and consequently updated from the results of a Spark GraphX analysis. The types of analysis I will be talking about are PageRank, connected components, triangle counting, and community detection.
Database technologies have evolved to be able to store big data, but are largely inflexible. For complex graph data models stored in a relational database there may be tedious transformations and shuffling around of data to perform large scale analysis.
Fast and scalable analysis of big data has become a critical competitive advantage for companies. There are open source tools like Apache Hadoop and Apache Spark that are providing opportunities for companies to solve these big data problems in a scalable way. Platforms like these have become the foundation of the big data analysis movement.
Speakers
Learn how advances in High Performance Computing (HPC) have resulted in dramatic improvements in application processing performance across a wide range of disciplines that range from manufacturing, finance, geological, life and earth sciences and many more.This mainstreaming of HPC has driven solution providers towards innovative Technical Computing solutions that are faster, scalable, reliable, and secure. For more information on IBM Systems, visit http://ibm.co/RKEeMO.
Visit http://bit.ly/KWh5Dx to 'Follow' the official Twitter handle of IBM India Smarter Computing.
Graph Database is the new paradigm of Big Data.
New insights are discovered in the connected data.
Fabricating Big Data into connected data is the cutting edge technology.
Graph database is the driver for sustainable growth in the Era of Big Data.
Graph Data is already prevailing among the global leading companies.
Graph Database will pass the dawn of standards.
The most widely adopted method will be the Hybrid Database.
Each company needs to prepare for the wave of change.
AgenGraph will support your business with superior capabilities.
For more information, please visit www.bitnine.net
The Neo4j Graph database was lacking a declarative query language.
We wanted to add a humane query language which is easy to read and understand. It borrows on other languages like SQL and SPARQL but brings it it's own flavor. Cypher uses ASCII ART to describe graph patterns that you're looking for.
We used Scala's parser combinator library in combination with functional approaches and lazy evaluation to develop the Cypher query language.
The talk describes the internals of the Cypher implementation.
Deploying Massive Scale Graphs for Realtime InsightsNeo4j
Graph databases have been at the forefront of helping organizations manage and generate insights from data relationships, and applying those insights in real-time to drive competitive advantage. As organizations gain value in deploying graph databases, the data volumes managed are growing exponentially pushing the limits of large-scale in-memory graph processing. Neo4j and IBM Power Systems combined forces to deliver a market leading scalable graph database platform capable of affordably storing and processing graphs of extremely large size and offering real-time insights, using flash and FPGA accelerators. In this session we will cover the use cases driving the need for this extremely scalable platform and how this platform offers an easy to deploy model for extreme scale graph databases.
New Opportunities for Connected Data - Emil Eifrem @ GraphConnect Boston + Ch...Neo4j
Today’s complex data is not only big, but also semi-structured and densely connected. In this session we’ll look at how size, structure and connectedness have converged to transform the data landscape. We’ll then go on to look at some of the new opportunities for creating end-user value that have emerged in a world of connected data, illustrated with practical examples drawn from the telecommunications, social media and logistics sectors.
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.
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.
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.
Knowledge Graphs and Generative AI
Dr. Katie Roberts, Data Science Solutions Architect, Neo4j
It’s no secret that Large Language Models (LLMs) are popular right now, especially in the age of Generative AI. LLMs are powerful models that enable access to data and insights for any user, regardless of their technical background, however, they are not without challenges. Hallucinations, generic responses, bias, and a lack of traceability can give organizations pause when thinking about how to take advantage of this technology. Graphs are well suited to ground LLMs as they allow you to take advantage of relationships within your data that are often overlooked with traditional data storage and data science approaches. Combining Knowledge Graphs and LLMs enables contextual and semantic information retrieval from both structured and unstructured data sources. In this session, you’ll learn how graphs and graph data science can be incorporated into your analytics practice, and how a connected data platform can improve explainability, accuracy, and specificity of applications backed by foundation models.
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.
How Graph Data Science can turbocharge your Knowledge GraphNeo4j
Knowledge Graphs are becoming mission-critical across many industries. More recently, we are witnessing the application of Graph Data Science to Knowledge Graphs, offering powerful outcomes. But how do we define Knowledge Graphs in industry and how can they be useful for your project? In this talk, we will illustrate the various methods and models of Graph Data Science being applied to Knowledge Graphs and how they allow you to find implicit relationships in your graph which are impossible to detect in any other way. You will learn how graph algorithms from PageRank to Embeddings drive ever deeper insights in your data.
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.
Enhancing Performance with Globus and the Science DMZGlobus
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
Monitoring Java Application Security with JDK Tools and JFR Events
Intro to Graphs and Neo4j
1. (c) Neo Technology, Inc 2014
Graph Database
Introduction
Meetup
April 2014
Michael Hunger
michael@neotechnology.com
@mesirii
@neo4j
2. (c) Neo Technology, Inc 2014
Agenda
1. Why Graphs,Why Now?
2. What Is A Graph, Anyway?
3. Graphs In The Real World
4. The Graph Landscape
i) Popular Graph Models
ii) Graph Databases
iii)Graph Compute Engines
20. (c) Neo Technology, Inc 2014
Rela<onships
(con<nued)
Nodes
can
have
more
than
one
rela<onship
Self
rela<onships
are
allowed
Nodes
can
be
connected
by
more
than
one
rela<onship
22. (c) Neo Technology, Inc 2014
Four
Building
Blocks
๏ Nodes
• En<<es
๏ Rela<onships
• Connect
en<<es
and
structure
domain
๏ Proper<es
• AJributes
and
metadata
๏ Labels
• Group
nodes
by
role
23. (c) Neo Technology, Inc 2014
Whiteboard
Friendlyness
Easy to design and model
direct representation of the model
25. (c) Neo Technology, Inc 2014
Tom Hanks Hugo Weaving
Cloud Atlas
The Matrix
Lana
Wachowski
ACTED_IN
ACTED_IN
ACTED_IN
DIRECTED
DIRECTED
26. (c) Neo Technology, Inc 2014
name: Tom Hanks
born: 1956
title: Cloud Atlas
released: 2012
title: The Matrix
released: 1999
name: Lana Wachowski
born: 1965
ACTED_IN
roles: Zachry
ACTED_IN
roles: Bill Smoke
DIRECTED
DIRECTED
ACTED_IN
roles: Agent Smith
name: Hugo Weaving
born: 1960
Person
Movie
Movie
Person Director
ActorPerson Actor
29. (c) Neo Technology, Inc 2014
What is NOSQL?
It’s not “No to SQL”
It’s not “Never SQL”
It’s “Not Only SQL”
NOSQL no-seek-wool n. Describes ongoing
trend where developers increasingly opt for
non-relational databases to help solve their
problems, in an effort to use the right tool for
the right job.
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31
Living in a NOSQL World
Density~=Complexity
Volume ~= Size
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31
Living in a NOSQL World
Density~=Complexity
Volume ~= Size
Key-Value
Store
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31
Living in a NOSQL World
Density~=Complexity
Column
Family
Volume ~= Size
Key-Value
Store
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31
Living in a NOSQL World
Density~=Complexity
Column
Family
Volume ~= Size
Key-Value
Store
Document
Databases
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31
Living in a NOSQL World
RDBMS
Density~=Complexity
Column
Family
Volume ~= Size
Key-Value
Store
Document
Databases
39. (c) Neo Technology, Inc 2014
31
Living in a NOSQL World
RDBMS
Density~=Complexity
Column
Family
Volume ~= Size
Key-Value
Store
Document
Databases
Graph
Databases
40. (c) Neo Technology, Inc 2014
31
Living in a NOSQL World
RDBMS
Density~=Complexity
Column
Family
Volume ~= Size
Key-Value
Store
Document
Databases
Graph
Databases
90%
of
use
cases
41. (c) Neo Technology, Inc 2014
31
Living in a NOSQL World
RDBMS
Density~=Complexity
Column
Family
Volume ~= Size
Key-Value
Store
Document
Databases
Graph
Databases
90%
of
use
cases
42. (c) Neo Technology, Inc 2014
31
Living in a NOSQL World
Aggregate Oriented
RDBMS
Density~=Complexity
Column
Family
Volume ~= Size
Key-Value
Store
Document
Databases
Graph
Databases
90%
of
use
cases
43. (c) Neo Technology, Inc 2014
“There is a significant downside - the whole approach works
really well when data access is aligned with the aggregates, but
what if you want to look at the data in a different way? Order
entry naturally stores orders as aggregates, but analyzing
product sales cuts across the aggregate structure. The
advantage of not using an aggregate structure in the database
is that it allows you to slice and dice your data different ways
for different audiences.
!
This is why aggregate-oriented stores talk so much about map-
reduce.”
Martin Fowler
Aggregate Oriented Model
44. (c) Neo Technology, Inc 2014
The connected data model is based on fine grained elements
that are richly connected, the emphasis is on extracting many
dimensions and attributes as elements.
Connections are cheap and can be used not only for the
domain-level relationships but also for additional structures
that allow efficient access for different use-cases. The fine
grained model requires a external scope for mutating
operations that ensures Atomicity, Consistency, Isolation and
Durability - ACID also known as Transactions.
!
Michael Hunger
Connected Data Model
65. (c) Neo Technology, Inc 2014
Looks different, fine.Who cares?
๏a sample social graph
•with ~1,000 persons
๏average 50 friends per person
35
66. (c) Neo Technology, Inc 2014
Looks different, fine.Who cares?
๏a sample social graph
•with ~1,000 persons
๏average 50 friends per person
๏pathExists(a,b) limited to depth 4
35
67. (c) Neo Technology, Inc 2014
Looks different, fine.Who cares?
๏a sample social graph
•with ~1,000 persons
๏average 50 friends per person
๏pathExists(a,b) limited to depth 4
๏caches warmed up to eliminate disk I/O
35
68. (c) Neo Technology, Inc 2014
Looks different, fine.Who cares?
๏a sample social graph
•with ~1,000 persons
๏average 50 friends per person
๏pathExists(a,b) limited to depth 4
๏caches warmed up to eliminate disk I/O
35
# persons query time
Relational database 1.000 2000ms
69. (c) Neo Technology, Inc 2014
Looks different, fine.Who cares?
๏a sample social graph
•with ~1,000 persons
๏average 50 friends per person
๏pathExists(a,b) limited to depth 4
๏caches warmed up to eliminate disk I/O
35
# persons query time
Relational database 1.000 2000ms
Neo4j 1.000 2ms
70. (c) Neo Technology, Inc 2014
Looks different, fine.Who cares?
๏a sample social graph
•with ~1,000 persons
๏average 50 friends per person
๏pathExists(a,b) limited to depth 4
๏caches warmed up to eliminate disk I/O
35
# persons query time
Relational database 1.000 2000ms
Neo4j 1.000 2ms
Neo4j 1.000.000 2ms
74. (c) Neo Technology, Inc 2014
Neo4j is a Graph Database
• A Graph Database:
• a schema-free labeled Property Graph
75. (c) Neo Technology, Inc 2014
Neo4j is a Graph Database
• A Graph Database:
• a schema-free labeled Property Graph
• perfect for complex, highly connected data
76. (c) Neo Technology, Inc 2014
Neo4j is a Graph Database
• A Graph Database:
• a schema-free labeled Property Graph
• perfect for complex, highly connected data
• A Graph Database:
77. (c) Neo Technology, Inc 2014
Neo4j is a Graph Database
• A Graph Database:
• a schema-free labeled Property Graph
• perfect for complex, highly connected data
• A Graph Database:
• reliable with real ACID Transactions
78. (c) Neo Technology, Inc 2014
Neo4j is a Graph Database
• A Graph Database:
• a schema-free labeled Property Graph
• perfect for complex, highly connected data
• A Graph Database:
• reliable with real ACID Transactions
• scalable: Billions of Nodes and Relationships, Scale out with
highly available Neo4j-Cluster
79. (c) Neo Technology, Inc 2014
Neo4j is a Graph Database
• A Graph Database:
• a schema-free labeled Property Graph
• perfect for complex, highly connected data
• A Graph Database:
• reliable with real ACID Transactions
• scalable: Billions of Nodes and Relationships, Scale out with
highly available Neo4j-Cluster
• fast with more than 2M traversals / second
80. (c) Neo Technology, Inc 2014
Neo4j is a Graph Database
• A Graph Database:
• a schema-free labeled Property Graph
• perfect for complex, highly connected data
• A Graph Database:
• reliable with real ACID Transactions
• scalable: Billions of Nodes and Relationships, Scale out with
highly available Neo4j-Cluster
• fast with more than 2M traversals / second
• Server with HTTP API, or Embeddable on the JVM
81. (c) Neo Technology, Inc 2014
Neo4j is a Graph Database
• A Graph Database:
• a schema-free labeled Property Graph
• perfect for complex, highly connected data
• A Graph Database:
• reliable with real ACID Transactions
• scalable: Billions of Nodes and Relationships, Scale out with
highly available Neo4j-Cluster
• fast with more than 2M traversals / second
• Server with HTTP API, or Embeddable on the JVM
• Declarative Query Language
82. (c) Neo Technology, Inc 2014
Graph Database: Pros & Cons
• Strengths
• Powerful data model, as general as RDBMS
• Whiteboard friendly, agile development
• Fast, for connected data
• Easy to query
• Weaknesses:
• Sharding (they can scale up and out reasonably well)
• Global Queries / Number Crunching
• Binary Data / Blobs
• Requires conceptual shift
• graph-like thinking becomes addictive
88. (c) Neo Technology, Inc 2014
Just use SQL
40users skillsuser_skills
select skills.name
from users join user_skills on (...) join skills on (...)
where users.name = “Michael“
92. (c) Neo Technology, Inc 2014
// find starting nodes
MATCH (me:Person {name:'Andreas'})
Andreas
You traverse the graph
42
93. (c) Neo Technology, Inc 2014
// find starting nodes
MATCH (me:Person {name:'Andreas'})
// then traverse the relationships
MATCH (me:Person {name:'Andreas'})-[:FRIEND]-(friend)
-[:FRIEND]-(friend2)
RETURN friend2
Andreas
You traverse the graph
42
94. (c) Neo Technology, Inc 2014
Cypher
a pattern-matching
query language for graphs
95. (c) Neo Technology, Inc 2014
Cypher attributes
#1 Declarative
You tell Cypher what you
want, not how to get it
44
100. (c) Neo Technology, Inc 2014
MATCH (n:Label)-[:REL]->(m:Label)
WHERE n.prop < 42
WITH n, count(m) as cnt,
collect(m.attr) as attrs
WHERE cnt > 12
RETURN n.prop,
extract(a2 in
filter(a1 in attrs
WHERE a1 =~ "...-.*")
| substr(a2,4,size(a2)-1)]
AS ids
ORDER BY length(ids) DESC
LIMIT 10
Query Structure
102. (c) Neo Technology, Inc 2014
MATCH (n:Label)-[:REL]->(m:Label)
WHERE n.prop < 42
WITH n, count(m) as cnt,
collect(m.attr) as attrs
WHERE cnt > 12
RETURN n.prop,
extract(a2 in
filter(a1 in attrs
WHERE a1 =~ "...-.*")
| substr(a2,4,size(a2)-1)]
AS ids
ORDER BY length(ids) DESC
SKIP 5 LIMIT 10
MATCH - Pattern
104. (c) Neo Technology, Inc 2014
MATCH (n:Label)-[:REL]->(m:Label)
WHERE n.prop < 42
WITH n, count(m) as cnt,
collect(m.attr) as attrs
WHERE cnt > 12
RETURN n.prop,
extract(a2 in
filter(a1 in attrs
WHERE a1 =~ "...-.*")
| substr(a2,4,size(a2)-1)]
AS ids
ORDER BY length(ids) DESC
SKIP 5 LIMIT 10
WHERE - filter
106. (c) Neo Technology, Inc 2014
MATCH (n:Label)-[:REL]->(m:Label)
WHERE n.prop < 42
WITH n, count(m) as cnt,
collect(m.attr) as attrs
WHERE cnt > 12
RETURN n.prop,
extract(a2 in
filter(a1 in attrs
WHERE a1 =~ "...-.*")
| substr(a2,4,size(a2)-1)]
AS ids
ORDER BY length(ids) DESC
SKIP 5 LIMIT 10
RETURN - project
108. (c) Neo Technology, Inc 2014
MATCH (n:Label)-[:REL]->(m:Label)
WHERE n.prop < 42
WITH n, count(m) as cnt,
collect(m.attr) as attrs
WHERE cnt > 12
RETURN n.prop,
extract(a2 in
filter(a1 in attrs
WHERE a1 =~ "...-.*")
| substr(a2,4,size(a2)-1)]
AS ids
ORDER BY length(ids) DESC
SKIP 5 LIMIT 10
ORDER BY LIMIT - Paginate
110. (c) Neo Technology, Inc 2014
MATCH (n:Label)-[:REL]->(m:Label)
WHERE n.prop < 42
WITH n, count(m) as cnt,
collect(m.attr) as attrs
WHERE cnt > 12
RETURN n.prop,
extract(a2 in
filter(a1 in attrs
WHERE a1 =~ "...-.*")
| substr(a2,4,size(a2)-1)]
AS ids
ORDER BY length(ids) DESC
SKIP 5 LIMIT 10
WITH + WHERE = HAVING
112. (c) Neo Technology, Inc 2014
MATCH (n:Label)-[:REL]->(m:Label)
WHERE n.prop < 42
WITH n, count(m) as cnt,
collect(m.attr) as attrs
WHERE cnt > 12
RETURN n.prop,
extract(a2 in
filter(a1 in attrs
WHERE a1 =~ "...-.*")
| substr(a2,4,size(a2)-1)]
AS ids
ORDER BY length(ids) DESC
LIMIT 10
Collections
113. (c) Neo Technology, Inc 2014
MATCH (:Country {name:"Sweden"})
<-[:REGISTERED_IN]-(c:Company)
<-[:WORKS_AT]-(p:Person:Developer)
WHERE p.age < 42
WITH c, count(p) as cnt,
collect(p.empId) as emp_ids
WHERE cnt > 12
RETURN c.name AS company_name,
extract(id2 in
filter(id1 in emp_ids
WHERE id1 =~ "...-.*")
| substr(id2,4,size(id2)-1)]
AS last_emp_id_digits
ORDER BY length(last_emp_id_digits) DESC
SKIP 5 LIMIT 10
Concrete Example
119. (c) Neo Technology, Inc 2014
MATCH (year:Year)
WHERE year.year % 4 = 0 OR
year.year % 100 <> 0 AND
year.year % 400 = 0
SET year:Leap
WITH year
MATCH (year)<-[:IN]-(feb:Month {month:2})
SET feb.days = 29
CREATE (feb)<-[:IN]-(:Day {day:29})
SET, REMOVE, DELETE
133. (c) Neo Technology, Inc 2014
Network Management - Statistics
// Most depended on component!
MATCH (n)<-[:DEPENDS_ON*]-(dependent)!
RETURN n, !
count(DISTINCT dependent) !
AS dependents!
ORDER BY dependents DESC!
LIMIT 1
Practical Cypher
n dependents
{name:"SAN"} 6
134. (c) Neo Technology, Inc 2014
๏ Full day Neo4j Training & Online Training
๏ Free e-Books
• Graph Databases, Neo4j 2.0 (DE)
๏ neo4j.org
• http://neo4j.org/develop/modeling
๏ docs.neo4j.org
• Data Modeling Examples
๏ http://console.neo4j.org
๏ http://gist.neo4j.org
๏ Get Neo4j
• http://neo4j.org/download
๏ Participate
• http://groups.google.com/group/neo4j
How to get started?
81