Protecting Australias red meat industry with interactive data - PowerPoint PPT Presentation

Protecting Australia’s red meat industry with interactive data visualisation Grant Carlson – Development Manager Binh (James) Tham – Lead Solution Architect Integrity Systems Company YOW! Data 2018

About us

About us Livestock Production National Livestock National Vendor Assurance Identification System Declaration 220,000 Customers e A y r y y n a F t T e B t t i f i r i o m l i r l o a f e o u a b i a w s c c d s l e e a Market access Customer expectations Safe red meat

Why traceability matters

Disease outbreaks are catastrophic A Foot & Mouth UK (FMD) outbreak Disease £11 Billion (FMD) outbreak $50 Billion in 2001 In Australia BSE (Mad Cow) National Livestock BSE cost $7 Billion Identification System $11 Billion 2003, 40 countries In USA beef exports ban imports 2004-2007 From Canada

Australia exports 70% of production World beef production: Ranking of countries World beef exports: Ranking of countries United States Brazil EU China India Argentina Australia Brazil India Australia United States New Zealand Canada Paraguay 14 2 3 rd largest 1.8 12 1.6 beef exporter 10 1.4 in the world 1.2 7 th largest 8 ‘million ‘million 1 beef producer 6 0.8 in the world 0.6 4 0.4 2 0.2 0 0

How traceability happens

What happens if a disease is found? the location(s) where a specified animal has been Within 48 hours establish a listing of all cattle that have lived on it must be possible the same property to determine the current location of all cattle

Traceability Starting point Cohorts Trace forward Property Current property Trace back

National Livestock Identification System All cattle are RFID tagged All movements recorded

National Livestock Identification System stats Over 500 million 230,000 Properties movement records 2 to 3 million movements 205 million 80,000 per month RFID tags users (30 million active cattle)

Cow Catcher – a national test of the system Emails SQL Scripts SQL CSV

Database modelling with graph database Graph = Nodes + Relationships Tag= WJWK0034XBM30929 PIC = WGED0026

Database modelling with graph database Graph = Nodes + Relationships Tagx stayed on PICy 2. Relationship “ stayed ” from a TAG to a PIC

Database modelling with graph database Graph = Nodes + Relationships Tagx stayed on PICy Movements from PICx to PICy

Database modelling with graph database Graph = Nodes + Relationships PIC PIC PIC TAG

Validate database model with CowCatcher Within 48 hours A live demonstration with Neo4J it must be possible to determine PIC-C PIC-B PIC-A TAG

Architecture DEMO Neo4j browser Queue GraphDB

with native browser Use the built in browser for the POC? Issue 1: Deleting nodes is a nightmare! Have to deal with orphan nodes

with native browser Use the built in browser for the POC? Issue 2: No “undo” for expansion

DEMO BROWSER

with enhanced browser https://github.com/btham/neo4j-browser Source code: For community ● Added in “cut” button ● Dealt with fixed and floating nodes differently when delete For tracing purpose: ● Parameterized expansion especially for tracing ● Cohort calculation and expansion for tracing ● Trace further - cohorts of cohorts (not in the demo)

Interactive visualisation can help simplify tracing A graph database model is perfect Building on the open source browser quick and easy – vote for Binh to push the feature request! AgTech is the place to be! Eat more beef

THANK YOU! Questions? No cows were harmed in the creation of this No animals were harmed in the creation presentation of this presentation