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From GraphAware Framework to GraphAware Hume

06 May 2021 by Michal Bachman Hume GraphAware Neo4j

From GraphAware Framework to GraphAware Hume

It has been over 8 years since I’ve written the first lines of code for the GraphAware Neo4j Framework as part of my MSc. thesis. That’s when the name GraphAware, as well as the (then) one-man show Neo4j consulting company was born. It is therefore my bittersweet duty to take you on a small trip down the memory laneand announce that we have decided to discontinue the development and support of the Framework and all of its modules.In this blog post, we briefly cover the bits of Neo4j and the GraphAware Framework history that are essential to understandingthe technical reasons...

What’s new in Hume 2.8: Snapshots, Virtual Relationships, and much more!

08 Apr 2021 by Esther Bergmark Hume GraphAware

What’s new in Hume 2.8: Snapshots, Virtual Relationships, and much more!

GraphAware is proud to announce the 2.8 release of Hume, our graph-powered insights engine. The release adds further unique capabilities to Hume’s knowledge graph visualisation and graph data analysis capabilities. Analysts, data scientists, investigators, and data-savvy business users will profit from these benefits: Save permanently the insights you uncovered using Snapshots. With Snapshots, you can pick up your exploration at any time without worrying about changes in data or schema and, eventually, share with other analysts. Make graph patterns more evident by shortcutting a long path through a virtual relationship: Hume’s Virtual Relationships let knowledge graph managers and analysts cut...

Neo4j Change Data Capture with GraphAware Hume

29 Mar 2021 by Christophe Willemsen Neo4j Hume

Neo4j Change Data Capture with GraphAware Hume

CDC (Change Data Capture) is a well defined software design pattern for a system that monitors and captures data changes so that other software can respond to those events.CDC has many advantages compared to the traditional polling approach : All changes are captured: Intermediary changes between two polls are tracked and can be acted upon Real-time and low overhead: Reacting to CDC events happens in real time and only when changes happen avoiding CPU overhead of frequent polling Loose coupling: CDC send captured changes to messaging brokers, consumers can be added or removed on demand Applications of Neo4j Change Data...

Registering a custom analyzer for phonetic search in Neo4j 4

11 Mar 2021 by Luanne Misquitta Neo4j Cypher Search

Phonetic matching attempts to match words by pronunciation instead of spelling. Words are typically misspelled and exact matches result in them not being found.Algorithms such as Soundex and Metaphone were developed to address this problem and they have found usage in the areas of voice assistants, search, record linking and fraud detection, misspelled names of things (for example, medical records) etc.Custom analyzersIn 2019, we blogged about creating a Czech analyzer to address accents in the language.With Neo4j 4, a few things have changed. This short blog post was inspired by a StackOverflow question on phonetic searches and resulted in me...

New in Hume 2.7: Search relevance, improved visualisation, and much more!

24 Feb 2021 by Dr. Alessandro Negro Hume GraphAware

New in Hume 2.7: Search relevance, improved visualisation, and much more!

GraphAware is proud to announce the 2.7 release of Hume, our graph-powered insights engine. The release significantly enhances Hume’s knowledge graph visualisation and graph data analysis capabilities. Analysts, data scientists, investigators, and data-savvy business users immediately get the following main benefits: Faster identification of the starting points for analysis through configurable search relevance and improved end-user search experience Speedup in time to insight thanks to the long-awaited, attribute-based relationship styling Increased flexibility for individual analysts brought by automatically computed virtual node and relationship attributes Reduction of dead-end investigation paths by preventing the “hairball” problem through configurable double-click actions Reduction of...

Hume Orchestra Monitoring

12 Feb 2021 by Andrea Evangelista Hume Monitoring

Hume Orchestra Monitoring

Enterprise Integration has existed since many, many years. Although it might seem like an old set of patterns, the reality is that more and more data silos, protocols and systems have been created in recent times which increase the need for the capabilities of an Enterprise Integration platform.OrchestraOrchestra is a module of the GraphAware Hume platform offering us the ability to manage and solve real business problems in the Big Data and Agile Integration space.The natural human approach taken to solve a problem is to decompose it into simple steps, adapt some of them, get help or advice from others...

Reactive data copy using Neo4j

14 Jan 2021 by Nicolas Mervaillie Neo4j Performance

Reactive data copy using Neo4j

The release of Neo4j 4.0 brought many improvements, one of them being areactive architecture across the stack, from query execution to clientdrivers. But how does that compare to other approaches ? As stated inthe reactive manifesto, areactive system is more scalable and responsive, by having a more efficient resource usage.I was curious to see this in action, and check the benefit. In thisarticle we will take a simple example of copying data from one databaseto another and compare the reactive approach to traditional onesregarding execution speed and resource usage.We’ll use Java to copy data between 2 separate Neo4j instances, all...

Exploring The MET Art Collections with Hume #2

06 Jan 2021 by Antonin Smid Hume Knowledge Graph

Exploring The MET Art Collections with Hume #2

In our last MET Art Collections post we ingested and processed part of a dataset containing more than 470,000 artworks from The Metropolitan Museum of Art and created a knowledge graph using Hume, GraphAware’s insights engine.This time, we will have a look at four use cases demonstrating how to get insights from the knowledge graph. We will start with Hume Visualisations to explore tag’s context; create Hume Actions to analyse the donors, and finally, use the Graph Data Science Library to suggest similar paintings.Exploring tag’s contextWe do not need complex queries to find interesting facts in the Art knowledge graph....

Exploring The MET Art Collections with Hume #1

10 Dec 2020 by Antonin Smid Hume Knowledge Graph

Exploring The MET Art Collections with Hume #1

The Metropolitan Museum of Art recently published a dataset of more than 470,000 works of art under the CC-zero License. Representing such a collection as a knowledge graph allows us to explore it in a unique way - seeing the artworks, their authors, donors, mediums, tags, or art movements deeply connected, being able to traverse the links between them and discover unexpected relations.The inspiration to explore this dataset spring from an exciting challenge by Neo4j, the Summer of Nodes: Week 2, make sure to check it out.To create and explore the Art knowledge graph we will use Hume insights engine....

Welcome to the Hume 2.6 live event

04 Dec 2020 by Michal Bachman, Alessandro Negro, Miro Marchi Hume Webinar

Welcome to the Hume 2.6 live event

In an increasingly complex and hyperconnected world, organizations need a level of insight, collaboration and optimization into their data that is locked away within different systems and siloed within the different teams. Information is hidden and opportunities are missed because of the lack of a single system to host, analyze, and visualize their data.Welcome to Hume.Hume is the insights engine that collects your scattered data into one full graph-powered solution for your analysts to make sense of their data. Built on top of the cutting-edge technology by Neo4j, Hume has the ability to connect your structured and unstructured data into...