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...
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...
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...
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...
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...
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....
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....
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...
Graphs are a perfect fit for IT Operations. Right from dependency management to impact analysis and capacity to outage planning, the interconnectedness of the components that make up networks and services, modelled naturally as a graph enable various teams such as support, help desk and devops to navigate potentially complex relationships.BackgroundThe size of networks has been rapidly increasing and along with it, assets such as applications, services, and devices.IT managers and operations teams have been facing challenges around quick response times and incident analysis due to the inability of traditional databases, such as relational, to process heavily hierarchical and interconnected...
GraphAware is proud to announce the release of Hume 2.6. This new release brings some major updates and exciting new features to our customers. In particular: [New] Hume.Perspectives: a mechanism for specifying multiple subschemas of the main Knowledge Graph. It improves security and readability, allowing users to specify who can read what. [Improved] Hume.Labs 2.0: the latest version of Hume.Labs improves security, aligns it to the Hume RBAC model, simplifies management of multiple projects and enormously reduces (almost to 0) the data scientists’ effort for building language models and related skills from an annotated text or dictionary. [Improved] Hume.Viz: the...
If you have read our post Hume in Space: Monitoring Satellite Technology Markets with a ML-powered Knowledge Graph, you surely wonder: is there a way to extract relations among named entities without heavy investment? Investment in terms of time to label training dataset and to develop, train and deploy a machine learning model?Yes, there is! But first things first …There are many ways to approach the problem. If you are a data scientist, your first instinct is probably Deep Learning (DL). Entity relation extraction, i.e. classifying relation types between named entities such as (:Person)-[:WORKS_FOR]->(:Organization), is clearly a perfect use case...
Everyone has a passion for something. Be it music, politics, sports, coffee or … pancakes. Such passion makes you strive for new information, for understanding of the current trends. Take pancakes: you might watch for new recipes on your favourite website, you might look at cooking shows or youtube videos to get more inspiration about how to serve them … but overall, you can probably handle this pretty well. It’s not like there is much room for revolutionising the pancake recipe.Imagine a different context: let’s say that your passion is not limited to your kitchen, but reaches from the ground...
GraphAware Hume helps governments in keeping their countries safe. In this 15-minute video, we demonstrate the use of Hume for contact tracing and smart quarantine in the context of the current coronavirus pandemic. Specifically, we will see how Hume can identify people at risk using actual and potential contact tracing, suggest who should be informed or quarantined, visually explain why someone is at risk, find quarantine offenders, and much more.Hume can do much more than structured data analysis. It is a full blown ecosystem for intelligent systems built upon the combined power of collaborative knowledge graphs and machine learning.Hume’s unique...
BOSTON, July 23, 2019 /PRNewswire/ – GraphAware, a leading Neo4j ISV and consulting practice, today announced the official launch of its Italian Research and Development entity Graph Aware S.r.l., headquartered in Lecce, Italy.This strategic investment by GraphAware represents a significant expansion as an ISV, with a fast growing development team of thought-leaders in GraphDBs with Neo4j, Natural Language Processing (NLP), Machine Learning (ML) and Artificial Intelligence (AI).Led by Chief Scientist Alessandro Negro and CTO Christophe Willemsen,the Lecce R&D center is the main lab and development center for GraphAware’s flagship software platform Hume- with a dedicated local and remote development team...