BOCEMON

BOCEMON is an imec.ICON project, consisting of three industry partners (Quares, DYAMAND and Daikin) and various IDLab teams (PreDICT, KNoWS, HomeLab, Antwerp) within the building management domain. It's a 2-year project that will start 1/10/2024. Project aim: To convert office buildings into truly sustainable, comfortable, healthy and flexible spaces extra services are needed to help building and facility managers to decide on building upgrade investments, optimally allocate office spaces and dynamically control HVAC systems. Such services require access to a broad range of data sources (HVAC data, user feedback, BIM models, etc.) via easy accessible interfaces, but current deployment requires a lot of (manual) work as data is often distributed over many systems, using different protocols and data models. There is a clear need to extend the interoperability capabilities between stakeholders and service providers involved in the building lifecycle, to enable a rapid scaling to many buildings. BOCEMON will develop tools to allow service providers to prototype, test and deploy their services much faster. By using a semantic data model a wide range of building services will be supported and data exchange with diverse stakeholders will be improved. Accompanying tools will be designed by IDLab.KNoWS for easy service design, to explore the available data in the semantic data model, and by IDLab.PreDICT to automatically visualize sensor data on floor plans. IDLab.KNoWS will collaborate with DYAMAND to design an automatic onboarding service to improve the discovery and mapping of sensors and actuators onto this domain model. Hybrid AI based transfer learning methodologies will be designed by IDLab. PreDICT and will allow the efficient transfer of ML models (e.g. to derive trends & patterns on user behavior, occupancy, building health state, etc.) between different building contexts. AI based algorithms will be designed to support investment decisions to realize sustainable and comfortable buildings, and realize a fine-grained occupant-centric and interoperable HVAC control.

AI

ERA-KG

Bilateral collaboration with the European Agency for Railways (ERA) to create and evolve a semantic interoperability layer that integrates the different base registries maintained by the agency.

Knowledge Graph - Railway Infrastructure

FRACTION

Today, centralized stores govern the storage and processing of Big Data. Due to regulation (e.g. GDPR) and increasing awareness of people about data sensitivity, a paradigm shift towards decentralization is imminent. This allows people to control their own personal data, by guarding all public and private data they or others create about them in a vault, and selectively granting access to people and organizations of their choice. Future uses of Big Data are thus bound to shift from a small number of large datasets to a large number of small datasets. As such, the fundamental assumptions on which the current approaches are built to deal with the characteristics of Big Data (Volume, Variety & Velocity) are no longer valid, i.e. volume cannot be tackled by centralising data in a single location, high velocity data streams cannot be sent to a centralised data center and the variety problem cannot be resolved by imposing a single data format. FRACTION supports the shift to a decentralized approach by leveraging Semantic Web technologies. It will investigate 1. algorithms that autonomously distribute the analytics across the decentralized network, while hiding its complexity to the user, 2. decentralized and user-friendly data access control policies, and 3. methods to exploit the heterogeneity of the decentralized network to improve scalability and performance of the analytics.

Decentralization - Decentralized scheduling of heterogeneous resources - Semantic reasoning and querying

MareGraph

MareGraph will build a data architecture for cost-efficient, flexible and sustainable data sharing for a Marine Knowledge Graph. In this project we collaborate with the Flemish Marine Institute (VLIZ), Digital Flanders and the Italian National Research Council (CNR).

Knowledge Graph - LDES - Marine Biology

Onto-DESIDE

The Onto-DESIDE (Ontology-based Decentralized Sharing of Industry Data in the European Circular Economy) will address the main challenges facing industry that prevent the efficient sharing of data and making it understandable and usable by humans and machines. The project targets four core project outcomes: - A shared vocabulary in the form of network of ontologies. - An open circularity platform, i.e. a secure and privacy-preserving decentralised data sharing platform using RML, Solid, Comunica, Verifiable Credential, and blockchain technologies. - Methods to find, analyse and assess the new circular value chain configurations. - Validation– demonstrating and quantifying the potential for increased retainment of value.

Blockchain - Comunica - RML - Solid - VC

OSLO

The Flemish government wants to optimize its services and make the exchange of information smoother. But government services to citizens and entrepreneurs are supported by specialized applications from various software suppliers. That is why there is a need for an unambiguous standard: OSLO (Open Standards for Linking Organizations).

Linked Data

PACSOI

PACSOI is a project focused on enabling Solid for healthcare. It’s a two-year project started 1/06/2024. Project aim: Healthcare ecosystems are siloed which makes it difficult to integrate personal health data with clinical data, access and jointly analyze it to generate meaningful insights. These silos are even more challenging for the increasingly common remote monitoring & digital therapeutics tools that gather real-world data & insights. This creates a barrier to leverage these data to achieve a holistic & longitudinal view on the patient’s condition. This is especially troubling for patients with chronic or complex conditions who receive care from multiple providers across settings. Currently, the ownership, control, transparency & consent of health data use & insight generation are outlined in data processing agreements between patients, care providers & third-party companies. This is a complex & time-consuming process, lacking patient involvement in data control decisions, which raises ethical concerns.

Solid

Serendipity Engine

We increasingly rely on algorithmically generated recommendations to navigate in both online and offline contexts: listening to music on streaming platforms, reading news online, or following recommendations about activities and events in your favorite city. These recommender systems help us dealing with the abundance of available information, but at the same time raise questions about their impact on individual citizens and society. The Serendipity Engine project sets out to address these challenges and support societal stakeholders in designing recommender systems to foster serendipity in public contexts.

Data discovery - Link Traversal - Serendipity

SHARCS

Data pods, for example based on the emerging Solid technology, securely store personal data and allow n-to-m data sharing between individuals and apps/third parties. However, there are still quite some research challenges remaining. These relate to which data are shared and with whom they are shared, but also to the validity of the data that are shared. The SHARCS project aims to solve a number of these challenges for the case of secure and selective sharing of accredited personal data.

HR - RDF - Solid - Verifiable Credentials - WebID

Solid4Media

With the active involvement of a representative group of Flemish media users, this living lab investigates the potential of Solid technology in a real media context. The goal is to migrate users' media profiles to a pod infrastructure and provide existing and new media services from there. The project proposal focuses on three important use cases identified by Flemish media stakeholders: (i) personal recommendations, (ii) trust through transparency and consent control, and (iii) targeted advertisements. By practically realizing the Solid infrastructure, the technical feasibility and scalability of a future-oriented decentralized media infrastructure is investigated and the concrete added value this can offer for existing and new media services is examined, both from a business and end-user perspective.

Media - Solid

Sytadel

Sytadel will provide technical data spaces architecture and design principles for actors who want to participate in the logistics data spaces in the future, together with open-source components and/or open software implementation guidelines.

Data Space - Logistics - Synchromodality

TRIPLE

The TRIPLE project, a collaborative effort between the SIB Swiss Institute of Bioinformatics, the University of Ghent and the IOCB Prague, aims to boost the (re)usability of existing knowledge graph resources and improve software tools for RDF data access, documentation and data model visualization. In addition, TRIPLE will increase interoperability between existing public SPARQL endpoints and private data stored in Solid Pods, thus creating an ecosystem of research data that can be seamlessly integrated through efficient and expressive federated SPARQL queries.

Open Research Data - RDF - Solid Pods - federated SPARQL

AI4FoodLogistics

Retail, and the food sector in particular, is a complex environment that requires close cooperation between multiple partners. At the same time, the sector continuously strives to increase efficiency and sustainability, e.g., by reducing waste. However, although all organizations within the supply chain are connected, data often remains stored in silos within each separate company. Within AI4FoodLogistics, we are exploring a federated data ecosystem that brings together data from different stakeholders in the retail ecosystem. The resulting insights will enable optimized forecasting and the development of intelligent algorithms.

Comunica - RML

AORTA

AORTA investigated ways hospitals can achieve efficiencies through the dynamic allocation of tasks and resources to logistics and nursing staff. The project seeks to support the different logistics processes and integrate them with new systems that continuously adapt to a hospital’s context. AORTA hopes this will, among other things, help reduce waiting times for patients, relieve nursing staff of non care related tasks and make sure consumables are stocked more efficiently. As such, AORTA designed a context-aware system to accurately get a view on the current context of the hospital and uses this information to dynamically assign transports to staff. By using AI, AORTA also investigates the reasons why transport delays occur and uses these to further optimize the allocation process. These delays can also be reported to management for follow-up. The AORTA consortium consisted of Televic Healthcare, Xperthis, AZ Maria Middelares, Ziekenhuis Netwerk Antwerpen (ZNA), Mintlab KU Leuven, ITEC KU Leuven, and IDLab Ghent University-imec.

Streaming MASSIF - eHealth

BESOCIAL

BESOCIAL is a cross-institutional research project, aiming to develop a sustainable strategy for archiving and preserving social media in Belgium. Heterogeneous social media content, provenance information provided by web archivists as well as preservation metadata enclosed in web archive (WARC) files characterize this use case.

RML - RMLMapper - YARRRML

BoB

Building Information Modelling (BIM) allows making elaborate, information-rich models of building designs. However, there is no easy way yet to couple these models to what is actually happening on-site, during a construction. The BoB project aims to create a 2-way link between BIM models and the actual building, improving building efficiency and avoiding costly errors.

RML

COMBUST

The opportunities of big data are tremendous. However, it is still a major challenge to combine available data from various heterogeneous sources and put it on offer in a way that is reliable, trustworthy and a good business case for the data owners. The COMBUST project was set up to create solutions and guidelines for this data fusion challenge, in a realistic setting and in view of offering a valuable data service.

RML - RMLEditor - RMLMapper

DAIQUIRI

During sports events, athletes use wearables and sensors to track their performances. Currently, this data is used for coaching, yet it might be useful for covering the events as well. As such, new Artificial intelligence (AI) applications in professional sports based on sensors, wearables and video data might enrich live sports reporting. However, a platform capable of turning insightful sports data into stories for live commenters, content editors or viewers does not yet exist. The DAIQUIRI project will develop AI algorithms that address current challenges associated with data overload, sensor-video matching, dynamic captioning and multi-modal stories. The outcome will be a sensor data platform and dashboard that supports media professionals in their live sports coverage and the audiences’ viewing experiences.

RML - RMLStreamer

DiSSeCt

DiSSeCt aims to design distributed semantic software solutions and algorithms for continuous exchange of huge streams of data between different partners in specific ecosystems. By converting data to knowledge, and exchanging this knowledge in an intelligent, secure and dynamic manner, personalised and context-aware services can be offered to end-users.

DyVerSIFy

Mining sensor data can give companies valuable insights into their assets and activities. But as technology advances, analyzing and visualizing the data becomes increasingly time- and resource-intensive. The DyVerSIFy project aims to develop software components and methodologies in the domains of dynamic visualization, adaptive anomaly detection and scalability to drive dynamic, adaptive and scalable sensor analytics.

RML - RMLEditor - RMLStreamer

EcoDaLo

Media companies in Flanders today have fragmented consumer data, with each company maintaining its own data management platform. As a result, data remains largely undervalorized. The EcoDaLo project aims to develop a knowledge platform for publishers allowing the integration of many diverse data sources.

RML - RMLMapper - YARRRML

ESSENCE

Human perspectives into a smart city platform that collects many media types, from personal devices and public displays to augmented reality. The ultimate goal is to create interactive stories embedded in the city environment about civic actions that boost engagement in these projects.

RML - RMLStreamer

FAST

Complicated rules, multiple contact points, numerous forms and applications and three administrative levels: processes as common as home renovations in Flanders require a lot of manual administrative work. To streamline customer journeys and simplify processes, municipalities and other public organizations would benefit from e-government applications. However, implementing these solutions is challenging, considering the lack of well-structured content and the need to integrate legacy systems. FAST aims to tackle the challenges to e-government adoption by creating an engine to automatically generate customer journeys from existing unstructured content – the ideal collection of digital interaction points between public services, organizations and citizens – for a range of public tasks.

FREME

The aim of the FREME innovative action is to establish an “Open Framework of E-Services for Multilingual and Semantic Enrichment of Digital Content”. Six enrichment services will be designed, piloted, and validated during the action. Their innovation, usability, and robustness will be shaped by four real world business cases that will bring FREME data innovation and technology transfer directly to the market: (1) authoring and publishing multilingually and semantically enriched eBooks; (2) integrating semantic enrichment into multilingual content during localisation; (3) enhancing cross-language sharing and access to open data and (4) empowering personalised content recommendations.

GreenMov

Definition of harmonized data models for green mobility and the development of advanced green mobility services, such as traffic monitoring, shared mobility and environmental impact.

Data Models - Green Mobility - Reference Architecture

Hello Jenny

Hello Jenny aims to help senior citizens who have no or limited contact with friends or family. It does this by providing a smart speaker to the senior citizen which allows them to schedule a visit with their assigned buddy. An analysis is performed to determine when a senior needs a visit and suggests this to the senior if this is the case.

N3 - RML - RMLMapper

Hybrid AI for Buildings

To convert office buildings in truly sustainable (to meet climate goals), comfortable & healthy (to improve employee wellbeing and related productivity) and flexible spaces (supporting the balance between telecommuting and on site working) extra (smart) services are needed to help building & facility managers to decide on building upgrade investments, optimally allocate office spaces and optimally control HVAC systems. Such services require access to a broad range of data sources (HVAC data, user feedback, BIM models, etc.) via easy accessible interfaces, but current deployment requires a lot of (manual) work as data is often distributed over many systems, using different protocols and data models. There is a clear need to extend the interoperability capabilities between stakeholders and service providers involved in the building lifecycle, to enable a rapid scaling to many buildings. Hybrid AI for buildings is an imec.AAA project focusing on designing tools and algorithms to easily design building optimization services by leveraging a semantic model for interoperability and easily supporting the design of novel AI services based on this semantically enriched data.

LDES - SHACL - Streaming MASSIF

i-Learn

Technical support concerning data modeling and integration for the i-Learn framework

Mellon Scholarly Communication

At the basis of this project is a vision of a researcher-centric, institution-enabled scholarly communication system, aligned with the Decentralized Web concepts and technologies, in which researchers use a personal domain and associated storage space as their long-term scholarly hub, and in which the core functions of scholarly communication are fulfilled in a decoupled manner.

Comunica - Solid

MOS2S

The MOS2S project focuses on media orchestration platforms and technologies that allow devices, data and media streams to be orchestrated into a rich and coherent media experience on various end-user devices. Applications include crowd journalism and live events (experience and entertainment).

RML - RMLMapper

Open Planner

The Open Planner Team wants to empower anyone to build any kind of route planner, ' without having to invest in data integration. Your route planner should work out of the box. Therefore, we are first and foremost working on the Open Data itself, by helping data owners publish their data.

Open Data - Public Transport - Route Planning

ORCA

ORCA was a bilateral project between IDLab Ghent University-imec and Televic Healthcare. It researched the design of a mobile, person-oriented and context-aware nurse call system.

N3 - RML - Reasoning - Rules - Streaming MASSIF - eHealth

POSH

POSH (Predictive Optimized Supply Chain) will research and realize methods and software solutions that leverage on the availability of big data to optimize integrated procurement and inventory management strategies. Within the scope of this project, we mainly focus on applications within B2B manufacturing supply chains, incorporating all actions required to bring parts from suppliers to the production or assembly manufacturing companies producing finished products for the consumer market.

RML - RMLMapper - YARRRML

R.A.M.P.

Both broadcasting systems and conferencing systems see a demand for high-quality blended data overlays within end-user video streams. Hence the need for an automatic play-out system which allows to capture and visualize events and which is able to integrate additional data in real time, only requiring minor guidance by a 'director' (be it the DJ or a conference chairman). Not only do we want to control play-out systems, we will investigate the control of physical objects in a studio by automated triggers, such as automatic adjusting of live cameras and microphone arrays based on audio, video and contextual information. A robust semantic understanding of the stream, based on the fusion of contextual information and the analysis of media content is mandatory. In the end, we designed a semantic play-out decision-support system centralized around the currently active radio- show or conference. This project will contribute to 1) the creation of a real new medium, 2) efficient and cost-reducing post-production, and 3) the opening up of TV Graphics to the ecosystem of Flemish web agencies. The consortium consisted of WMMa, Televic Conference, Small Town Heroes, MIX iMinds, IDLab Ghent University-imec, Mintlab KU Leuven.

Media - N3 - RML - Reasoning - Rules - Streaming MASSIF

RE-ENNOVATE

RE-ENNOVATE is an ambitious 3-year project to make home renovation smarter, cheaper, faster and better and re-innovate the process of energetic renovation. As the need for more energy efficient homes is only amplified by the current energy crisis, new tools are necessary for homer owners and professionals to understand which renovation measures work for them. With novel data-driven methods, RE-ENNOVATE will make the benefits and cost of renovations more transparent to individuals, propose personalized renovation packages, and increase automation, while continuously improving all steps by a closed feedback loop. The research consortium includes WTCB, imec, Ghent University, Adaptive, Mosard, Scone, Leuven2030 and Vandereyt. The project runs in cooperation with Flux50 and VLAIO.

Streaming MASSIF

Smart Water Way

The volume of roadway traffic is steadily increasing in Belgium despite the introduction of tolls. Consequently, there is a strong focus on increasing quality of life in urban areas by moving last-mile logistics (LML) to local waterways. To drive this modal shift, the SmartWaterWay project will make autonomous pallet shuttle barges cost effective in urban settings by combining low-cost onboard sensors with onshore sensors equipped at bridges, locks and bays.

SolidLab

To reinforce the trust of its citizens and help companies compete against internet giants, Flanders is leading the race to set a new standard in data protection. A EUR 7 million government investment is providing a major boost for the practical implementations of a technology called Solid. Three of Flanders’ leading universities are joining forces in SolidLab Vlaanderen.

Comunica - LDES - RML - Solid - TREE

Velopark

To facilitate information access to bicycle users about parking infrastructure, Fietsberaad, a pro-cycling organization managed by the Flemish government, worked together with local authorities, parking facility owners and operators in Belgium to design common framework for modeling and publishing (live) information of parking facilities. Existing data and interfaces (APIs) were reused and transformed to a common semantic data representation using RML rules in YARRRML syntax. Currently more than 30 municipalities across Belgium publish data of 2500+ parkings, made accessible to cyclists online.

JSON-LD - Live Data Streams - RML - RMLMapper - YARRRML

Vlaamse Smart Data Space

The Flemish Smart Data Space (in Dutch: Vlaamse Smart Data Space) ensures the sustainable publication and consultation of sensor data and their context information. As a decentralized solution, the Flemish Smart Data Space uses a joint ecosystem with clear rules, allowing us to share, publish and reuse data in a smart way.

LDES - RDF