This document provides an overview of graph databases and Neo4j. It begins with an introduction to graph databases and their advantages over relational databases for modeling connected data. Examples of real-world use cases that are well-suited for graph databases are given. The document then describes the core components of the graph data model including nodes, relationships, properties, and labels. It provides examples of how to model data as a graph and query graphs using Cypher, the query language for Neo4j. The document concludes by discussing Neo4j as an example of a graph database and its key features and capabilities.
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 native graph database that allows organizations to leverage connections in data to create value in real-time. Unlike traditional databases, Neo4j connects data as it stores it, enabling lightning-fast retrieval of relationships. With over 200 customers including Walmart, UBS, and adidas, Neo4j is the number one database for connected data by providing a highly scalable and flexible platform to power use cases like recommendations, fraud detection, and supply chain management through relationship queries and analytics.
Introduction to Neo4j for the Emirates & BahrainNeo4j
This document provides an agenda and overview of a Neo4j presentation. It discusses Neo4j as the leading native graph database, its graph data science capabilities, and deployment options like Neo4j Aura and Cloud Managed Services. Success stories are highlighted like Minka using Neo4j Aura to power Colombia's new real-time ACH payments system. The presentation aims to demonstrate Neo4j's technology, use cases, and how it can drive business value through connecting data.
This document provides an overview of Neo4j, a graph database management system. It discusses how Neo4j stores data as nodes and relationships, allowing for fast querying of connected data. Traditional relational databases struggle with complex relationships, while NoSQL databases don't support relationships at all. Neo4j addresses these issues through its native graph storage and processing capabilities. The document highlights key Neo4j features like scalability, high performance, and its Cypher query language.
How Graph Algorithms Answer your Business Questions in Banking and BeyondNeo4j
This document provides an agenda and overview for a presentation on using graph algorithms in banking. The presentation introduces graphs and the Neo4j graph database, demonstrates sample banking data modeled as a graph, and reviews several graph algorithms that could be used for applications like fraud detection, including PageRank, weakly connected components, node similarity, and Louvain modularity. The document concludes with a demo and Q&A section.
Neo4j 4.1 introduces new features for security including role-based access control, schema-based security, and granular security for write operations. It also includes improvements to causal clustering, performance, and developer tools. This document reviews the history of releases from Neo4j 3.0 through 4.1 and highlights some of the main new capabilities in security, performance, and operations.
Neo4j GraphSummit London - The Path To Success With Graph Database and Data S...Neo4j
The document discusses Neo4j's graph data platform and graph data science capabilities. It provides an overview of Neo4j's tools for data scientists, machine learning workflows, algorithms, and ecosystem integrations. Examples are given of improved customer outcomes including increased fraud detection and better predictive models. The document also outlines new capabilities in algorithms, embeddings, machine learning pipelines, and GNN support.
Max De Marzi gave an introduction to graph databases using Neo4j as an example. He discussed trends in big, connected data and how NoSQL databases like key-value stores, column families, and document databases address these trends. However, graph databases are optimized for interconnected data by modeling it as nodes and relationships. Neo4j is a graph database that uses a property graph data model and allows querying and traversal through its Cypher query language and Gremlin scripting language. It is well-suited for domains involving highly connected data like social networks.
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
The document provides an outline for a presentation on graph-based data models. It introduces some key concepts about graphs and how they are used to model real-world interconnected data. It discusses how early adopters of graph technologies grew by focusing on data relationships. The document also covers graph data structures, graph databases, and graph query languages like Cypher and Gremlin.
This document discusses how graphs and graph databases can be used for data science and machine learning. It provides an overview of Neo4j's graph data science capabilities including graph algorithms, machine learning techniques, and real-world use cases.
The key points are:
1) Neo4j provides a graph data science library with over 70 graph algorithms and machine learning methods that can be used for tasks like link prediction, node classification, and graph feature engineering.
2) The library allows for both unsupervised and supervised machine learning on graph data in order to identify patterns, anomalies, and make predictions.
3) Real-world examples are presented where companies have used Neo4j's graph data
Join us for this 30-minute webinar to hear from Zach Blumenfeld, Neo4j’s Data Science Specialist, to learn the basics of Graph Neural Networks (GNNs) and how they can help you to improve predictions in your data.
Neo4j: The path to success with Graph Database and Graph Data ScienceNeo4j
This document provides an overview of the Neo4j graph data platform and its capabilities for data science and analytics. It discusses Neo4j's native graph architecture, tools for data scientists and analysts, and how Neo4j enables graph data science across the machine learning lifecycle from feature engineering to model deployment. Algorithms, embeddings, and machine learning pipelines in Neo4j are highlighted. Integration with common data ecosystems is also covered.
Application Modeling with Graph Databases - Relationships are coolLars Martin
This document discusses application modeling with graph databases. It begins with an introduction of the speaker and an agenda. It then covers the status quo of databases, issues with SQL joins in relational databases, and basics of graphs including vertices, edges, and real world examples. The bulk of the document discusses application modeling with graph databases using Spring Data and XO frameworks. It provides an example of modeling users and tweets with relationships and queries the graph. The document concludes that graph databases are well-suited for connected data and can provide more insights than relational databases for certain problems.
Shutl delivers with Neo4j by addressing issues with their previous MySQL database including exponential growth of joins, complex unmaintainable code, and slowing API response times. They chose Neo4j as a graph database because relationships are explicitly stored, domain modeling is simplified, and performance remains constant with growth. Queries in Neo4j use the Cypher language which focuses on pattern matching rather than implementation details.
Graph Databases, a little connected tour (Codemotion Rome)fcofdezc
This document provides an introduction to graph databases and Neo4j. It discusses how graph databases are better suited than relational databases for certain types of connected data. It uses social network and movie recommendation examples to demonstrate how to model and query data in a graph database using the Cypher query language.
Neo4j GraphTalks - Einführung in GraphdatenbankenNeo4j
The document announces a GraphTalks event in Cologne in February 2017 hosted by Neo Technology. The agenda includes an introduction to graph databases and Neo4j, a presentation on semantic data management, and an open networking session. Complex topics like the internet of things, domain modeling, and traditional vs graph approaches to data modeling will also be discussed.
The Five Graphs of Government: How Federal Agencies can Utilize Graph TechnologyNeo4j
In this session from Neo4j Government Graphday, Philip Rathle discusses how federal agencies and contractors can utilize graphs to power their applications.
GraphTalks Rome - Selecting the right TechnologyNeo4j
Dirk Möller discusses selecting the right database technology, with a focus on graph databases like Neo4j. He outlines the benefits of graph databases over relational and NoSQL databases for connected data, including high performance, easy maintenance, and seamless evolution. Möller also provides examples of common use cases where graph databases have business benefits in areas like recommendations, fraud detection, and network operations.
This document introduces Neo4j, a graph database developed by Neo Technology. It discusses how graph databases can model and query data relationships more easily than relational or NoSQL databases. The document provides an overview of Neo4j's history and growth, key features, examples of use cases, and how it helps customers like Adidas, Die Bayerische insurance, and SFR communications manage data relationships.
GraphTalks Rome - Identity and Access ManagementNeo4j
This document summarizes a presentation about using graph databases for identity and access management (IAM). It discusses how IAM systems traditionally assume rigid hierarchies that do not reflect modern complex organizations. Graph databases provide a flexible model for IAM by representing relationships between users, roles, devices, and other entities as nodes connected by relationships. This allows querying complex access scenarios and augmenting existing IAM systems. The presentation provides examples of building full IAM systems or augmenting existing ones using a graph database to better model complex real-world relationships.
This document summarizes Cerved Group's use of Neo4j and graph databases. Cerved processes large amounts of data on companies and individuals to provide credit risk management, marketing, and other services. Neo4j allows Cerved to more efficiently analyze relationships between entities, such as beneficial owners of companies. Cerved's Graph4You platform makes some of this graph data accessible to customers and data scientists to explore use cases. Cerved sees graph databases and extracting additional insights from relationships in data as important to its future.
Knowledge Architecture: Graphing Your KnowledgeNeo4j
This document discusses using knowledge architecture to transform data into actionable knowledge. It outlines challenges such as wasted research spending and inadequate information for decisions. The summary defines knowledge architecture as designing intellectual infrastructure combining knowledge management, informatics, and data science. It then shows how the presenter applied these fields by graphing a NASA lesson learned database to find patterns, topics, and correlations to enable more informed decisions.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A native graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
This webinar explains why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.
Whether it's directly improving patient care or helping lower costs to provide more access to healthcare, organizations are continuing to use IT to move the needle for an industry that is at a pivotal point in innovation.
Learn how our innovative storage solutions can help your organization meet its healthcare Big Data challenges: http://www.netapp.com/us/solutions/industry/healthcare/
What we carry with us in our everyday lives and interactions is just as important for our success as our technical skills and achievements.
This is what I carry with me. What do YOU carry?
Slides designed and produced with Haiku Deck for iPad. Set your story free with Haiku Deck at http://www.haikudeck.com/
You can learn more about Jonathon Colman at http://www.jonathoncolman.org/
This document describes the design and implementation of a visualization tool to provide more information about the impact of academic publications than citation counts alone. It represents references, citations, and self-citations over time and allows viewing individual papers or collections in different contexts. The tool uses shape grammars to classify papers by impact level into glyphs for compact overviews. It has been implemented in D3 and deployed on the INSPIRE platform for high energy physics publications. Future work may include a Chrome extension and exploring better impact metrics.
GraphDay Stockholm - Levaraging Graph-Technology to fight Financial FraudNeo4j
1) Graph technology can be leveraged to fight financial fraud by detecting fraud rings and relationships between entities like individuals and locations that may indicate fraudulent activity.
2) A graph database like Neo4j is well suited for fraud detection because it can easily model and traverse complex relationships in connected data, identify patterns more quickly than SQL, and enable near real-time response.
3) A fraud detection demo using Neo4j showed how operational data from various sources could be integrated and analyzed to generate alerts when potential fraud cases are detected.
This document provides an agenda for the Neo4j GraphDay Stockholm event on February 21, 2017. The agenda includes speakers from Neo4j and partner companies, and sessions on use cases, hands-on demos, partner presentations, training, and Q&A clinics. The event will take place in Stockholm and cover topics like graphs in action, recommendations in retail, and the manufacturing value chain.
GraphDay Stockholm - iKnow Solutions - The Value Add of Graphs to Analytics a...Neo4j
This document discusses how graph databases like Neo4j can help with analytics and information management. It provides examples of how graph queries in Neo4j are simpler than equivalent SQL queries for finding connections between nodes. Graph databases allow for impact analysis and easily reflecting new relationships. They also help with recommendations by incorporating events from the current user session.
In this webinar we'll explore a data set using Neo4j and Cypher and compare the approach we might take with a relational database and SQL. We'll cover the following topics: Modeling the data set Importing the data Querying the data Evolving the model and queries as the data changes.
New Opportunities for Connected Data - Emil Eifrem @ GraphConnect Boston + Ch...Neo4j
The document discusses graph databases and Neo4j. It provides examples of industries using graph databases and discusses Neo4j's performance advantages over MySQL for graph-oriented queries on social network data. Upcoming versions of Neo4j aim to improve ease of use and support larger datasets. The remainder of the document advertises an upcoming Neo4j user conference.
Neo4j Generative AI workshop at GraphSummit London 14 Nov 2023.pdfNeo4j
The document outlines an agenda for a generative AI workshop covering topics such as building a knowledge graph from a GDB dataset, semantic search using vector indexes and graph patterns, generating node embeddings, and developing a marketing campaign application that integrates all the techniques discussed.
La bi, l'informatique décisionnelle et les graphesCédric Fauvet
The document discusses how graph databases and graph technologies can be used for business intelligence, analytics, and decision making. It provides examples of how companies in various industries like communications, logistics, online recruiting, and consumer web have used graph databases from Neo4j to power applications, gain insights, and improve user experiences. Specific use cases discussed include network management, parcel routing, social job search, recommendations, and interactive television programming. The benefits of the graph model over relational databases for complex connected data are also highlighted.
1. The document discusses Neo4j, the world's most popular graph database. It highlights Neo4j's customers in top retail, financial, and software firms and its presence in Silicon Valley and global offices.
2. Neo4j is used both on-premises and in the cloud as a database-as-a-service. The document also discusses Neo4j's graph data science capabilities and its rise in popularity from 2010 to 2020.
3. Going forward, Neo4j is focusing on cloud services and positioning developers at the center of its strategy and products like Neo4j Aura and the Graph Data Science Library.
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 Innovation Lab – Bringing the Best of Data Science and Design Thinking ...Neo4j
The document discusses the importance of understanding data structures when designing products. It notes that product designers and data scientists both aim to reduce friction. Their work intersects as user experience depends on the underlying data architecture. Different data structures like relational databases, graphs, and knowledge graphs are suited to different problems. Case studies show how graphs power applications like image recognition and last-mile delivery by connecting product, inventory, logistics and other data. The document proposes a data thinking prototyping framework to map business problems, data models, value opportunities and applications when considering new solutions.
Connecting the Dots—How a Graph Database Enables DiscoveryInside Analysis
Leon Guzenda from Objectivity presented on graph databases and how they can enable discovery of additional value from existing enterprise data or big data repositories. Graph databases are well-suited for modeling complex relationships and connections in data. Objectivity offers InfiniteGraph, a massively scalable graph database built on its Objectivity/DB platform. InfiniteGraph allows organizations to store, manage and query relationships in their data to find patterns and insights not accessible through traditional analytics. It provides a high performance connection platform for linking disparate data sources and enabling fast, parallel graph queries and analytics.
The document discusses prototyping location apps with real data. It describes generating realistic datasets of people moving around cities by gathering check-in data from Foursquare tweets and visualizing the check-ins on maps. It also discusses generating social networks by extracting people and connection data from Wikipedia and DBpedia, including types of entities and links between pages. Code examples are provided to load and filter this data using Pig scripts on Amazon EMR.
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.
raph Databases with Neo4j – Emil Eifrembuildacloud
This document provides an overview of the graph database Neo4j. It discusses that Neo4j is a graph database with nodes, relationships, and properties that is well-suited for complex, highly connected data. Examples are given demonstrating how Neo4j can be used for network management in telecommunications companies and content management, access control, and collaboration at Adobe. Cypher, the query language for Neo4j, is also introduced.
Get Started with the Most Advanced Edition Yet of Neo4j Graph Data ScienceNeo4j
The document discusses Neo4j's graph data science capabilities. It highlights that Neo4j provides tools for graph algorithms, machine learning pipelines for tasks like node classification and link prediction, and a graph catalog for managing graph projections from the underlying database. The document also notes that Neo4j's capabilities allow users to leverage relationships in connected data to answer business questions.
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.
The Connected Data Imperative: An Introduction to Neo4jNeo4j
This document outlines an agenda for the Neo4j GraphTalk event in Atlanta on May 3rd 2017. The event will include an introduction to Neo4j and its capabilities for connected data, a presentation on real-world uses of Neo4j in production, and a reception. Neo4j is a native graph database created by Neo Technology to leverage connections in data in real-time to create value for organizations. It is well-suited for applications involving connected data, such as recommendations, fraud detection, and customer analytics.
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.
The document summarizes a presentation about graph databases and Neo4j. It discusses:
1) Why graph databases are useful for modeling connected data and enabling fast querying of relationships.
2) How the Neo4j graph database works with nodes, relationships, and properties to intuitively represent real-world networks.
3) A demonstration of starting a graph database project using Neo4j to model and query connected data.
The Art of the Possible with Graph - Sudhir Hasbe - GraphSummit London 14 Nov...Neo4j
The document discusses how massive trends like connected data, cloud innovation, and the rise of generative AI are transforming industries. It argues that to thrive in this new environment, organizations must turn data into insights and knowledge. Graph databases are presented as better for this task by preserving relationships that get lost with relational databases. The document promotes Neo4j's graph database platform and its capabilities for enabling insights, powering cloud applications, and combining with generative AI through knowledge graphs.
Neo4j GraphTalk Helsinki - Introduction and Graph Use CasesNeo4j
This document provides an introduction to graphs and Neo4j. It discusses that Neo4j is a native graph database that allows organizations to leverage connections in data in real-time to create value. It then provides information on Neo4j as a company and as a product, including that it is the world's leading graph database. The document goes on to define what graphs are from a data structure perspective and provides examples of famous graphs like social networks. It discusses why graph databases are useful compared to relational databases for representing complex, connected data and provides examples of use cases for Neo4j like recommendations, fraud detection, and network analysis.
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.
Webinar - IA generativa e grafi Neo4j: RAG time!Neo4j
Here are the key limitations of using vector databases for RAG:
1. Schema-less - Vector databases don't enforce a schema, making it difficult to represent structured knowledge like entities, relationships and properties.
2. Indexing challenges - It's hard to efficiently index and retrieve data based on semantic relationships rather than just keywords.
3. Explainability - Without an explicit graph structure, it's difficult to explain how a particular piece of retrieved data is relevant or related to the user's question.
4. Knowledge representation - Vector spaces are not well-suited for representing hierarchical, multi-relational knowledge like you would find in a knowledge graph.
A graph database overcomes these limitations by providing an
Looming Marvelous - Virtual Threads in Java Javaland.pdfjexp
Nowadays we have 2 options for concurrency in Java:
* simple, synchronous, blocking code with limited scalability that tracks well linearly at runtime, or.
* complex, asynchronous libraries with high scalability that are harder to handle.
Project Loom aims to bring together the best aspects of these two approaches and make them available to developers.
In the talk, I'll briefly cover the history and challenges of concurrency in Java before we dive into Loom's approaches and do some behind-the-scenes implementation. To manage so many threads reasonably needs some structure - for this there are proposals for "Structured Concurrency" which we will also look at. Some examples and comparisons to test Loom will round up the talk.
Project Loom is included in Java 19 and 20 as a preview feature, it can already be tested how well it works with our applications and libraries.
Spoiler: Pretty good.
Easing the daily grind with the awesome JDK command line toolsjexp
Included in the JDK installation are a lot of handy tools for Java developers, from java, jshell and jcmd to jfr and jdeprscan. These allow you to analyze a running JVM, generate JRE's, run Java source code and much more. In this talk I would like to present a number of these tools with practical examples and thus expand the toolbox of the participants. With the command line tools, many tasks can be automated and executed more efficiently, leaving more time for the exciting things in developer life.
Today, we have 2 options for concurrency in Java:
Simple, synchronous, blocking code with limited scalability that tracks well linearly at runtime, or
complex, asynchronous libraries with high scalability, which are harder to handle
Project Loom aims to bring together the best aspects of these two approaches and make them available to developers.
In the talk, I'll briefly discuss the history and challenges of concurrency in Java before we dive into Loom's approaches and look a bit behind the scenes.
Project Loom is included since Java 17 as a preview feature, it can already be tested to see how well it works with our applications and libraries. Spoiler: Pretty good.
GraphConnect 2022 - Top 10 Cypher Tuning Tips & Tricks.pptxjexp
I was there when Cypher was invented in 2012
and have been using it ever since. The language is
extremely powerful and easy to learn. But to truly
master it, you need to understand how it works
internally and how the database executes your
queries. In this session, you'll learn to look behind
the scenes at execution plans with PROFILE and
EXPLAIN and which specific clauses, expressions,
structures, and operations help you minimize
Cypher and database operations. After this talk,
you should be able to speed up your Cypher
statements quite a bit.
The newly released Neo4j Connector for Apache Spark can be used to read and write data between the two systems.
In this demo I show how to use the investigative Data from the FinCEN files to have a full pipeline up an running.
Notebook is in https://github.com/jexp/fincen
How Graphs Help Investigative Journalists to Connect the Dotsjexp
Investigative journalists use graphs and graph databases like Neo4j to connect disparate pieces of data and uncover hidden relationships. The Panama Papers investigation involved loading over 2.6 TB of leaked data into Neo4j to allow over 370 journalists from 80 countries to collaborate and find connections between entities, addresses, intermediaries and officers. Visualizing the data in Neo4j helped journalists tell the full story and have a global impact, exposing offshore dealings of world leaders and others.
Who doesn't know him, the office hero, who sat in the office late into the evening and repaired production? The fact that perhaps another colleague sat on the sofa at home and had an equal share in this success is unfortunately not so appreciated in most company cultures. But why is that? Because we are not used to working at home? Because we think that you are not so productive at home? Because you have family, garden or other activities at home? Michael has been working for distributed companies for a long time, but has also worked in offices for a long time. He will take you on his journey through different working environments and tell you what worked well for him.
This document provides a high-level summary of GraalVM and its capabilities for running applications and languages on the Java Virtual Machine. Specifically, it discusses how GraalVM allows running JavaScript, Python, Ruby, R, Java and C/C++ efficiently on the JVM through projects like Truffle and Substrate. It also summarizes GraalVM's polyglot capabilities for interoperability between languages and ahead-of-time compilation of Java into native binaries.
Neo4j Graph Streaming Services with Apache Kafkajexp
This document discusses Neo4j Streams, which enables real-time streaming of Neo4j database changes to Apache Kafka. It includes a change data capture plugin that streams transaction events from Neo4j to Kafka, a sink plugin that ingests data from Kafka into Neo4j based on custom rules, and procedures to consume and produce data directly from Cypher. The presenters demonstrate how Neo4j Streams can be used to build real-time data pipelines and streaming applications integrated with Neo4j. They encourage attendees to try the integration and provide feedback.
How Graph Databases efficiently store, manage and query connected data at s...jexp
Graph Databases try to make it easy for developers to leverage huge amounts of connected information for everything from routing to recommendations. Doing that poses a number of challenges on the implementation side. In this talk we want to look at the different storage, query and consistency approaches that are used behind the scenes. We’ll check out current and future solutions used in Neo4j and other graph databases for addressing global consistency, query and storage optimization, indexing and more and see which papers and research database developers take inspirations from.
APOC Pearls - Whirlwind Tour Through the Neo4j APOC Procedures Libraryjexp
APOC has become the de-facto standard utility library for Neo4j. In this talk, I will demonstrate some of the lesser known but very useful components of APOC that will save you a lot of work. You will also learn how to combine individual functions into powerful constructs to achieve impressive feats
This will be a fast-paced demo/live-coding talk.
Video: https://neo4j.com/graphconnect-2018/session/neo4j-utility-library-apoc-pearls
Unicorn images by TeeTurtle.com (Unstable Unicorns is a fun game & cool t-shirts)
This document discusses refactoring and summarizes the key points from Martin Fowler's book Refactoring. It covers what refactoring is, when it should be used by recognizing code smells, and how it should be done through small, incremental changes backed by thorough testing. The benefits of refactoring include improving code quality by reducing bugs and technical debt, while making the code easier to understand and modify. Tools now make refactoring easier by providing code analysis, refactoring suggestions, and quick fixes.
New Features in Neo4j 3.4 / 3.3 - Graph Algorithms, Spatial, Date-Time & Visu...jexp
Highlighting the progress in Neo4j 3.3 and 3.4 especially
Neo4j Desktop, Graph Algorithms, NLP, Date-Time, Geospatial, and performance.
Also featuring the new visualization tool Neo4j Bloom.
GraphQL - The new "Lingua Franca" for API-Developmentjexp
Three years ago, with the release of the GraphQL specification, Facebook took a fresh stab at the topic of "API design between remote services and applications." The key aspects of GraphQL provide a common, schema-based, domain-specific language and flexible, dynamic queries at interface boundaries.
In the talk, I'd like to compare GraphQL and REST and showcase benefits for developers and architects using a concrete example in application and API development, data source and system integration.
This document provides an overview of GraphDB and Neo4j. It discusses why graphs are useful for modeling connected data and common use cases. It also summarizes Neo4j's transactional graph database capabilities, performance advantages, and deployment options. Key topics covered include causal clustering, query planning, and driver and tooling support for developers.
We recently released the Neo4j graph algorithms library.
You can use these graph algorithms on your connected data to gain new insights more easily within Neo4j. You can use these graph analytics to improve results from your graph data, for example by focusing on particular communities or favoring popular entities.
We developed this library as part of our effort to make it easier to use Neo4j for a wider variety of applications. Many users expressed interest in running graph algorithms directly on Neo4j without having to employ a secondary system.
We also tuned these algorithms to be as efficient as possible in regards to resource utilization as well as streamlined for later management and debugging.
In this session we'll look at some of these graph algorithms and the types of problems that you can use them for in your applications.
Despite the “Graph” in the name, GraphQL is mostly used to query relational databases, object models or APIs. But it is really easy to support GraphQL endpoints from graph databases too. In this talk, I’ll demonstrate how we implemented a GraphQL extension for the Neo4j graph database. It uses the GraphQL schema definition map arbitrary GraphQL queries into single graph queries and runs them against the data in the Graph database. Using directives in the schema, we added some cool features that are transparent to the end user like computed fields and auto-generated mutations and query types. That allows you to create GraphQL APIs of some complexity without writing a single line of code.
I will show how to use the Neo4j-GraphQL extension, by creating an endpoint for the Game of Thrones dataset, and how we then can use our well-known tools (GraphiQL, apollo-client, graphql-cli, voyager) to interact with it.
Despite the “Graph” in the name, GraphQL is mostly used to query relational databases or object models. But it is really well suited to querying graph databases too. In this talk, I’ll demonstrate how I implemented a GraphQL endpoint for the Neo4j graph database and how you would use it in your app.
The world around us is full of connected information. Neo4j was originally developed to solve two complex "network" problems in a document management system, as it was too hard to manage rich connection information efficiently in traditional and new "NOSQL" databases.During this meetup, we will talk about the technology, and about the journey that a couple of technologists from Malmö took. You will learn* how Neo Technology grew from just the three founders in to a global database company with use-cases in every domain imaginable.* how focusing on customer and community feedback allows us to provide a solution for managing connected data to everyone, not just the large internet companies.
Of course we will also introduce the graph model, it's whiteboard friendlyness and how you get started with Neo4j and it's easy and powerful query language Cypher. We'll also compare the graph and relational data model to see how they differ in shape and capabilities. Finally we discuss the foundations that enable Graph databases to provide higher join performance, faster development processes and more inclusive software for all stakeholders. With use-cases from Gaming, Dating and Finance we'll see how to apply the graph capabilities to these domains to realize new functionality or opportunities that were not possible before.
Finally, if there's a question you've always wanted to ask/discuss, we'll have plenty of time for that at the end of Michael's presentation.
Each of the files or classes of a projects source code represents a tree (AST). Looking at dependencies to other classes besides inheritance creates a graph though. Field types and method parameters are also implicit dependencies. Storing this information in a graph database like Neo4j allows for interesting queries and insights. Class-Graph provides that and is available as open-source github project.
Fluttercon 2024: Showing that you care about security - OpenSSF Scorecards fo...Chris Swan
Have you noticed the OpenSSF Scorecard badges on the official Dart and Flutter repos? It's Google's way of showing that they care about security. Practices such as pinning dependencies, branch protection, required reviews, continuous integration tests etc. are measured to provide a score and accompanying badge.
You can do the same for your projects, and this presentation will show you how, with an emphasis on the unique challenges that come up when working with Dart and Flutter.
The session will provide a walkthrough of the steps involved in securing a first repository, and then what it takes to repeat that process across an organization with multiple repos. It will also look at the ongoing maintenance involved once scorecards have been implemented, and how aspects of that maintenance can be better automated to minimize toil.
Best Practices for Effectively Running dbt in Airflow.pdfTatiana Al-Chueyr
As a popular open-source library for analytics engineering, dbt is often used in combination with Airflow. Orchestrating and executing dbt models as DAGs ensures an additional layer of control over tasks, observability, and provides a reliable, scalable environment to run dbt models.
This webinar will cover a step-by-step guide to Cosmos, an open source package from Astronomer that helps you easily run your dbt Core projects as Airflow DAGs and Task Groups, all with just a few lines of code. We’ll walk through:
- Standard ways of running dbt (and when to utilize other methods)
- How Cosmos can be used to run and visualize your dbt projects in Airflow
- Common challenges and how to address them, including performance, dependency conflicts, and more
- How running dbt projects in Airflow helps with cost optimization
Webinar given on 9 July 2024
Blockchain technology is transforming industries and reshaping the way we conduct business, manage data, and secure transactions. Whether you're new to blockchain or looking to deepen your knowledge, our guidebook, "Blockchain for Dummies", is your ultimate resource.
UiPath Community Day Kraków: Devs4Devs ConferenceUiPathCommunity
We are honored to launch and host this event for our UiPath Polish Community, with the help of our partners - Proservartner!
We certainly hope we have managed to spike your interest in the subjects to be presented and the incredible networking opportunities at hand, too!
Check out our proposed agenda below 👇👇
08:30 ☕ Welcome coffee (30')
09:00 Opening note/ Intro to UiPath Community (10')
Cristina Vidu, Global Manager, Marketing Community @UiPath
Dawid Kot, Digital Transformation Lead @Proservartner
09:10 Cloud migration - Proservartner & DOVISTA case study (30')
Marcin Drozdowski, Automation CoE Manager @DOVISTA
Pawel Kamiński, RPA developer @DOVISTA
Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
09:40 From bottlenecks to breakthroughs: Citizen Development in action (25')
Pawel Poplawski, Director, Improvement and Automation @McCormick & Company
Michał Cieślak, Senior Manager, Automation Programs @McCormick & Company
10:05 Next-level bots: API integration in UiPath Studio (30')
Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
10:35 ☕ Coffee Break (15')
10:50 Document Understanding with my RPA Companion (45')
Ewa Gruszka, Enterprise Sales Specialist, AI & ML @UiPath
11:35 Power up your Robots: GenAI and GPT in REFramework (45')
Krzysztof Karaszewski, Global RPA Product Manager
12:20 🍕 Lunch Break (1hr)
13:20 From Concept to Quality: UiPath Test Suite for AI-powered Knowledge Bots (30')
Kamil Miśko, UiPath MVP, Senior RPA Developer @Zurich Insurance
13:50 Communications Mining - focus on AI capabilities (30')
Thomasz Wierzbicki, Business Analyst @Office Samurai
14:20 Polish MVP panel: Insights on MVP award achievements and career profiling
論文紹介:A Systematic Survey of Prompt Engineering on Vision-Language Foundation ...Toru Tamaki
Jindong Gu, Zhen Han, Shuo Chen, Ahmad Beirami, Bailan He, Gengyuan Zhang, Ruotong Liao, Yao Qin, Volker Tresp, Philip Torr "A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models" arXiv2023
https://arxiv.org/abs/2307.12980
An invited talk given by Mark Billinghurst on Research Directions for Cross Reality Interfaces. This was given on July 2nd 2024 as part of the 2024 Summer School on Cross Reality in Hagenberg, Austria (July 1st - 7th)
Quantum Communications Q&A with Gemini LLM. These are based on Shannon's Noisy channel Theorem and offers how the classical theory applies to the quantum world.
Choose our Linux Web Hosting for a seamless and successful online presencerajancomputerfbd
Our Linux Web Hosting plans offer unbeatable performance, security, and scalability, ensuring your website runs smoothly and efficiently.
Visit- https://onliveserver.com/linux-web-hosting/
Measuring the Impact of Network Latency at TwitterScyllaDB
Widya Salim and Victor Ma will outline the causal impact analysis, framework, and key learnings used to quantify the impact of reducing Twitter's network latency.
BT & Neo4j: Knowledge Graphs for Critical Enterprise Systems.pptx.pdfNeo4j
Presented at Gartner Data & Analytics, London Maty 2024. 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. Join this session to hear their story, the lessons they learned along the way and how their future innovation plans include the exploration of uses of EKG + Generative AI.
YOUR RELIABLE WEB DESIGN & DEVELOPMENT TEAM — FOR LASTING SUCCESS
WPRiders is a web development company specialized in WordPress and WooCommerce websites and plugins for customers around the world. The company is headquartered in Bucharest, Romania, but our team members are located all over the world. Our customers are primarily from the US and Western Europe, but we have clients from Australia, Canada and other areas as well.
Some facts about WPRiders and why we are one of the best firms around:
More than 700 five-star reviews! You can check them here.
1500 WordPress projects delivered.
We respond 80% faster than other firms! Data provided by Freshdesk.
We’ve been in business since 2015.
We are located in 7 countries and have 22 team members.
With so many projects delivered, our team knows what works and what doesn’t when it comes to WordPress and WooCommerce.
Our team members are:
- highly experienced developers (employees & contractors with 5 -10+ years of experience),
- great designers with an eye for UX/UI with 10+ years of experience
- project managers with development background who speak both tech and non-tech
- QA specialists
- Conversion Rate Optimisation - CRO experts
They are all working together to provide you with the best possible service. We are passionate about WordPress, and we love creating custom solutions that help our clients achieve their goals.
At WPRiders, we are committed to building long-term relationships with our clients. We believe in accountability, in doing the right thing, as well as in transparency and open communication. You can read more about WPRiders on the About us page.
Scaling Connections in PostgreSQL Postgres Bangalore(PGBLR) Meetup-2 - MydbopsMydbops
This presentation, delivered at the Postgres Bangalore (PGBLR) Meetup-2 on June 29th, 2024, dives deep into connection pooling for PostgreSQL databases. Aakash M, a PostgreSQL Tech Lead at Mydbops, explores the challenges of managing numerous connections and explains how connection pooling optimizes performance and resource utilization.
Key Takeaways:
* Understand why connection pooling is essential for high-traffic applications
* Explore various connection poolers available for PostgreSQL, including pgbouncer
* Learn the configuration options and functionalities of pgbouncer
* Discover best practices for monitoring and troubleshooting connection pooling setups
* Gain insights into real-world use cases and considerations for production environments
This presentation is ideal for:
* Database administrators (DBAs)
* Developers working with PostgreSQL
* DevOps engineers
* Anyone interested in optimizing PostgreSQL performance
Contact info@mydbops.com for PostgreSQL Managed, Consulting and Remote DBA Services
Transcript: Details of description part II: Describing images in practice - T...BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and slides: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
INDIAN AIR FORCE FIGHTER PLANES LIST.pdfjackson110191
These fighter aircraft have uses outside of traditional combat situations. They are essential in defending India's territorial integrity, averting dangers, and delivering aid to those in need during natural calamities. Additionally, the IAF improves its interoperability and fortifies international military alliances by working together and conducting joint exercises with other air forces.
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.
34. (c) Neo Technology, Inc 2014
31
Living in a NOSQL World
Density~=Complexity
Volume ~= Size
35. (c) Neo Technology, Inc 2014
31
Living in a NOSQL World
Density~=Complexity
Volume ~= Size
Key-Value
Store
36. (c) Neo Technology, Inc 2014
31
Living in a NOSQL World
Density~=Complexity
Column
Family
Volume ~= Size
Key-Value
Store
37. (c) Neo Technology, Inc 2014
31
Living in a NOSQL World
Density~=Complexity
Column
Family
Volume ~= Size
Key-Value
Store
Document
Databases
38. (c) Neo Technology, Inc 2014
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