Current projects

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


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


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


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


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


Past projects

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


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.


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


i-Learn

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


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


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.