18th International Semantic Web Conference, Industry track, 28-30 October 2019, Auckland, NZ

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On the Use of Cloud and Semantic Web
Technologies for Generative Design
Daniel Mercier, PhD
Ali Hashemi, PhD

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Outline
 Context
 Data unification
 Data validation
 Data organization
 Conclusions

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Context

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Autodesk
CAD
Computer Aided Design
Manufacturing
Architecture & Construction
Games & Movies

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Autodesk
Rich portfolio

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The Cloud
 New computing paradigm
 Unlimited storage
 Powerful computing capabilities
 Highly resilient and secured (if properly implemented)
 Synchronized access from multiple geo-locations across the world
 Favors interconnected micro-services
Collaborative & powerful

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Simulation
Different types of simulations:
 Fluid flow,
 Structural,
 Thermal, …
Simulations can be very complex
Execution can vary greatly (milliseconds to weeks)
Making virtual, real
Ref: Autodesk Fusion 360 simulation options
Ref: Fire simulation in Autodesk Maya

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Generative Design
Derive from optimization

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Challenges
 Offer a rich set of multi-physic solver
 Fast ones for an interactive experience, accurate ones to refine selected designs
 Assembling existing solvers and developing new ones
 Aggregate the necessary data consumed by these solvers
 materials, machines, processes, ...
 Composed a schema-based data unification service
 Guide the user to compose the initial problem and identify the desired design
 Composed a validation and recommendation service
 Structure content for optimal storage and performance
 Total/partial reuse with history branching
 Working on application specific data & metadata template system
Create a compatible cloud platform

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Data unification

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Historical deployment
Encapsulated solutions
…

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Engaged migration
Online aggregation of data
…

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Segmented database
One solution = Unique language
Moldflow
Netfabb
Revit
FeatureCam
材料データへのアクセス
…

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Ontology service
To share content
Ontology
service
Moldflow
Netfabb
Revit
FeatureCam
…

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Ontology service
Database information
Schemas
DB registry
Ontology
service
…
Moldflow
Netfabb
Revit
FeatureCam

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Ontology service
Content
Moldflow Term ↔ Class
Molfdlow
Term
Term
Mappings Domain ontology DB registry
Term
Netfabb
Revit
Netfabb Term ↔ Class
Revit Term ↔ Class

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White paper
“Unified Access to Heterogeneous Data Sources Using an Ontology”
Semantic Technology: 8th Joint International Conference,
November 2018, Awaji, Japan
DOI: 10.1007/978-3-030-04284-4_8

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Considerations
On-demand vs Permanent
Dispersed content vs Aggregated content
Lossless vs Losses
Time consuming vs Fast
Next …
 Word embeddings targeting specific engineering domains
to automate/simplify schema matching
Over service uses

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Data validation

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Assist during content creation
With recommendations
JSON
orChunks
of JSON
Creation
Process
Report
&
Recommend
Validator
Generate
Validation
Successful

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z
Data transfer
Internet
Cloud
Server
 Secure communication
 Secure message content
 Assess data syntax (schema)
 Validate data
 Consolidate data
 Propagate data
File storage
and
Database
Compute
workflow

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Data transfer
Internet
Cloud
Server
File storage
and
Database
Compute
workflow
Validator
Service meshes
Used for validation:
• Schema syntax
• Descriptive logic (Ontology axioms)
• Code logic (Procedural codes)
Leverages
JSON

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Knowledge structure
Domain ontologies Application model
• Application specific
• Map to external resources
• Versioned
• Domain dependent
• Portable, Reusable
• Object oriented (versioning)
e.g. geometry (mesh, polygon, vertices),
materials (mechanical, thermal properties)
e.g. Autodesk Autocad, Moldflow, Revit, Maya, …
Classes used to compose

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White paper
“Validation and Recommendation Engine from Service Architecture and
Ontology”
11th International Joint Conference on Knowledge Discovery, Knowledge
Engineering and Knowledge Management
September 2019, Vienna, Austria
DOI: 10.5220/0008070602660273

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Next …
Develop higher intelligence with big data & ML
Internet
Cloud
Server
Validator
Server
Validator
Users
Subject-matter expert
Server
Knowledge
Repository
Big Data
ML
Init/Update Monitor/Collect

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Data organization

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Technology
Transformation
Pre-processing
Processing
Software
Browser
Desktop computer
Web-based interface
Post-processing
DB
User interface
Cloud
Derivatives services
for data transformations
Server
Authentication/Authorization
DB
Object
storage
Computation services

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Data
Transformation
All in one/several files
holds all data
potentially heavy
favors redundancies
versioning not included
user organized
portable
protected by file copies
DB
Object
storage
Structured metadata
point to data
light weight
minimizes redundant data
versioning integrated
automatically organized
centralized content
global availability
protected by block replicas
Desktop Cloud

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Supporting data content & lifecycle
 Thousands of studies per project (up to TB of data)
 Large histories and extended branching
Linked information to define & monitor an application data ecosystem
 Listing & location of data
 Embedded & associated metadata
 Definition of this data lifecycle
For Generative Design

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Conclusions

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Challenges
Using Semantic Web technologies:
 Composed a schema-based data unification service
 Composed a validation and recommendation service
 Working on enhancing the management of data and metadata
For Generative Design

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Knowledge graphs
 Source of data cohesion
 Map complex concepts
 Useful for existing or new applications
 Natural bridge between Human and Machine
 Integration, application specific
 User Interface(UI) / API, critical to abstract inherent complexity
To support Cloud operations

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Semantic Technologies
 Good first layer of intelligence using Descriptive Logic, DL and reasoners
 Excellent complement to Machine Learning, ML
As piece of AI

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Questions

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