In the new era of digitalization, there is an ever-growing need for design and production processes capable of increasing systems quality, reducing risks and the chance of errors, while, at the same me, reducing overall production costs. Nowadays, more and more systems design scenarios comprise a high number of domains.
However, the underlying tool landscape is still dominated by closed ecosystems, resulting in the design data remaining in separate silos. To effectively deal with novel, massively diverse yet interconnected engineering scenarios, while also considering industrial sustainability and the well-being of the future digital society, we have to propose new ways to look at the digital thread, supporting every phase of a digital engineering lifecycle, while turning the siloed multi-domain engineering data into a holistic, accessible and globally analyzable digital thread.
Report
Share
Report
Share
1 of 37
More Related Content
Similar to IncQuery_presentation_Incose_EMEA_WSEC.pptx
Eclipse Hawk provides scalable querying of models by indexing them into graph databases. It addresses challenges of collaborative modeling on large systems by distributed teams. The Hawk API is designed for flexibility, performance, and scalability through features like multiple communication styles, efficient encodings, and paged results.
Deploying ML models in production, with or without CI/CD, is significantly more complicated than deploying traditional applications. That is mainly because ML models do not just consist of the code used for their training, but they also depend on the data they are trained on and on the supporting code. Monitoring ML models also adds additional complexity beyond what is usually done for traditional applications. This talk will cover these problems and best practices for solving them, with special focus on how it's done on the Databricks platform.
This presentation is about a lecture I gave within the "Software systems and services" immigration course at the Gran Sasso Science Institute, L'Aquila (Italy): http://cs.gssi.infn.it/.
http://www.ivanomalavolta.com
Source-to-source transformations: Supporting tools and infrastructurekaveirious
Introduction to source-to-source transformation. Concept and overview. Basics of existing tools (TXL, ROSE, Cetus, EDG, C-to-C, Memphis); pros and cons. Part of an internal evaluation for selecting a source-to-source transformation tool.
Data Scientists and Machine Learning practitioners, nowadays, seem to be churning out models by the dozen and they continuously experiment to find ways to improve their accuracies. They also use a variety of ML and DL frameworks & languages , and a typical organization may find that this results in a heterogenous, complicated bunch of assets that require different types of runtimes, resources and sometimes even specialized compute to operate efficiently.
But what does it mean for an enterprise to actually take these models to "production" ? How does an organization scale inference engines out & make them available for real-time applications without significant latencies ? There needs to be different techniques for batch (offline) inferences and instant, online scoring. Data needs to be accessed from various sources and cleansing, transformations of data needs to be enabled prior to any predictions. In many cases, there maybe no substitute for customized data handling with scripting either.
Enterprises also require additional auditing and authorizations built in, approval processes and still support a "continuous delivery" paradigm whereby a data scientist can enable insights faster. Not all models are created equal, nor are consumers of a model - so enterprises require both metering and allocation of compute resources for SLAs.
In this session, we will take a look at how machine learning is operationalized in IBM Data Science Experience (DSX), a Kubernetes based offering for the Private Cloud and optimized for the HortonWorks Hadoop Data Platform. DSX essentially brings in typical software engineering development practices to Data Science, organizing the dev->test->production for machine learning assets in much the same way as typical software deployments. We will also see what it means to deploy, monitor accuracies and even rollback models & custom scorers as well as how API based techniques enable consuming business processes and applications to remain relatively stable amidst all the chaos.
Speaker
Piotr Mierzejewski, Program Director Development IBM DSX Local, IBM
The document discusses Clean Architecture, an architectural pattern for software design. It aims to facilitate maintainability, technical agility, and independent development. Clean Architecture prescribes separating an application into distinct layers - entities, use cases, interfaces, and entry points. This separation aims to make codebases independent of frameworks and easily testable. The document outlines principles like SOLID and DRY, and patterns like layered architecture and MVC that influence Clean Architecture. It provides tips for migrating existing applications to this architecture.
How a Data Mesh is Driving our Platform | Trey Hicks, GlooHostedbyConfluent
At Gloo.us, we face a challenge in providing platform data to heterogeneous applications in a way that eliminates access contention, avoids high latency ETLs, and ensures consistency for many teams. We're solving this problem by adopting Data Mesh principles and leveraging Kafka, Kafka Connect, and Kafka streams to build an event driven architecture to connect applications to the data they need. A domain driven design keeps the boundaries between specialized process domains and singularly focused data domains clear, distinct, and disciplined. Applying the principles of a Data Mesh, process domains assume the responsibility of transforming, enriching, or aggregating data rather than relying on these changes at the source of truth -- the data domains. Architecturally, we've broken centralized big data lakes into smaller data stores that can be consumed into storage managed by process domains.
This session covers how we’re applying Kafka tools to enable our data mesh architecture. This includes how we interpret and apply the data mesh paradigm, the role of Kafka as the backbone for a mesh of connectivity, the role of Kafka Connect to generate and consume data events, and the use of KSQL to perform minor transformations for consumers.
[2017/2018] Introduction to Software ArchitectureIvano Malavolta
This document provides an introduction to software architecture concepts. It defines software architecture as the selection of structural elements and their interactions within a system. Common architectural styles are described, including Model-View-Controller (MVC), publish-subscribe, layered, shared data, peer-to-peer, and pipes and filters. Tactics are introduced as design decisions that refine styles to control quality attributes. The document emphasizes that architectural styles solve recurring problems and promote desired qualities like performance, security, and maintainability.
[2016/2017] Introduction to Software ArchitectureIvano Malavolta
This presentation is about a lecture I gave within the "Software systems and services" immigration course at the Gran Sasso Science Institute, L'Aquila (Italy): http://cs.gssi.infn.it/.
http://www.ivanomalavolta.com
Revolutionary container based hybrid cloud solution for MLPlatform
Ness' data science platform, NextGenML, puts the entire machine learning process: modelling, execution and deployment in the hands of data science teams.
The entire paradigm approaches collaboration around AI/ML, being implemented with full respect for best practices and commitment to innovation.
Kubernetes (onPrem) + Docker, Azure Kubernetes Cluster (AKS), Nexus, Azure Container Registry(ACR), GlusterFS
Workflow
Argo->Kubeflow
DevOps
Helm, kSonnet, Kustomize,Azure DevOps
Code Management & CI/CD
Git, TeamCity, SonarQube, Jenkins
Security
MS Active Directory, Azure VPN, Dex (K8s) integrated with GitLab
Machine Learning
TensorFlow (model training, boarding, serving), Keras, Seldon
Storage (Azure)
Storage Gen1 & Gen2, Data Lake, File Storage
ETL (Azure)
Databricks, Spark on K8, Data Factory (ADF), HDInsight (Kafka and Spark), Service Bus (ASB)
Lambda functions & VMs, Cache for Redis
Monitoring and Logging
Graphana, Prometeus, GrayLog
Integration Patterns for Big Data ApplicationsMichael Häusler
Big Data technologies like distributed databases, queues, batch processors, and stream processors are fun and exciting to play with. Making them play nicely together can be challenging. Keeping it fun for engineers to continuously improve and operate them is hard. At ResearchGate, we run thousands of YARN applications every day to gain insights and to power user facing features. Of course, there are numerous integration challenges on the way:
* integrating batch and stream processors with operational systems
* ingesting data and playing back results while controlling performance crosstalk
* rolling out new versions of synchronous, stream, and batch applications and their respective data schemas
* controlling the amount of glue and adapter code between different technologies
* modeling cross-flow dependencies while handling failures gracefully and limiting their repercussions
We describe our ongoing journey in identifying patterns and principles to make our big data stack integrate well. Technologies to be covered will include MongoDB, Kafka, Hadoop (YARN), Hive (TEZ), Flink Batch, and Flink Streaming.
A world's one of the first complete Online Web-based Development Frameworks to develop and deploy Decision Support Systems, Knowledge-based systems, Web-sites and Applications backed by Expert System, Case-Based Reasoning and Hybrid AI Technologies
This document discusses application architecture and considerations for different layers including presentation, domain, and data source layers. It covers topics like layering, client types, content delivery, domain layer patterns like transaction script, domain model and table module. It also discusses data source layer patterns like gateway, active record and data mapper. Finally, it provides an example of implementing user signup in the Play! framework.
Apidays Paris 2023 - Productizing AsyncAPI for Data Replication and Changed D...apidays
Apidays Paris 2023 - Software and APIs for Smart, Sustainable and Sovereign Societies
December 6, 7 & 8, 2023
Productizing AsyncAPI for Data Replication and Changed Data Capture
Julien Testut, Senior Principal Product Manager, Oracle
------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
https://www.apiscene.io
Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
Helixa uses serverless machine learning architectures to power an audience intelligence platform. It ingests large datasets and uses machine learning models to provide insights. Helixa's machine learning system is built on AWS serverless services like Lambda, Glue, Athena and S3. It features a data lake for storage, a feature store for preprocessed data, and uses techniques like map-reduce to parallelize tasks. Helixa aims to build scalable and cost-effective machine learning pipelines without having to manage servers.
The document discusses software design and implementation. It describes the design phase as involving high-level architectural design to develop the overall structure of a software program, and low-level detailed design to develop specific algorithms and data structures. The implementation phase includes activities like constructing software components, testing, developing prototypes, training, and installing the system. Good design principles include modularity, low coupling between modules, and high cohesion within modules.
In this introductory session, we dive into the inner workings of the newest version of Azure Data Factory (v2) and take a look at the components and principles that you need to understand to begin creating your own data pipelines. See the accompanying GitHub repository @ github.com/ebragas for code samples and ADFv2 ARM templates.
Similar to IncQuery_presentation_Incose_EMEA_WSEC.pptx (20)
Towards Scalable Validation of Low-Code System Models: Mapping EVL to VIATRA ...IncQuery Labs
Presented at the LowCode Workshop 2021 at MODELS 2021 by Benedek Horváth. Authors are Qurat ul ain Ali, Benedek Horváth, Dimitris Kolovos, Konstantinos Barmpis and Ákos Horváth.
Towards Continuous Consistency Checking of DevOps ArtefactsIncQuery Labs
Presented at the International Workshop DevOps@MODELS 2021 by Benedek Horváth. The authors are Alessandro Colantoni, Benedek Horváth, Ákos Horváth, Luca Berardinelli, and Manuel Wimmer (Johannes Kepler University Linz, IncQuery Labs).
The Genesis of Holistic Systems Engineering: Completeness and Consistency Man...IncQuery Labs
The document discusses the challenges of disconnected engineering silos and proposes a framework to address it. It presents the 3C challenge of completeness, correctness, and consistency when transferring systems engineering data to detailed design tools. The framework includes automated bridge tools to create a digital thread between tools and digital thread analytics to analyze the connections and identify issues. It demonstrates connecting a systems engineering tool to an electrical design tool to map components and ensure signal allocations are consistent.
On 18th September, our CEO, István Ráth, joined by Enrique Krajmalnik from Zuken, presented at the 2021 INCOSE Western States Regional Conference. Their talk concentrated on the current challenges of systems engineering, promoting a much-needed paradigm shift and a novel, holistic approach.
The conceptual framework underpinning this novel concept is the combination of light-weight bridge tools, such as the E3.GENESYS Connector from Zuken, and digital thread analytics powered by our flagship product, the IncQuery Suite. This framework provides discipline-specific views of multi-domain engineering data, and powerful structural and numerical analysis to ensure completeness, correctness and consistency throughout the entire design process.
Towards the Next Generation of Reactive Model Transformations on Low-Code Pla...IncQuery Labs
Authors: Benedek Horváth(IncQuery Labs cPlc., Johannes Kepler University Linz, Linz, Austria), Ákos Horváth (IncQuery Labs cPlc.), Manuel Wimmer (Johannes Kepler University Linz, Linz, Austria)
Read the research here: https://dl.acm.org/doi/10.1145/3417990.3420199
Model Checking as a Service: Towards Pragmatic Hidden Formal MethodsIncQuery Labs
Authors: Bence Graics, Ákos Hajdu, Zoltán Micskei, Vince Molnár, István Ráth, Luigi Andolfato, Ivan Gomes, and Robert Karban
Read the research here: https://dl.acm.org/doi/10.1145/3417990.3421407
1. The paper presents EMF-IncQuery, a framework for incrementally evaluating model queries over EMF models as the models evolve.
2. Experiments show that EMF-IncQuery can efficiently recompute query results incrementally in real-time as models with over 1.5 million elements are modified, enabling on-the-fly model validation.
3. EMF-IncQuery has been applied in both academic research and industrial tools/applications for tasks like model validation, visualization, program analysis, and design space exploration.
Incquery Suite Models 2020 Conference by István Ráth, CEO of IncQuery LabsIncQuery Labs
This document discusses how IncQuery Suite can be used to analyze digital threads in model-based systems engineering (MBSE) projects. It provides an overview of IncQuery Suite's features for efficiently extracting and analyzing engineering data across proprietary tools, validating documents and projects, performing graph queries and full-text search, and integrating with various tools. The document also presents two case studies, one involving integrating IncQuery Suite with Airbus's application platform to enable data continuity, and another using IncQuery Suite to provide model checking as a service for SysML models.
Lessons learned from building Eclipse-based add-ons for commercial modeling t...IncQuery Labs
In this presentation, we summarize the lessons we have learned during the MagicDraw adaptation of VIATRA, Eclipse’s open source framework for scalable reactive model transformations. We have built V4MD, an open source extension for MagicDraw that others can freely reuse and build on, and IncQuery for MagicDraw, a commercial add-on that provides powerful yet user-friendly querying and validation capabilities.
IN Dubai [WHATSAPP:Only (+971588192166**)] Abortion Pills For Sale In Dubai** UAE** Mifepristone and Misoprostol Tablets Available In Dubai** UAE
CONTACT DR. SINDY Whatsapp +971588192166* We Have Abortion Pills / Cytotec Tablets /Mifegest Kit Available in Dubai** Sharjah** Abudhabi** Ajman** Alain** Fujairah** Ras Al Khaimah** Umm Al Quwain** UAE** Buy cytotec in Dubai +971588192166* '''Abortion Pills near me DUBAI | ABU DHABI|UAE. Price of Misoprostol** Cytotec” +971588192166* ' Dr.SINDY ''BUY ABORTION PILLS MIFEGEST KIT** MISOPROSTOL** CYTOTEC PILLS IN DUBAI** ABU DHABI**UAE'' Contact me now via What's App… abortion pills in dubai Mtp-Kit Prices
abortion pills available in dubai/abortion pills for sale in dubai/abortion pills in uae/cytotec dubai/abortion pills in abu dhabi/abortion pills available in abu dhabi/abortion tablets in uae
… abortion Pills Cytotec also available Oman Qatar Doha Saudi Arabia Bahrain Above all** Cytotec Abortion Pills are Available In Dubai / UAE** you will be very happy to do abortion in Dubai we are providing cytotec 200mg abortion pills in Dubai** UAE. Medication abortion offers an alternative to Surgical Abortion for women in the early weeks of pregnancy. We only offer abortion pills from 1 week-6 Months. We then advise you to use surgery if it's beyond 6 months. Our Abu Dhabi** Ajman** Al Ain** Dubai** Fujairah** Ras Al Khaimah (RAK)** Sharjah** Umm Al Quwain (UAQ) United Arab Emirates Abortion Clinic provides the safest and most advanced techniques for providing non-surgical** medical and surgical abortion methods for early through late second trimester** including the Abortion By Pill Procedure (RU 486** Mifeprex** Mifepristone** early options French Abortion Pill)** Tamoxifen** Methotrexate and Cytotec (Misoprostol). The Abu Dhabi** United Arab Emirates Abortion Clinic performs Same Day Abortion Procedure using medications that are taken on the first day of the office visit and will cause the abortion to occur generally within 4 to 6 hours (as early as 30 minutes) for patients who are 3 to 12 weeks pregnant. When Mifepristone and Misoprostol are used** 50% of patients complete in 4 to 6 hours; 75% to 80% in 12 hours; and 90% in 24 hours. We use a regimen that allows for completion without the need for surgery 99% of the time. All advanced second trimester and late term pregnancies at our Tampa clinic (17 to 24 weeks or greater) can be completed within 24 hours or less 99% of the time without the need for surgery. The procedure is completed with minimal to no complications. Our Women's Health Center located in Abu Dhabi** United Arab Emirates** uses the latest medications for medical abortions (RU-486** Mifeprex** Mifegyne** Mifepristone** early options French abortion pill)** Methotrexate and Cytotec (Misoprostol). The safety standards of our Abu Dhabi** United Arab Emirates Abortion Doctors remain unparalleled. They consistently maintain the lowest complication rates throughout the nation. Our
Discover Passkeys, the next evolution in secure login methods that eliminate traditional password vulnerabilities. Learn about the CBSecurity Passkeys module's installation, configuration, and integration into your application to enhance security.
Sami provided a beginner-friendly introduction to Amazon Web Services (AWS), covering essential terms, products, and services for cloud deployment. Participants explored AWS' latest Gen AI offerings, making it accessible for those starting their cloud journey or integrating AI into coding practices.
Major Outages in Major Enterprises Payara ConferenceTier1 app
In this session, we will be discussing major outages that happened in major enterprises. We will analyse the actual thread dumps, heap dumps, GC logs, and other artifacts captured at the time of the problem. After this session, troubleshooting CPU spikes, OutOfMemoryError, response time degradations, network connectivity issues, and application unresponsiveness may not stump you.
Drona Infotech is one of the best Mobile App Development Company in Noida. Elevate your business with our professional app development services. Let us help you create user-friendly and high-performing mobile applications.
Visit Us For: https://www.dronainfotech.com/mobile-application-development/
Seamless PostgreSQL to Snowflake Data Transfer in 8 Simple StepsEstuary Flow
Unlock the full potential of your data by effortlessly migrating from PostgreSQL to Snowflake, the leading cloud data warehouse. This comprehensive guide presents an easy-to-follow 8-step process using Estuary Flow, an open-source data operations platform designed to simplify data pipelines.
Discover how to seamlessly transfer your PostgreSQL data to Snowflake, leveraging Estuary Flow's intuitive interface and powerful real-time replication capabilities. Harness the power of both platforms to create a robust data ecosystem that drives business intelligence, analytics, and data-driven decision-making.
Key Takeaways:
1. Effortless Migration: Learn how to migrate your PostgreSQL data to Snowflake in 8 simple steps, even with limited technical expertise.
2. Real-Time Insights: Achieve near-instantaneous data syncing for up-to-the-minute analytics and reporting.
3. Cost-Effective Solution: Lower your total cost of ownership (TCO) with Estuary Flow's efficient and scalable architecture.
4. Seamless Integration: Combine the strengths of PostgreSQL's transactional power with Snowflake's cloud-native scalability and data warehousing features.
Don't miss out on this opportunity to unlock the full potential of your data. Read & Download this comprehensive guide now and embark on a seamless data journey from PostgreSQL to Snowflake with Estuary Flow!
Try it Free: https://dashboard.estuary.dev/register
Non-Functional Testing Guide_ Exploring Its Types, Importance and Tools.pdfkalichargn70th171
Are you looking for ways to ensure your software development projects are successful? Non-functional testing is an essential part of the process, helping to guarantee that applications and systems meet the necessary non-functional requirements such as availability, scalability, security, and usability.
Lots of bloggers are using Google AdSense now. It’s getting really popular. With AdSense, bloggers can make money by showing ads on their websites. Read this important article written by the experienced designers of the best website designing company in Delhi –
Break data silos with real-time connectivity using Confluent Cloud Connectorsconfluent
Connectors integrate Apache Kafka® with external data systems, enabling you to move away from a brittle spaghetti architecture to one that is more streamlined, secure, and future-proof. However, if your team still spends multiple dev cycles building and managing connectors using just open source Kafka Connect, it’s time to consider a faster and cost-effective alternative.
Explore the rapid development journey of TryBoxLang, completed in just 48 hours. This session delves into the innovative process behind creating TryBoxLang, a platform designed to showcase the capabilities of BoxLang by Ortus Solutions. Discover the challenges, strategies, and outcomes of this accelerated development effort, highlighting how TryBoxLang provides a practical introduction to BoxLang's features and benefits.
A captivating AI chatbot PowerPoint presentation is made with a striking backdrop in order to attract a wider audience. Select this template featuring several AI chatbot visuals to boost audience engagement and spontaneity. With the aid of this multi-colored template, you may make a compelling presentation and get extra bonuses. To easily elucidate your ideas, choose a typeface with vibrant colors. You can include your data regarding utilizing the chatbot methodology to the remaining half of the template.
CommandBox was highlighted as a powerful web hosting solution, perfect for developers and businesses alike. Featuring a built-in server and command-line interface, CommandBox simplified web application management. Developers could deploy multiple application instances simultaneously, optimizing development workflows. CommandBox's efficient deployment processes ensured reliable web hosting, seamlessly integrating into existing workflows for scalability and feature enhancements.
How to debug ColdFusion Applications using “ColdFusion Builder extension for ...Ortus Solutions, Corp
Unlock the secrets of seamless ColdFusion error troubleshooting! Join us to explore the potent capabilities of Visual Studio Code (VS Code) and ColdFusion Builder (CF Builder) in debugging. This hands-on session guides you through practical techniques tailored for local setups, ensuring a smooth and efficient development experience.
2. The IncQuery Group is an international team of engineering experts with a strong
research and development background. We support systems engineers in several
to create tailormade solutions. Automotive professionals, aircraft engineers, space
engineers all trust us to make their systems work exceptionally, safer, faster, and more
reliable.
Who We
Are
5. Digital Thread
More and more systems design scenarios comprise a high number of domains, also
displaying a remarkable diversity in their nature
Digital Threads
• Siloed multi-domain engineering data
• Digital tools and toolchains
• Lifecycle management
• Connections that bridge data across silos
Promises
• Increasing systems quality
• Reducing risks and chances of errors
• Reducing overall production costs
6. The impact of disconnected silos
Various isolated disciplines
Systems Engineering, Mechanical,
Electrical, ALM/PLM, …
As disconnected silos, what is the interface between
architecture and disciplines?
- It is often a document produced from a discipline specific tool
- Consequence: data reentry and/or copy-paste
- No guarantee of completeness, correctness and consistency
A lot of time and money is wasted!
No global consistency
Difficult customization
Data lock-in
Vendor lock-in
10. The 3C Challenge
Completeness
• Make sure all my components and functions exist both in SE and ECAD
Correctness
• If component A has is of type “PCB” (in SE)
it should be mapped to a PCB device (in ECAD)
Consistency
• If a connection transfers an item between components A and B (in SE)
there is a wire carrying the corresponding signal between devices A and B (in
ECAD)
Cable/Harness
Video Drone Model
What causes 3C problems?
• Input error
• Forgetting/missing something
• Copy/paste error
• Incorrect mappings
• Roundtripping gone bad
• Change
13. First-class citizen
What can links connect?
• Full documents
• Repositories/Large set of data
• Low-level elements/objects
Link
Link
Link
document document
E.g. linking between a serialized version of the video drone and
cable/harness models as files.
E.g. linking between given version of the video drone and
cable/harness models stored in the silos.
E.g. linking between components in a video drone model and wires in
a cable/harness model.
14. Linking between Silos
• Multiplicity
• 1-to-1, 1-to-many, many-to-many
Link
Link
Link
E.g. different links for each
component and wire, item
and signal.
E.g. one link for all wires
related to a component.
E.g. for each component
pairs and connections
between them, there is a
link to the relevant devices
and wires between them.
15. Linking between Silos
• Multiplicity
• 1-to-1, 1-to-many, many-to-many
• Recognize broken links
• Automated (Immediate/Scheduled), Manual
Link
Link
Link
E.g. a component is
deleted which had a
related wire.
16. Linking between Silos
• Multiplicity
• 1-to-1, 1-to-many, many-to-many
• Recognize broken links
• Automated (Immediate/Scheduled), Manual
• Managing Versions
• Supporting all versions, Supporting only published versions, Only latest revision
Link
Link’
Link’’
Device
Device'
Component
Component'
E.g. for each change a new
link is created.
17. Linking between Silos
• Multiplicity
• 1-to-1, 1-to-many, many-to-many
• Recognize broken links
• Automated (Immediate/Scheduled), Manual
• Managing Versions
• Supporting all versions, Supporting only published versions, Only latest revision
Link
Link’’
Manually publish new version
Device
Device'
Component
Component'
E.g. links are created when
it is triggered by a publish.
E.g. for each change a new
link is created.
18. Linking between Silos
• Multiplicity
• 1-to-1, 1-to-many, many-to-many
• Recognize broken links
• Automated (Immediate/Scheduled), Manual
• Managing Versions
• Supporting all versions, Supporting only published versions, Only latest revision
Link’’
Device'
Component'
E.g. no version information
is available.
E.g. links are created when
it is triggered by a publish.
E.g. for each change a new
link is created.
19. Managing links with data between
Silos
View data & links
- Data and links are presented for end-
users in a table/tree/diagram format
- Custom representation or existing tools
- Data and links are navigable
Querying data & links
- Simple/Complex filtering
- Relation/Graph based querying
- Full/text search
- Hybrid
20. Data visible from the Silos
No data replication from Silos
A wrapper is used for accessing the data inside Silos
Full data replication of data from Silos in native format
Data is stored in a native format (object blobs, files etc.)
Data Warehousing, Data Lake
Full data replication from Silos
All data is extracted from the Silos to provide a full access
to the data
Publishing a state of the data from Silos
Usually requires a manual step to publish the data
All data of a given snapshot of the data is accessible
Wrapper
blob,
files,
etc…
Access
Request
Manually publish new version
21. Mapping between Silos:
Rule based
Precondition (in SE):
there is connection that transfers an item
between components A and B
Action (in ECAD):
create a wire carrying the corresponding signal
between devices A and B
Silo Silo
Systems Engineering ECAD
Transformation
22. Automated creation of links
Handover automation:
Bridges capable of moving data, metadata, and documents between tools automatically. It
helps in replacing redundant and error-prone data re-entry with automated import-export
steps.
Requires customized transformation capabilities:
- Model to model, Model to text, Text to model
- Possibility to create custom rule definitions
- Diagram-based editor
- Text-based editor
24. Comparison Table*
Tool Linking Querying Transformation Data Storage
Syndeia™ - Intercax Generic links with
tool-specific
endpoints
Gremlin Rule-based
synchronization
No replication
(links only)
Smartfacts OSLC linking
support
Traceability
coverage queries
? (no information) No replication
SBE Vision Generic links with
tool-specific
endpoints
“Semantic search”
(elastic search)
? (no information) Full replication
(ontology based)
The Reuse company–
Eningeering
Studio
OSLC KM,
Interface modelling
Rule-based
validation
Rule-based
transformation
framework
Hybrid Replication
(latest version)
IncQuery Cloud Generic links based
on URLs
Elastic search,
SPARQL, VQL
Tool-specific
bridges
Full replication
(multiple)
*Based on data accessible from the websites of the given tools as of 2023 / 04 / 12
25. Addressing the 3C Analysis
case study with Zuken
E3.GENESYS and IncQuery
26. Our take
• Our take
Discipline-specific, automated
bridge tools that create the
digital thread
Overlaid layer of digital thread
analytics that can expose parts
of the digital thread depending
on the need/use-case
Vendor-neutral federated tool
integration
• Single source of truth is NOT a single model
it is the ”model of models”
• Digital thread analytics can
• look at links AND look into models
• Semantically analyze both
• Holistic adaptable to all tools in the
toolchain
29. A new platform for digital engineering
automation.
• Creates a unified, searchable, and
analyzable representation of your complete
digital thread: the knowledge graph
• Automated Quality Gates: detailed
validation reports and analysis dashboards
that integrate seamlessly with modern, web-
based tools
• Handover Automation: light-weight bridge
tools that eliminate copy-paste and date re-
entry
• Powered by digital thread analytics:
queries and mapping rules that can
seamlessly cross tool (silo) boundaries
IncQuery Suite
DESKTOP
VALIDATOR
CLOUD
30. Main features
- Works with popular tools like Enterprise Architect and MagicDraw/Cameo out
of the box.
- Runs as a standalone application or as part of a DevOps pipeline
- Provides a convenient extension framework to define custom validation
rules for models, which we rely on for the GENESYS adaptation
- Supports centrally-shared / version-managed projects, by integrating with
Teamwork Cloud, or file-based VCSs such as Git/SVN.
Devops-ready automated quality gate, providing detailed model
quality reports, based on standard and custom rules.
- Helps Systems Engineers to assess key quality-related metrics of their work,
independently of what authoring environment they work in.
- Helps downstream stakeholders (e.g. QA Engineers, Software Architects,
Electrical Engineers, …) to automatically assess the quality of an inbound
systems architecture model, based on rules such as the library provided by the
SAIC Digital Engineering Validation Tool, or 3C analysis.
IncQuery Validator
32. Validation report for 3C Analysis
• Results after initial import performed with
GENESYS.E3 Connector
• Partially complete
(Subsystem mapping is
disabled by default)
• Inconsistent signal allocations
“If a connection transfers an item between components A and B (in SE)
there is a wire carrying the corresponding signal between
devices A and B (in ECAD)”
34. Validation Report for 3C
Analysis
• Re-run the validation
• Result: Allocation problems resolved
35. Progress tracking
• Historical analytics as the “progress bar” of a complex engineering process
• Model Integrator / Reviewer can follow the “Transition to Detailed Design” process on a version
control dashboard
• Track progress via KPIs as the mapping completeness is improved
• Identify and fix correctness issues quickly
35
36. Takeaway
• Creating the Digital thread requires a lot of
underlying methodologies and technologies to work in harmony
• There is no single golden solution
• Define your requirements carefully
• Consistency, completeness, consistency analysis
• Version control
• Link management
• Handover automation
• Access control
• Model validation
• Etc.
Be open to share your successes and failures
Bridge can address
Correctness
Completeness (to a certain degree)
Consistency – not really, as there are several additional and manual steps to be made by the electrical engineer that are specific to the ECAD domain and cannot be automated.
“Single Source of Truth” in reality is not a single model, it’s the “model of models”
Therefore, to ensure that consistency can be checked and maintained throughout the entire digital thread, We need an additional solution that
Can look at links between models and can look into models
Analyze both in a semantically meaningful way
Is holistic in terms of the complete digital thread, i.e. adaptable to other tools as well
E.g. ALM/PLM
Now let’s look at how we can build an analytics dashboard for the 3C validation challenge of the “transition to detailed design” scenario as Enrique has introduced earlier.
Numerical charts,
Tables,
Hypertext,
Web components
Etc
In fact, the table shown here contains hyperlinks which navigate directly into the respective tools, in this case GENESYS or E3.series, so that the electrical engineer can fix problems quickly.
All organized into interactive documents which can be exported to standard formats such as PDF or published into platforms such as Confluence.
After realizing the issues that need to be address, in Step 2, the electrical engineer will proceed to create a wiring diagram and add signal carriage information to their design.
In the final, third step of our demonstration sequence, the electrical engineer then uses the IQ MA again to validate that indeed, as a result of their actions, the number of inconsistencies reported has decreased.
Going further, and looking at the whole scope of the transition process, this 3C Analyis dashboard can be enriched with historic capabilities which enables the electrical engineer or a model reviewer to keep track of the progress and accurately assess the remaining time needed to complete the transition process. In other words, our dashboard can act as a progress bar of a very complex engineering process, showing not just the percentage of correctly mapped model elements, but also when errors have been introduced and fixed. By the way, all of these charts can be exported into Excel, together with the underlying data, at a click of a button.