Linkurious, founded in 2013 in Paris, is a graph-based product lifecycle management startup that helps clients like NASA and Cisco visualize and analyze complex data using graph technology. Their software enables efficient detection and investigation of insights from interconnected data, addressing challenges in various sectors such as cybersecurity, IT operations, and life sciences. By leveraging graph databases, Linkurious enhances productivity and decision-making throughout product lifecycles, while overcoming limitations of traditional relational database management systems.

1. SAS founded in 2013 in Paris | http://linkurio.us | @linkurious
Graph-based Product
Lifecycle Management

2. Graph visualization and analysis
startup founded in 2013.
40+ clients in 20+ countries (NASA,
Cisco, French Ministry of Finances).
Linkurious Enterprise and Linkurious
SDK.
About Linkurious

3. What is graph technology?
A graph is a data structure that
consists of nodes and edges.
Graph databases store & process
large connected graphs in real-time.
Linkurious’ software helps analysts
easily detect and investigate insights
hidden in graph data.
Node
STORE
ACCESS
Data
Graph
database
Linkurious
ORGANIZE
Edge

4. Some use cases
Cyber-security
Servers, switches, routers,
applications, etc.
Suspicious activity patterns,
identify impact of a compromised
asset.
IT Operations
Servers, switches, routers,
applications, etc.
Impact analysis, root cause
analysis.
Intelligence
People, emails, transactions,
phone call records, social.
Detecting and investigating
criminal or terrorist networks.
AML
People, transactions, watch-lists,
companies, organizations.
Detecting suspicious
transactions, identify beneficiary
owners.
Fraud
Claims, people, financial records,
personal data.
Detecting and investigating
criminal networks.
Life Sciences
Proteins, publications,
researchers, patents, topics.
Understanding protein
interactions, new drugs.
Enterprise
Architecture
Servers, applications, metadata,
business objectives.
Data lineage, curating enterprise
architecture.

5. Product Lifecycle
Management (PLM)
is the process of
managing the entire
lifecycle of a product
from design stage to
development to
go-to-market to
retirement to
disposal.

6. Quality & Compliance
management
Requirement
management
Manufacturing process
management
Maintenance & repair
management
Source and supply chain
management
Portfolio
management
Document
management
Component
management
Configuration/BOM
management
Classification
management
Workflow/Process
management
Change
management
The stakes of PLM
↗ Productivity
↗ Product quality
↘ Costs
↘ Risks

7. The field of PLM technology today
Existing PLM systems rely on
RDBMS.
Siloed data, inability to model
real-life complexities and to adapt to
changes.
Performance issues with multi-level
queries (impact analysis).
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Components
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Bike
Product
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Jaycon
Blueline
Sub-components

8. Limitations of the relational approach
Inability to scale data model without
costs and risks as business evolves.
Searching for information is
time-consuming, 12-30% of
engineer’s time.
Missing insights regarding
dependencies leads to poor decisions
and increases risks.

9. The advantages of graph technology for PLM
Process changes
quickly
Graph technology are more
flexible and scalable than
relational technology and let
you adapt to change.
Reduce search
time
Easily and quickly find
information from large
datasets with interactive
graph data visualizations.
Access hidden insights
about dependencies
Don’t miss any of the
complex dependencies
between elements even on
multiple levels.

10. Represent cross-department data
about products and processes as
a graph and store it as one.
Aggregate product hierarchies and
connections into a single source of
truth.
Easily edit, or expand your model
as your needs evolve.
A flexible and unified model of product lifecycle

11. Save time by searching and
exploring your data through
interactive visualizations.
Filter visualizations to work on
specific elements or specific
sections of the data.
Visually track dependencies between
entities and get contextual insights.
Access hidden insights with graph visualization

12. Demo: PLM with Linkurious.
- Modeling your product data as a graph
- Visualizing your data in Linkurious Enterprise
- Running impact analysis

13. Modeling multi-level BOM data into a graph.

14. Exploring component hierarchy and interdependencies in Linkurious Enterprise

15. Finding patterns of components counting more than 9 dependencies to subcomponents

16. Load data into one of the graph or
RDF DB supported by Linkurious:
Neo4j, DataStax, Titan, AllegroGraph.
Windows / Linux / Mac, on-premise
or in the cloud, supports all modern
browsers.
Use Linkurious Enterprise
off-the-shelf interface or build your
custom application with Linkurious
SDK.
How it works.
DMS or DBMS
Synchronize
automatically
ERP (SAP, Oracle
Applications, IFS
Applications…)
Graph DB (Neo4j,
AllegroGraph,
Titan, DataStax..)

17. Background
International vehicle manufacturer.
Problem
Complex products with many
subcomponents make it hard to
conduct change management.
Benefit
Graph approach breaks silos and
enables easy impact analysis.
Project planning and impact analysis (confidential).

18. Background
International industrial
manufacturer.
Problem
Numerous BOMs to keep track of.
Hard to understand dependencies
between components.
Benefit
Visualization helps communicate
complex results and drive action.
BOM & component management (confidential).

19. Our partners

20. Questions?
www.linkurio.us
contact@linkurio.us

21. Bibliography :
● Bruggen Blog. Using Neo4j to Manage and Calculate Hierarchies. Available:
http://blog.bruggen.com/2014/03/using-neo4j-to-manage-and-calculate.html [September 2017]
● Product lifecycle management. PLM models. Available:
http://www.product-lifecycle-management.info/plm-elements/plm-models.html [September 2017]
● Beyond PLM. PLM graph-aware architecture and search for data. Available
http://beyondplm.com/2017/05/10/plm-graph-aware-architecture-search-relevant-data/
[September 2017]
● PLM Book. What is the right data model for PLM. Available
http://plmbook.com/what-is-right-data-model-for-plm/
Images:
● Istock
● Data visualization icon by Creative Outlet from the Noun Project
● Browser Analytics icon by Oliviu Stoian from the Noun Project
Sources and links