At GraphDB Meetup Czech Republic in Prague, Christophe Willemsen talks about creating a chatbot with Amazon Alexa, Neo4j and GraphAware NLP
Michael Hunger introduces Neo4j to the audience of the Czech GraphDB Meetup in Prague, Czech Republic
In the previous years we have got the Polyglot Persistence. This is a fancy term which means that when storing data, it is best to use multiple data storage technologies, chosen based upon the way data is being used by. If we have multiple persistence, then sometimes we need polyglot operations. One of the most popular use case in Big Data is searching. Almost all websites provide a search function to their users, to be able to find what they are looking for. Usually it is an Apache Lucene based solution, like Elasticsearch or Solr. I will show you how to enrich this kind of searching with the power of graph based searches, and implement a polyglot search functionality, where the results are based on the cooperation of a search engine and a graph based real time recommendation.
With more than 9 million users and 21 million repositories, Github is the world's biggest code sharing platform. Its API offers a window to the public activity of about 600.000 events a day. In this talk you will discover how this amount of user activities transformed in a suitable graph can become a new source of knowledge.
A great part of the world's knowledge is stored using text in natural language, but using it in an effective way is still a major challenge. Natural Language Processing (NLP) techniques provide the basis for harnessing this huge amount of data and converting it into a useful source of knowledge for further processing. It uses computer science, artificial intelligence and formal linguistics concepts to analyze natural language, aiming at deriving meaningful and useful information from text.
Ever struggle with writes performance in Cypher? This Lightning talk is for you! In only 15 minutes, Christophe will show you some tips and tricks for making your Cypher write transactions as fast as possible.
Building Spring Data Neo4j 4.1 Applications Like A Superhero: The latest release of Spring Data Neo4j 4.1 offers advanced features to map your Java domain models to the Neo4j Graph Database. Powered by the Neo4j-OGM library, it offers you the convenience and familiarity of Spring-based programming. After this introductory tour of its features, followed by a demonstration of how easy it is to rapidly build an application, you'll have the confidence to start developing with Spring Data Neo4j 4.1 today.
Graph Databases are naturally well-suited for building recommendation engines. In this talk, Christophe will share his experience building a number of production-ready recommendation engines using Neo4j and introduce the open-source GraphAware Reco4PHP Library, which enables PHP developers to rapidly build their own recommendation systems.
A great part of the world’s knowledge is stored using text in natural language, but using it in an effective way is still a major challenge. Natural Language Processing (NLP) techniques provide the basis for harnessing this huge amount of data and converting it into a useful source of knowledge for further processing. By Alessandro Negro, Chief Scientist, GraphAware.
@Leisure Group case study
‘GraphAware successfully implemented a real-time recommendation engine on our site in just two weeks. That is an astonishingly short time to production.’
--David Stephenson, Managing Director, DSI Analytics and interim Head of Analytics, Belvilla
Read the Case StudyThe Dataportal is a data resource search engine which connects users with visualizations, tools, curated data, and metrics to do their job more effectively. It aids with data discovery, trust, and empowers Airbnb employees to be ""data informed"" in their decision making, and encourages a culture of self-service.
Podcast Interview with Michal Bachman, GraphAware by Rik Van Bruggen.
Bryce Merkl Sasaki from Neo4j chatted with Christophe Willemsen, our Senior Neo4j Consultant Christophe and Bryce spoke at GraphConnect San Francisco last October.
Michael Bachman of GraphAware discusses how to build a high-performance recommendation engine with Neo4j. He discusses business and technical challenges and shows their Java and Cypher code.
InfoJobs case study
‘GraphAware didn’t just help us build our recommendation service: they helped our developers acquire a whole new set of programming skills.’
--Marc Pou, Product Owner, InfoJobs
Read the Case StudyVince Bickers, Principal Consultant, GraphAware, contributor to Spring Data Neo4j:Learn about the latest version (v4) of Spring Data Neo4j (SDN). Neo4j is the world’s leading graph database, a scalable, open-source NoSQL solution for your data relationships. Spring Data offers convenient APIs for Spring Developers to build modern applications using new data stores. It supports object-mapping, DAO repositories and consistent access to underlying database APIs.
Bryce Merkl Sasaki of Neo4j interviews Luanne Misquitta, Senior Consultant at GraphAware, during Graph Connect Europe 2016. Luanne Misquitta talks about Spring Data Neo4j 4.x, a completely rewritten version of SDN to support a high performance object/graph map. Version 4.1 supports both an embedded library as well as Bolt, the new binary protocol for Neo4j.
GraphAware: Towards Online Analytical Processing in Graph Databases
MSc. Thesis submitted for MSc. program in Computing at Imperial College London, written by GraphAware's managing director Michal Bachman.
Download PDFReal-Time Recommendations with Graphs and the Future of Search: Michal Bachman, Managing Director, GraphAware. Michal talks about how they use Neo4j in combination with Elasticsearch to power real-time recommendations.
