Modeling Physical Systems in the Metaverse Easily with Graphs
•Download as PPTX, PDF•
0 likes•103 views
A company called Arcurve helps clients analyze physical systems by modeling them as graphs in Neo4j. As an example, they modeled a high school's HVAC system from its BIM data to assess risk, trace contaminants, and visualize inspections. Arcurve translated the building data into a graph database, ran graph algorithms like centrality measures to find hot spots, and visualized the analyses and assessments back in an interactive 3D model of the building. Modeling physical systems as graphs in this way opens opportunities for virtual inspections, simulations, and intuitive understanding of complex real-world systems.
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Modeling Physical Systems in the Metaverse Easily with Graphs
Similar to Modeling Physical Systems in the Metaverse Easily with Graphs (20)
More from Neo4j
More from Neo4j (20)
Recently uploaded
Recently uploaded (20)
Modeling Physical Systems in the Metaverse Easily with Graphs
- 1. © 2022 Neo4j, Inc. All rights reserved. © 2022 Neo4j, Inc. All rights reserved. Modeling Physical Systems in the Metaverse Easily with Graphs • Mike Morley, Director AI/ML Technology, Arcurve • Peter Tunkis, Lead Data Scientist, Arcurve
- 2. © 2022 Neo4j, Inc. All rights reserved. © 2022 Neo4j, Inc. All rights reserved. 2 Introduction Real World Analytics Environment Mining Energy AEC Facilities Your technology partner... from strategy to execution • We are surrounded by data • How can we leverage these data? • Communicate and use the right tool for the job • Today we will demonstrate how Arcurve takes physical systems, models them in graph, and takes it all to the next level to bring out value for our clients
- 3. © 2022 Neo4j, Inc. All rights reserved. 3 About Arcurve Execution Strategy Digital Transformation Prepare, Plan & Execute AI & Data Analytics Software Development Advisory Services Software & Vendor Selection System Automation & Design Process Automation & Design Software Design Custom Development Delivery Operations Enablement Strategic Planning Business Analysis Product Management
- 4. © 2022 Neo4j, Inc. All rights reserved. © 2022 Neo4j, Inc. All rights reserved. 4 The challenge • Create an interactive application to solve practical business problems • Fictional client proposal: School Board wants to inspect/analyze HVAC in a High School ◦ Assess risk ◦ Trace contaminants ◦ Intuitively visualize
- 5. © 2022 Neo4j, Inc. All rights reserved. 5 The Solution • Why be satisfied by being told the what when we can instead show both what and where? High school Design Model • BIM data stored/modeled in REVIT • Data wrangling reveals enough meta-data to get us started (spaces, ducts, HVAC equipment, etc.) • Use neo4j to model the building as a graph • Facilitate analytics with exploratory queries, graph algorithms Translate Building/HVAC to Graph DB Visualize building/HVAC & analytics • Bring the graph back to a 'digital twin' of the building/HVAC • Visualize graph analytics interactively and intuitively in the 3D model Intuitive Interactive Actionable
- 6. © 2022 Neo4j, Inc. All rights reserved. 6 Thanks to... • Will Reynolds, whose expertise of REVIT and neo4j we leveraged • The Arcurve Team ◦ Unity and C#: Colton Osterlund & Andrew Koenig ◦ Neo4J and graph theory: Eric Nosal ◦ Business concepts, messaging and design: Dani Finch, Jason Hamm, Michael Smith
- 7. © 2022 Neo4j, Inc. All rights reserved. 7 Bringing the Building into Graph From REVIT to Neo4J
- 8. © 2022 Neo4j, Inc. All rights reserved. 8 Exploring the Building in Graph How Does Graph Address the Challenges?
- 9. © 2022 Neo4j, Inc. All rights reserved. 9 How Does Graph Address the Challenges? • Discover and traverse the graph = “walking the ducts”, virtual inspection • GDS: Centrality (Weighted Degree) ◦ Discover and monitor ‘hot spots’ ◦ Recommendations for maintenance and applying filtration • GDS: Pathfinding (Weighted Spanning Trees) ◦ Simulate where COVID may go based on where a first-cough happens ◦ Simulate where COVID may have come from if (non-origin) detected • Results in neo4j give us the meat and potatoes—can we step it up a notch?
- 10. © 2022 Neo4j, Inc. All rights reserved. 10 Bringing the Graph into the Building Intuitive, Interactive, Actionable 3D Visualizations
- 11. © 2022 Neo4j, Inc. All rights reserved. © 2022 Neo4j, Inc. All rights reserved. 11 The possibilities are endless... Summary • Include 'NPCs' to simulate hypothetical persons' behaviour • Enhance Unity Model to be fully real-time/interactive (AR/VR) • Integrate and implement supervised/predictive modeling for incidents • Leverage a fuller extent of the data in the graph (properties galore!) • Using graphDB technology to model physical systems opens a wealth of opportunity to expand path-to-value • Client wanted to assess and analyze HVAC in a High School ◦ Translate the building into a graph database ◦ Conduct assessment and analyses using powerful tools in neo4j GDS ◦ Visualize assessments and analyses in an intuitive 3D representation of the building Physical Systems + Graph = Wealth of Opportunities
- 12. © 2022 Neo4j, Inc. All rights reserved. 12 Graph Is the Central Hub • Neo4j is the master data management system that links all the modelling systems together into a unified model ◦ Engineering design ◦ Machine learning for prediction ◦ Computational models such as CONTAM for simulation ◦ Unity or equivalent analytics output for visualizing and interpreting results
- 13. © 2022 Neo4j, Inc. All rights reserved. © 2022 Neo4j, Inc. All rights reserved. 13 Thank you! Contact us at info@arcurve.com
Editor's Notes
- More and more we are all coming to realize that everything around us is data—it’s up to us to learn and determine how to leverage these data. The growing capability in the graph data base and analytics technology space means that we have more opportunities than ever to harness the data around us, but also ever-increasing challenges to avoid the pitfalls of Maslow’s Hammer When we take on a new project, we make sure to sit down with stakeholders to ensure we can achieve the goals of the project: Addressing not just what’s wanted, but what’s needed—this helps ensure that we are using the right tools for the job All this to say that we’ve seen a steady increase in clients interested in leveraging the physical data around us—from those in telecommunications and energy industries to logistics and even human resources!
- We essentially approach workflows holistically, but comprehensively across areas relevant to any project: Software and vendor selection System automation and design App development Business Analysis Product Management Sustainment While we don’t have the liberty of openly discussing the specifics of the work in these partnerships, we would like to demonstrate a project, couched in our general workflow, in which we take a complex problem with which many of us are familiar, leverage graph technology to facilitate intuitive analyses by modeling a physical system and taking all of this to the next level with cutting edge, intuitive visualization
- One question that we were interested in exploring was if covid would spread through ventilation systems. So we thought it might make a good basis for a demo of graph + visualization. This also helps illustrated work we are doing on actual projects . The challenge of this demonstration on its face is concrete: A hypothetical client representing a school board came to us with a request to help assess risk in the HVAC system of on of their high schools. Ensuring HVAC is in top shape is important at any time, but as we know, this profile has been raised thanks to COVID This leads to a couple of questions/asks: How can we proactively address possible risks in our HVAC with respect to the spread of contaminants like COVID? Where are the riskiest chokepoints or junctions from which COVID could spread easily? If we do experience contamination, is it possible to determine the source of any contaminants? Or if we know the source, can we determine the extent of possible spread and contamination? Is it possible to visualize, or ‘inspect’ our HVAC and any analyses we do on it in an intuitive way to help speed up reactions?
- Following consultation with the stakeholders to define the scope: both the challenges to address and the availability of data to do so, we planned how to accomplish this task: The board had technical specs and a design model of the building, including HVAC, in REVIT Utilizing graph database technology provides an intuitive way to model complex physical systems featuring lots of interconnectivity and/or dependencies: Buildings/architecture Transportation and logistics Energy and natural resource systems like pipelines or processing facilities The list goes on! Finally, to facilitate and expedite data-driven decision-making, we opted to bring together the physical model and analytics together in UNITY: a widely-used 3D modeling platform Why 3D model? Communicating results is just as—if not often more—important than the analytics or data science that power any deliverables Visualizing findings in the abstract using tables, bar graphs, pie charts and the like is minimally viable, but why not make it truly intuitive? Why not visualize the graph and any analytics embedded in our 3D model—that is, visualize our analysis in the building itself, as if we were conducting virtual inspections? Why be satisfied by being told the what when we can instead show both what and where?
- Before we go on…a few words of thanks
- First step is to bring the REVIT model into Graph MIMO TO LEAD DEMO PART 1
- This slide begins Demo part 2 (stuff in neo4j) PETE TO LEAD DEMO PART 2
- With the model in graph, we can traverse the HVAC system in graph as tough we were “walking the ducts” from rooms or spaces to terminals to ducts, equipment, duct transitions [PETE to RUN ERIC’s DEMO QUERY] We can leverage GDS to assess risky hotspots—either places that could be spreader sources or chokepoints Recommend monitoring, upgrading components and/or filtration in these areas Similar to how we might try to find bottlenecks when dealing with transportation/logistics We can further leverage GDS to conduct probabilistic contact-tracing, either forward or backward Proactively simulate forward tracing – you know where COVID-cough took place Reactively simulate backward tracing – you know where COVID appeared, but not where it originated [PETE to DEMO IN NEO4J BROWSER FOR GDS, run through queries/algo results]
- As we can see, tables and graphy visualizations take us a good lot of the way there, but how can we make our traversals and analyses more intuitive, and expedite the path-to-value in decision-making processes? Which is easier: trying to find something that showed up in a list/table, or getting shown exactly where that something is on a scaled map/visual representation? MIMO TO LEAD DEMO PART 3
- Information management throughout the organization: What needs to be seen: when, where, why, and how?