This document discusses the development of a validation and recommendation engine for simulation workflows. The engine uses ontologies and application models to structure validation knowledge that it applies to simulation data. It can check for logical consistency and perform data transformations. The validation services can be distributed across cloud resources and orchestrated using service meshes for flexibility and scalability. Future work involves spreading validators for continuous data quality and integrating machine learning into the knowledge repository.

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Validation and Recommendation Engine
from Service Architecture and Ontology
Daniel Mercier, Anthony Ruto

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Outline
 Context
 Foundations
 Developed service
 Current and future

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Context

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Generative Design
New opportunities

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Simulation
Features:
 Process sequence
 Analyses types
 Solver parameters
 Geometry
 Constraints
 Boundary conditions
Different types of simulations: Fluid flow, structural, thermal, …
 Simulations can be very complex
 Simulations can take a long time (seconds to weeks)
A complex environment
Ref: Autodesk Fusion 360 simulation startup menu

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Cloud integration
 New paradigm
 Unlimited storage
 Favors micro-services
 Libraries of interconnected capabilities
 Scalable to support highly distributed computations
 High availability with redundancies & accelerations
 Capacity to set a high level of security

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Foundations

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z
Data transfers
Requirements
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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Our solution
Internet
Cloud
Server
File storage
and
Database
Compute
workflow
Validator

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Knowledge
Creation, storage and use
Internet
Cloud
Server
Validator
Server
Validator
Users
Subject-matter expert
Server
Knowledge
Repository
Initialize / Update

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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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Developed service

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Knowledge structure
Domain ontologies Application model
• Application specific
• Object orientated
• Map to expected data
• Independent
• Reusable
• Portable
e.g. geometry (mesh, polygon, vertices),
materials (mechanical, thermal properties)
e.g. Autodesk Fusion, Moldflow …
Classes used to compose

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Types of validation
Class content
Descriptive logic Axioms based on ontology content
Based on entities & relationships
Populated by the data through instances of entities
Reasoner to check coherence and consistence
Code logic Procedural programming
Simple data transformations (local)
Advanced data transformations (remote)

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User interface mock
Knowledge creation

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Class components
 Attributes
 Data content required from the data pool for class validation
 Dependencies
 Prerequisites to determine whether the class should be validated
 Conditions
 Requirements to validate the class
 Consolidates original data pool with additional content
 Generates messages intended as recommendations
Note: Another set of recommendations comes from an interpreted form of the reasoner output.

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Execution workflow
Check
Descriptive
Logic
Check
Code Logic
Consolidate
original
data
Pool of
classes
Identify
classes to
validate
Report
Check
prerequisites
No more class
to validate
Run class validations
Inject data
into model

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Advanced data transformations
Dedicated services for workflow orchestrators such as AWS step functions.
Or by micro-service orchestration by ‘service mesh’.
A configurable, low-latency infrastructure layer designed to handle a high volume of network-based inter-process
communication among application infrastructure services using application programming interfaces (APIs)
Remote executions
Ref: nginx.com/blog/what-is-a-service-mesh

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Flexibility
 Many Cloud providers
 Many service mesh solutions from &
By plugin

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Current and future

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Our current solution
Internet
Cloud
Server
Independent
compute
units
File storage
and
Database
Service
meshes
Validator

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Validators
Spreading internally
To ensure constant data quality
Internet
Cloud
Server
Independent compute units
File storage and Database
Service meshes
ValidatorValidatorsValidatorsValidators

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Knowledge repository
Current workflow
Internet
Cloud
Server
Validator
Server
Validator
Users
Subject-matter expert
Server
Knowledge
Repository
Initialize / Update

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Knowledge repository
Envisioned capability
Internet
Cloud
Server
Validator
Server
Validator
Users
Subject-matter expert
Server
Knowledge
Repository
Big Data
ML

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Summary
 Lightweight service for validation and recommendation
 JSON based
 Rich set of validation techniques
with Descriptive Logic and Code Logic
 Well-structured and easy to use validation knowledge base
by combining Domain ontologies and Application models
 Easily deployable and scalable

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Questions

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