Graphs for water networks, company metadata, and recipe ontologies

Date: 
Wednesday, April 26, 2017 - 18:30
Source: 
Neo4j - London User Group
Attendees: 
58
City: 
London

This month we'll have lightning talks showing how to use Neo4j to analyse a problem in the Thames Water network and to manage metadata at ENGIE, a dynamic exploration-production company.

Please make sure you sign up on the Skillsmatter page as well for quick entry into the venue.


Data moons on geo-sticks
Nick Fosbery, Subsurface Data Analyst and Application Support at ENGIE E&P

The Subsurface Data Management department at ENGIE E&P UK had a requirement for a new software solution to manage their seismic metadata to replace an ageing SQL based application that was no longer up to the job, but needed to be integrated with the in-house GIS, business intelligence and SharePoint systems.

However, with a collapse in the price of oil and a budget of zero pounds, it was time to put some data moons on geo sticks to build a novel solution with Neo4j (including the new spatial libraries) at the core – on time and on budget!

How Neo4j solved a problem at Thames Water
Dylan Omran, Business Intelligence Analyst/Project Manager at Aqua Tech Consulting

In this talk Dylan will explain how Neo4j offered a way to consider the connectivity of a water network when querying datasets like events against contacts. The specific challenge that prompted Dylan to turn to Neo4j was identifying a link between supply interruptions (people don't have water) and the complaints Thames Water received.

Various people had tried to model that link but without considering network connectivity. That doesn't work very well because it leads to associating unconnected events. The model Dylan built in Neo4j worked really well and has since been used on other challenges like data cleanse and some water domain-specific tasks.

Recipe and ingredients ontology with Neo4j
Irene Iriarte, Data Scientist at Gousto

Gousto is an online retailer that delivers recipe kit boxes with exact quantities of fresh ingredients and step-by-step recipes. Customers can pick from a weekly menu, consisting of 22 different recipes. It is vital that the menus are balanced and contain a wide range of recipes, with, for example, different cuisines and different protein sources. Designing these menus can therefore be a time consuming task.
In order to automate and speed up this process, we needed a way to measure the similarity between recipes.

We therefore implemented a recipe and ingredient ontology into NEO4J, which gave us the flexibility of structure we needed to look at recipes from different angles and ensure our menus are varied enough. 

Skillsmatter (CodeNode)

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