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Artikel von Florian Huemer
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Florian Huemer hat dies geteiltImagine building an exact 1:1 copy of your house in Minecraft. Then you wire up real-world sensors so that when you open your actual bedroom window, the window in your game opens at the exact same time. That is a Digital Twin. But there is a huge technical roadblock when you try to actually build it. Your physical sensors do not speak the same language as your digital world. When your window sensor sends data, it just blasts out a raw stream of numbers (like "Modbus Register 256"). Your software has no idea what "256" means. It just sees random data junk. It doesn't know if that number represents a temperature, a light switch, or a window. Clever developers fix this by giving the system a semantic brain. Instead of letting the computer guess what the data means... ...you use a special framework called the SOSA/SSN ONTOLGY. It acts like a real-time translator for your code: 1. Raw Data: The system takes the basic JSON config file (the raw, unstructured data your sensor spits out). 2. Translation: It maps that dumb data to a specific tag called a sosa:Procedure. 3. Smart Object: Suddenly, your computer stops seeing "Register 256" and understands... "Oh, this is a Window1Sensor observing if Window1 is open!" By mapping the data this way, your Digital Twin isn't just a basic 3D graphic anymore. It actually understands the physical world, the objects in it, and how they all connect. You can then run code (using SPARQL) to ask your virtual world complex questions about the REAL world in real-time. ---------- Follow me for #digitaltwins Links in my profile Florian Huemer
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Florian Huemer hat dies geteiltThe entire world is distracted by LLMs. But the biggest race in tech right now is happening quietly in the back. Giving AI a functional map to the physical world! You are leaving your AI completely blind, if you build without ontology. Dumping IoT metrics, CAD models, and ERP records into a massive data lake doesn’t help. It creates an expensive digital graveyard. When you stack Operational Systems (IoT, WMS) next to Spatial Systems (GIS) without a semantic map, your AI cannot contextualize your assets. It just sees raw numbers. The missing architectural layer isn't more compute power. It’s Ontology. If you want true AI-ready asset infrastructure, you must map relationships. It is the only way to convert raw inputs into actual AI understanding. Instead of siloed databases, place your Asset at the exact center of your architecture. Give it a unique ID, and make your data orbit it. Don't just store a spec sheet in a folder, link Attributes directly to the asset node. Don't just save a 3D model, directly link to the physical counterpart. When your data is structured through this ontological lens, your stack comes alive. Your AI can traverse the graph, query a drawing management system, and instantly grasp the context. This is when Predictive Maintenance and Contextualized Decision Making actually work. The AI finally understands what is breaking, where it sits, and how it impacts the pipeline. Let's troubleshoot your tech stack in the comments🖐️ -------- Follow me for #digitaltwins Links in my profile Florian Huemer
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Florian Huemer hat dies geteiltYour blueprint for making your Digital Twin data AI-ready. 👇 Everyone wants predictive analytics and automated optimizations. But AI is only as smart as the data it’s fed. If you are building a DT, your priority isn't rendering - it's data engineering. Here is the data workflow required to build AI-ready twins: Phase 1: Required data is sourced from relevant systems for analysis. Critical and meaningful data is preserved for processing. Phase 2: Cross-references data from different sectors into a unified model to build relationships. Phase 3: Removes data repetition, optimizing storage and improving data quality, while also filtering and organizing massive datasets by removing noise and unwanted entries. Phase 4: Adjusts and prepares data for use across varying sector requirements so your AI models can actually consume it. AI readiness isn't an accident. It's an engineered outcome. ------ Follow me for #digitaltwins Links in my profile Florian Huemer
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Florian Huemer hat dies geteiltEveryone wants "AI-driven maintenance" for their assets (Layer 5). Almost nobody actually has the architecture to make it work (Layers 1-4). You cannot "install" TensorFlow (Layer 5) on a raw piece of machinery. And expect it to magically predict failures. You are missing the bridge. The true value of a Digital Twin is not the AI. It is the integration stack that feeds it. To build a DT that actually works, you have to reverse-engineer: The Goal (Layer 5): Anomaly detection via TensorFlow or PyTorch. How do you get there? ���� Contextual Memory (Layer 4): Fusing multi-source data via APIs and storage like AWS DynamoDB. 👉 Digital Twin Model (Layer 3): Spatial context (Autodesk/Rhinoceros) that the sensor data maps to. 👉 The Nervous System (Layer 2): Lightweight, reliable data transmission. This is where Apache Kafka and Mosquitto is used. 👉 The Senses (Layer 1): Node-RED managing the raw IoT streams. Stop thinking about DT as visuals. Start thinking about them only as DATA PIPES. --------- Follow me for #digitaltwins Links in my profile Florian Huemer
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Florian Huemer hat dies geteiltLook at the BIM, GIS, LiDAR, IoT, ERP, and MEP. They are fundamentally disparate data types: 1️⃣relational asset databases (ERP) 2️⃣raster maps (GIS) 3️⃣massive vector geometry files (BIM) 4️⃣point cloud data (LiDAR) 5️⃣and dynamic event streams (IoT/MEP) The API-to-API approach fails because it doesn't solve the semantic mismatch. We need more than connective tissue. We need a single, unified operational brain, which is a DATA FUSION ENGINE. Let me know your thoughts in the comments.... ------ Follow me for #digitaltwins Links in my profile Florian Huemer
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Florian Huemer hat dies geteiltWe spent some hours analyzing why firms successfully launch Digital Twins while others waste millions on failed pilot programs. The secret isn't better sensors, and it isn't a higher budget. It’s better system architecture. Period. This networkgraph 👇 illustrates exactly why. DTs are not a single platform - it’s an ecosystem of 5 distinct, overlapping tech nodes. Here is what happens when you ignore the map: 1️⃣ Ignore the Purple Node (Supply Chain): Your project management software (Blue Node) ends up operating on delayed, inaccurate material data. 2️⃣Skip the Yellow Node (Visualization): Stakeholder buy-in plummets because non-technical executives can't understand the raw data. 3️⃣Disconnect the Red Node (Sustainability): You lose out on the long-term ROI of lifecycle energy efficiency tracking. Implementing a Digital Twin requires aggressive systems-thinking. If you are leading a digital transformation initiative, this network graph is your blueprint. Zoom in, study the intersections, and see where your current architecture falls short. Found a missing link in your own systems? 🚀 --------- Follow me for #digitaltwins Links in my profile Florian Huemer
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Florian Huemer hat dies geteiltDo not write a single line of code for your DT until your standards are locked in 🖐️ Integrating Deep GIS with building information and real-time IoT is notoriously difficult. If you try to force-fit proprietary formats together, your system becomes unmaintainable. Check the architecture cheat sheet below 👇 This is your non-negotiable stack: CityJSON & CityGML SensorThings API IFC & BIM Notice the synergy. You pair IFC for the heavy BIM, use CityJSON for a simplified, web format, and tie it with GIS IO standards to ensure data exchange is frictionless. Your DT needs to integrate with new tech 5 or 10 years down the line. Open standards are my silent heroes! Are you currently using OGC standards in your stack? ----- Follow me for #digitaltwins Links in my profile Florian Huemer
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Florian Huemer hat dies geteiltMost technical teams are architecting their digital twins entirely wrong. They focus 100% on maintenance and operational dashboards... ...completely ignoring the largest emerging revenue stream of the decade. The carbon market is projected to skyrocket from $2.5B in 2024 to $30.0B by 2030. If you are building a digital twin today and aren't engineering it to track, verify, and tokenize carbon emissions, you are leaving massive ROI on the table. Carbon credits require rigorous, undeniable MRV (Monitoring, Reporting, and Verification). Manual spreadsheets and yearly audits won't cut it anymore. You are already mapping physical assets to digital data streams. The telemetry is there. The sensors are live. The solution? Build carbon tracking into your data pipelines from day one. When you design your twin's architecture, include immutable ledgers and real-time emission APIs. Turn your physical-to-digital mapping into verifiable sustainability assets. Stop building twins just for dashboards. Build them for the carbon economy. --------- Follow me for #digitaltwins Links in my profile Florian Huemer
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Florian Huemer hat dies geteilt6 architectural flaws that will break your Digital Twin deployment 💥 I've looked under the hood of dozens of failed implementations. The code is usually fine. The deployment strategy is what breaks the system. Here are the most common mistakes engineering teams make again and again: Buying software before defining objectives. Starting with visualization. Ignoring metadata. Ignoring interoperability. Building isolated digital twins. No governance. Your 3D model is just the very tip of the iceberg. The real engineering happens in the data pipelines, API gateways, and semantic modeling. --------- Follow me for #digitaltwins Links in my profile Florian Huemer
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Florian Huemer gefällt dasImagine building an exact 1:1 copy of your house in Minecraft. Then you wire up real-world sensors so that when you open your actual bedroom window, the window in your game opens at the exact same time. That is a Digital Twin. But there is a huge technical roadblock when you try to actually build it. Your physical sensors do not speak the same language as your digital world. When your window sensor sends data, it just blasts out a raw stream of numbers (like "Modbus Register 256"). Your software has no idea what "256" means. It just sees random data junk. It doesn't know if that number represents a temperature, a light switch, or a window. Clever developers fix this by giving the system a semantic brain. Instead of letting the computer guess what the data means... ...you use a special framework called the SOSA/SSN ONTOLGY. It acts like a real-time translator for your code: 1. Raw Data: The system takes the basic JSON config file (the raw, unstructured data your sensor spits out). 2. Translation: It maps that dumb data to a specific tag called a sosa:Procedure. 3. Smart Object: Suddenly, your computer stops seeing "Register 256" and understands... "Oh, this is a Window1Sensor observing if Window1 is open!" By mapping the data this way, your Digital Twin isn't just a basic 3D graphic anymore. It actually understands the physical world, the objects in it, and how they all connect. You can then run code (using SPARQL) to ask your virtual world complex questions about the REAL world in real-time. ---------- Follow me for #digitaltwins Links in my profile Florian Huemer
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Florian Huemer gefällt dasThe entire world is distracted by LLMs. But the biggest race in tech right now is happening quietly in the back. Giving AI a functional map to the physical world! You are leaving your AI completely blind, if you build without ontology. Dumping IoT metrics, CAD models, and ERP records into a massive data lake doesn’t help. It creates an expensive digital graveyard. When you stack Operational Systems (IoT, WMS) next to Spatial Systems (GIS) without a semantic map, your AI cannot contextualize your assets. It just sees raw numbers. The missing architectural layer isn't more compute power. It’s Ontology. If you want true AI-ready asset infrastructure, you must map relationships. It is the only way to convert raw inputs into actual AI understanding. Instead of siloed databases, place your Asset at the exact center of your architecture. Give it a unique ID, and make your data orbit it. Don't just store a spec sheet in a folder, link Attributes directly to the asset node. Don't just save a 3D model, directly link to the physical counterpart. When your data is structured through this ontological lens, your stack comes alive. Your AI can traverse the graph, query a drawing management system, and instantly grasp the context. This is when Predictive Maintenance and Contextualized Decision Making actually work. The AI finally understands what is breaking, where it sits, and how it impacts the pipeline. Let's troubleshoot your tech stack in the comments🖐️ -------- Follow me for #digitaltwins Links in my profile Florian Huemer
Berufserfahrung und Ausbildung
Projekte
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City Digital Twin - Ryiadh, Saudi Arabia
Envisioning the next generation of smart cities, the creation of a data-rich, highly visualized virtual digital twin of the capital represents a transformative step. By layering critical data streams, such as infrastructure networks, live traffic analytics, and Internet of Things (IoT) feeds within a unified digital environment, we unlock powerful tools for analysis, simulation, and strategic decision-making that will define the future.
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AL'MA HOME
The future of sustainable urban development increasingly focuses on the built environment, leveraging a data-driven, highly visualized virtual digital twin managed through a central command center. This system integrates critical Environmental data layers specific to the construction and building lifecycle – tracking metrics such as embodied carbon, construction waste volumes, sustainable material sourcing, operational energy consumption, and building water efficiency. These inputs feed…
The future of sustainable urban development increasingly focuses on the built environment, leveraging a data-driven, highly visualized virtual digital twin managed through a central command center. This system integrates critical Environmental data layers specific to the construction and building lifecycle – tracking metrics such as embodied carbon, construction waste volumes, sustainable material sourcing, operational energy consumption, and building water efficiency. These inputs feed specialized dashboards within the command center, delivering actionable, real-time insights. This empowers stakeholders to optimize sustainable construction practices, monitor real-time building performance against efficiency targets, and strategically manage the city's building stock for minimized environmental footprint and enhanced resilience.
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Digital Twin Command Center - Project Level
The future of intelligent building management is realized through a dedicated command center powered by a data-driven, highly visualized digital twin of the individual building. This system integrates diverse in-building sensor data and system feeds – monitoring parameters like granular energy usage per zone/system, HVAC performance metrics, lighting levels, real-time occupancy data, water consumption points, indoor air quality (IAQ), and equipment status. Dynamic dashboards within the command…
The future of intelligent building management is realized through a dedicated command center powered by a data-driven, highly visualized digital twin of the individual building. This system integrates diverse in-building sensor data and system feeds – monitoring parameters like granular energy usage per zone/system, HVAC performance metrics, lighting levels, real-time occupancy data, water consumption points, indoor air quality (IAQ), and equipment status. Dynamic dashboards within the command center translate this complex data into actionable, real-time insights. This empowers facility managers and operators to proactively optimize building systems for peak efficiency, minimize energy and resource consumption, enhance occupant comfort and well-being, predict maintenance requirements before failures occur, and ensure the operational resilience, safety, and security of that specific building asset.
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Martin Blaschka
Seit gestern kreist der Entwurf zum #Digitalgesetz #GeDIG 📑: das "Gesetz für Daten und digitale Innovation im Gesundheitswesen". Die meisten Punkte sind zwar wenig überraschend, doch die Leitlinien des BMG werden immer klarer: Die Rolle der #gematik soll gestärkt werden, hin zum aktiven "Provider": Komponenten, Dienste und Anwendungen der #Telematikinfrastruktur #TI sollten zukünftig zentral ausgeschrieben, gebündelt und betrieben werden können. Dazu kommen weitreichende Durchsetzungsbefugnisse, auch gegenüber Herstellern und Betreibern von Softwaresystemen auf der TI. Die #ePA wird weiterentwickelt. Apotheken sollen erweiterte Zugriffsrechte erhalten, Krankenkassen dürfen so genannte "Mehrwertanwendungen" in der ePA einführen. Über die ePA-App soll ein direkter digitaler Einstieg in die ambulante Versorgung über #Ersteinschätzung und #Terminbuchung möglich werden. Zusätzlich sollen den Krankenkassen die Nutzung von ePA-Daten für Programme nach § 25b SGB V ermöglicht werden. Die #EHDS-Verordnung der EU wird in nationales Recht umgesetzt. Der Anschluss an MyHealth@EU wird ab 2029 verpflichtend. Deutschland bekommt damit den notwendigen gesetzlichen Rahmen für den #EU-weiten Gesundheitsdatenraum. #Interoperabilität wird weitreichender verpflichtet: Für Leistungserbringer und Systemhersteller gleichermaßen. Patientendaten müssen künftig in interoperablen Formaten vorgehalten werden, Hersteller von Praxissoftware müssen das technisch unterstützen. u.v.m. Viele der Themen stehen natürlich auch im Herbst auf der Agenda unseres 2. TI-SUMMIT in #Leipzig am 7.-8. Oktober 2026 - von der Rolle der gematik über die Weiterentwicklung der TI (2.0) bis hin zu ePA, E-Rezept, KIM & TIM, PoPP, EHDS, Forschungsdaten... bis dahin wird das Gesetz sicher schon weiter gediehen sein, und sich ggf. bereits seinem Inkrafttreten nähern. Es wird spannend! 🙏 #DigitalHealth #ehealth #gesundheitsministerium #TISUMMIT26 #TISUMMIT
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