Neo4j - graph database for recommendations

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.

Ready to leverage the power of a graph database to bring your application to the next level, but all the data is still stuck in a legacy relational database? Fortunately, Neo4j offers several ways to quickly and efficiently import relational data into a suitable graph model. It's as simple as exporting the subset of the data you want to import and ingest it either with an initial loader in seconds or minutes or apply Cypher's power to put your relational data transactionally in the right places of your graph model. In this webinar, Michael will also demonstrate a simple tool that can load relational data directly into Neo4j, automatically transforming it into a graph representation of your normalized entity-relationship model.

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 document discusses new features of Neo4j Bloom, a graph visualization tool. It provides an overview of where Bloom fits in the Neo4j ecosystem and for what users it is intended. Major sections cover the key features added in recent Bloom releases, including scene saving and sharing, search phrases, scene actions, captions redesign, and upcoming GDS integration. Selected features like these are then explained in more detail. The document concludes by providing additional resources for learning more about and getting started with Bloom.

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.

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.

An introduction of the Neo4j Graph database. Introduces the NOSQL space, the Graph Database concept, and Neo4j with examples.

Graph Database Meetup in Seoul #1. What is Graph Database? 국내 유일 그래프 데이터베이스 연구 개발 전문 기업, <비트나인> 주최로 진행된 그래프 데이터베이스 밋업(Meetup) "그래프 데이터베이스 기본 개념 소개" 입니다. 그래프 데이터베이스의 기본 개념 및 특징, 활용 분야 등에 대해 간략하게 소개하였으며, 추후 진행되는 밋업에서 좀 더 자세한 실제 활용 사례 등을 소개드릴 예정입니다. 밋업 관련 정보는, https://www.meetup.com/ko-KR/graphdatabase/ 관련 문의는 hnkim@bitnine.net으로 부탁드립니다. https://bitnine.net/ 에서 그래프 데이터베이스 솔루션 AgensGraph를 직접 다운로드 하시어 사용해 보실 수 있습니다. :)

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 GraphTour 2019: Neo4j Graph Platform Overview, Kurt Freytag, Neo4j, Director of Product Management Cloud, Neo4j

This document outlines an agenda and logistics for a training on Neo4j fundamentals and Cypher. It introduces graph concepts like nodes, relationships, and properties. It discusses why graphs are useful and shows examples of real-world domains that can be modeled as graphs. The training will cover introductory Cypher concepts like creating and matching patterns, and modeling exercises like representing a social network or movie genres graph. Logistics are provided like the WiFi password and a suggestion to work together in pairs on exercises.

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.

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.

Presented at the Second openCypher Implementers Meeting in London, UK, May 2017 @ http://www.opencypher.org/blog/2017/06/13/ocim2-blog/

This document outlines an upcoming workshop on graph technology and data science using Neo4j. The workshop will cover knowledge graphs, graph algorithms, graph machine learning techniques, and use cases. It will include demonstrations of algorithms like node similarity and centrality measures on graphs. Attendees will learn how graph databases like Neo4j can power graph analytics and machine learning to gain insights from connected data.

1) Governments can use graph databases to make their countries more secure, provide better services, and make government functions more efficient by leveraging connections in data. 2) Graph databases allow for analysis of connected data and detection of complex patterns across different domains like money laundering, law enforcement investigations, fraud detection, and national security. 3) Examples of how graph databases can be used include modeling money laundering networks, synchronizing law enforcement data, detecting fraud rings, and extracting insights from security and intelligence data involving people, locations, and events from multiple sources.

The document provides an overview and introduction to Neo4j, a graph database. It discusses what graphs and Neo4j are, how to model data in a graph versus SQL, the Cypher query language to interact with Neo4j, and demonstrates Neo4j through the browser. It concludes by suggesting next steps to download Neo4j, choose a driver, join the community, and attend upcoming events.

Recommendation and personalization systems are an important part of many modern websites. Graphs provide a natural way to represent the behavioral data that is the core input to many recommendation algorithms. Thomas Pinckney and his colleagues at Hunch (recently acquired by eBay) built a large scale recommendation system, and then ported the technology to eBay. Thomas will be discussing how his team uses Cassandra to provide the high I/O storage of their fifty billion edge graphs and how they generate new recommendations in real time as users click around the site.

This tutorial will provide you with a basic understanding of graph database technology and the ability to quickly begin development of a graph database application. You will have the capability to recognize graph-based problems and present the benefits of using graph technology for problem resolution. The tutorial will give you an understanding of: • Graph theory - origins and concepts • Benefits of graph databases • Different types of graph databases • Typical graph database API • Programming basics • Use cases Bring your laptops for a hands-on opportunity to practice some sample codes. A basic understanding of Java programming is a recommended prerequisite to understand this course. This session is led by the InfiniteGraph technical team and the demonstration code will be drawn from InfiniteGraph examples, however the broader educational presentation is product-neutral and not a commercial presentation of their products. To participate in the hands-on portion of the graph tutorial users must have: • Java programming experience • Java Developer Kit (JDK) • Current InfiniteGraph installed on laptop. (To download visit www.objectivity.com/infinitegraph) • HelloGraph test – Upon installing IG, run HelloGraph to test the install. (HelloGraph can be found online at http://wiki.infinitegraph.com/2.1/w/index.php?title=Download_Sample_Code) Leon Guzenda was one of the founding members of Objectivity in 1988 and one of the original architects of Objectivity/DB. He currently works with Objectivity's major customers to help them effectively develop and deploy complex applications and systems that use the industry's highest-performing, most reliable DBMS technology, Objectivity/DB. He also liaises with technology partners and industry groups to help ensure that Objectivity/DB remains at the forefront of database and distributed computing technology. Leon has more than 35 years experience in the software industry. At Automation Technology Products, he managed the development of the ODBMS for the Cimplex solid modeling and numerical control system. Before that, he was Principal Project Director for International Computers Ltd. in the United Kingdom, delivering major projects for NATO and leading multinationals. He was also design and development manager for ICL's 2900 IDMS product. He spent the first 7 years of his career working in defense and government systems. Leon has a B.S. degree in Electronic Engineering from the University of Wales.

Graph databases are a type of NoSQL database that is optimized for storing and querying connected data and relationships. A graph database represents data in graphs consisting of nodes and edges, where the nodes represent entities and the edges represent relationships between the entities. Graph databases are well-suited for applications that involve complex relationships and connected data, such as social networks, knowledge graphs, and recommendation systems. They allow for flexible querying of relationships and connections via graph traversal operations.

The document discusses NoSQL databases and CouchDB. It provides an overview of NoSQL, the different types of NoSQL databases, and when each type would be used. It then focuses on CouchDB, explaining its features like document centric modeling, replication, and fail fast architecture. Examples are given of how to interact with CouchDB using its HTTP API and tools like Resty.

In this webinar, Barry Zane, our Vice President of Engineering, discusses the evolution of databases from Relational to Semantic Graph and the Anzo Graph Query Engine, the key element of scale in the Anzo Smart Data Lake. Based on elastic clustered, in-memory computing, the Anzo Graph Query Engine offers interactive ad hoc query and analytics on datasets with billions of triples. With this powerful layer over their data, end users can effect powerful analytic workflows in a self-service manner.

There is a lot of confusion about the place and purpose of the many recent non-relational database solutions ("NoSQL databases") compared to the relational database solutions that have been around for so many years. In this presentation I will first clarify what exactly these database solutions are, compare them, and discuss the best use cases for each. I'll discuss topics involving OLTP, scaling, data warehousing, polyglot persistence, and the CAP theorem. We will even touch on a new type of database solution called NewSQL. If you are building a new solution it is important to understand all your options so you take the right path to success.

Ian closely looks at design and implementation strategies you can employ when building a Neo4j-based graph database solution, including architectural choices, data modelling, and testing.g

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.

Graph databases are based on graph theory and use nodes and relationships to store data. They are a type of property graph database that can scale easily. Graph databases are well-suited for applications that rely on relationships between data, need to find patterns in behavioral data, or require linking disparate data sources. Specifically, they are effective for fraud detection by identifying patterns across entities, providing a 360-degree view of customers by linking their various profiles and activities, and making recommendations by leveraging connections in user data.

This document compares relational and non-relational databases. It discusses how in 2003 the main databases were relational, but by 2010 non-relational databases grew popular in the "NoSQL movement". However, the document argues that there are no truly new database designs and that relational and non-relational databases can be combined. It advises to choose a database based on the specific problem and features needed rather than general classifications. The document provides examples of which types of databases fit certain data and access needs.