Data-driven innovation has recently changed the mindset in data sharing from centralized architectures and monolithic data exploitation by data providers (data platforms) to decentralized architectures and different data sharing options among all involved participants (data ecosystems). Data sharing is further strengthened through the establishment of several legal frameworks (e.g., European Strategy for Data, Data Act, Data Governance Act) and the emerging initiatives that provide the means to build data ecosystems, which is evident in the formulated communities, established use cases, and the technical solutions. However, the data ecosystems have not been thoroughly studied so far. The differences between the various data ecosystems are not clear, making it hard to choose the most suitable for each use case, negatively impacting their adoption. Since the domain is growing fast, a review of the state-of-the-art data ecosystem initiatives is needed to analyze what each initiative offers, identify collaboration prospects, and highlight features for improvement and open research topics. In this paper, we review the state-of-the-art data ecosystem initiatives, describe their innovative aspects, compare their technical and business features, and identify open research challenges. We aim to assist practitioners in choosing the most suitable data ecosystem for their use cases and scientists to explore emerging research opportunities. Furthermore, we will provide a framework that outlines the key criteria for evaluating these initiatives, ensuring that stakeholders can make informed decisions based on their specific needs and objectives. By synthesizing our findings, we hope to foster a deeper understanding of the evolving landscape of data ecosystems and encourage further advancements in this critical field.

The conventional approach for controlling the supply temperature in collective space heating networks relies on a predefined heating curve determined by outdoor temperature and heat emitter type. This prioritises thermal comfort but lacks energetic and financial optimisation. This research proposes an adaptive supply temperature control in well-insulated dwellings, responsive to diverse environmental parameters. The approach considers variable electricity prices and accommodates different indoor temperature set points in dwellings. The study evaluates the effectiveness of two Deep Reinforcement Learning (DRL) algorithms, i.e., Proximal Policy Optimisation (PPO) and Deep Q-Network (DQN), across various scenarios. Results reveal that DQN excels in collective space heating systems with underfloor heating in each dwelling, while PPO proves superior for radiator-based systems. Both outperform the traditional heating curve, achieving up to 13.77% (DQN) and 16.15% (PPO) cost reduction while guaranteeing thermal comfort. Additionally, the research highlights the capability of DRL-based methods to dynamically set the supply temperature based on a cloud of set points, showcasing adaptability to diverse environmental factors and addressing the growing significance of indoor heat gains in well-insulated dwellings. This innovative approach holds promise for more efficient and environmentally conscious heating strategies within collective space heating networks.

Automated Guided Vehicles (AGV) are omnipresent, and are able to carry out various kind of preprogrammed tasks. Unfortunately, a lot of manual configuration is still required in order to make these systems operational, and configuration needs to be re-done when the environment or task is changed. As an alternative to current inflexible methods, we employ a learning based method in order to perform directed exploration of a previously unseen environment. Instead of relying on handcrafted heuristic representations, the agent learns its own environmental representation through its embodiment. Our method offers loose coupling between the Reinforcement Learning (RL) agent, which is trained in simulation, and a separate, on real-world images trained task module. The uncertainty of the task module is used to direct the exploration behavior. As an example, we use a warehouse inventory task, and we show how directed exploration can improve the task performance through active data collection. We also propose a novel environment representation to efficiently tackle the sim2real gap in both sensing and actuation. We empirically evaluate the approach both in simulated environments and a real-world warehouse.

e-Government applications have hard-coded and non-personalized user journeys with high maintenance costs to keep up with, e.g., changing legislation. Automatic administrative workflows are needed. We present the OSLO-steps vocabulary and the workflow composer: combined, they are a means to create cross-organizational interoperable user journeys, adapted to the user's needs. We identify the requirements for automating administrative workflows and present an architecture and its implemented components. By using Linked Data principles to decentrally describe independent steps using states as pre- and postconditions, and composing workflows on-the-fly whilst matching a user's state to those preconditions, we automatically generate next steps to reach the user's goal. The validated solution shows its feasibility, and the upcoming interest around interoperable personal data pods (e.g., via Solid) can further increase its potential.

The transformation of society towards a digital economy and government austerity creates a new context leading to changing roles for both government and private sector. Boundaries between public and private services are blurring, enabling government and private sector to collaborate and share responsibilities. In Belgium, the regional Government of Flanders embedded the re-use of public sector information in its legislation and published a data portal containing well over 4000 Open Datasets. Due to a lack of interoperability, interconnecting and interpreting these sources of information remain challenges for public administrations, businesses and citizens. To dissolve the boundaries between the data silos, the Flemish government applied Linked Data design principles in an operational public sector context. This paper discusses the trends we have identified while ‘rewiring’ the Authentic Source for addresses to a Linked Base Registry. We observed the impact on multiple interoperability levels; namely on the legal, organisational, semantic and technical level. In conclusion Linked Data can increase semantic and technical interoperability and lead to a better adoption of government information in the public and private sector. We strongly believe that the insights from the past thirteen years in the region of Flanders could speed up processes in other countries that are facing the complexity of raising technical and semantic interoperability.

Industrial processes are heavily instrumented by employing a large number of sensors, generating huge amounts of data. One goal of the Industry 4.0 era is to apply data-driven approaches to optimize such processes. At the basic oxygen furnace (BOF), molten iron is transformed into steel by lowering its carbon content and achieving a certain chemical endpoint. In this work, we propose a data-driven approach to predict the endpoint temperature and chemical concentration of phosphorus, manganese, sulfur and carbon at the basic oxygen furnace. The prediction is based on two distinct datasets. First, a collection of static features is used which represent a more classic data-driven solution. The second approach includes time-series data that provide a better estimate of the final endpoint and enable further tuning of the process parameters, if necessary. For both approaches, model-based feature selection is used to filter the most relevant information. Results obtained by both models are compared in order to estimate the added value of including the time series data analysis on the performance of the BOF process. Results show that a simple feature extraction approach can enhance the prediction for phosphorus, manganese and temperature.

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When researchers formulate search queries to find relevant content on the Web, those queries typically consist of keywords that can only be matched in the content or its metadata. The Web of Data extends this functionality by bringing structure and giving well-defined meaning to the content and it enables humans and machines to work together using controlled vocabularies. Due the high degree of mismatches between the structure of the content and the vocabularies in different sources, searching over multiple heterogeneous repositories of structured data is considered challenging. Therefore, the authors present a semantic search engine for researchers facilitating search in research related Linked Data. To facilitate high-precision interactive search, they annotated and interlinked structured research data with ontologies from various repositories in an effective semantic model. Furthermore, the authors' system is adaptive as researchers can synchronize using new social media accounts and efficiently explore new datasets.

Nowadays, the Web has become one of the main sources of biodiversity information. An increasing number of biodiversity research institutions add new specimens and their related information to their biological collections and make this information available on the Web. However, mechanisms which are currently available provide insufficient provenance of biodiversity information. In this paper, we propose a new biodiversity provenance model extending the W3C PROV Data Model. Biodiversity data is mapped to terms from relevant ontologies, such as Dublin Core and GeoSPARQL, stored in triple stores and queried using SPARQL endpoints. Additionally, we provide a use case using our provenance model to enrich collection data.

Evaluating federated Linked Data queries requires consulting multiple sources on the Web. Before a client can execute queries, it must discover data sources, and determine which ones are relevant. Federated query execution research focuses on the actual execution, while data source discovery is often marginally discussed—even though it has a strong impact on selecting sources that contribute to the query results. Therefore, the authors introduce a discovery approach for Linked Data interfaces based on hypermedia links and controls, and apply it to federated query execution with Triple Pattern Fragments. In addition, the authors identify quantitative metrics to evaluate this discovery approach. This article describes generic evaluation measures and results for their concrete approach. With low-cost data summaries as seed, interfaces to eight large real-world datasets can discover each other within 7 minutes. Hypermedia-based client-side querying shows a promising gain of up to 50% in execution time, but demands algorithms that visit a higher number of interfaces to improve result completeness.

In order to assess the trustworthiness of information on social media, a consumer needs to understand where this information comes from, and which processes were involved in its creation. The entities, agents and activities involved in the creation of a piece of information are referred to as its provenance, which was standardized by W3C PROV. However, current social media APIs cannot always capture the full lineage of every message, leaving the consumer with incomplete or missing provenance, which is crucial for judging the trust it carries. Therefore in this paper, we propose an approach to reconstruct the provenance of messages on social media on multiple levels. To obtain a fine-grained level of provenance, we use an approach from prior work to reconstruct information cascades with high certainty, and map them to PROV using the PROV-SAID extension for social media. To obtain a coarse-grained level of provenance, we adapt our similarity-based, fuzzy provenance reconstruction approach -- previously applied on news. We illustrate the power of the combination by providing the reconstructed provenance of a limited social media dataset gathered during the 2012 Olympics, for which we were able to reconstruct a significant amount of previously unidentified connections.

Interest in eLearning environments is increasing, as well as in digital textbooks and gamification. The advantages of gamification in the context of education have been proven. However, gamified educational material, such as gamified digital textbooks and systems, are scarce. As an answer to the need for such material, the framework GEL (Gamification for EPUB using Linked Data) has been developed. GEL allows to incorporate gamification concepts in a digital textbook, using EPUB 3 and Linked Data. As part of GEL, we created the ontology Gamification Ontology (GO), representing the different gamification concepts, and a JavaScript library. Using GO allows to discover other gamified books, to share gamification concepts between applications and to separate the processing and representation of the gamification concepts.

We have created and evaluated an algorithm capable of deduplicating and clustering exact- and near-duplicate media items of type photo and video that get shared on multiple social networks in the context of events. This algorithm works in an entirely ad hoc manner without requiring any pre-calculation. When people attend events, they more and more share event-related media items publicly on social networks to let their social network contacts relive and witness the attended events. In the past, we have worked on methods to accumulate such public user-generated multimedia content in order to summarize events visually, for example, in the form of media galleries or slideshows. In this paper, first, we introduce social-network-specific reasons and challenges that cause near-duplicate media items. Second, we detail an algorithm for the task of deduplicating and clustering exact- and near-duplicate media items stemming from multiple social networks. Finally, we evaluate the algorithm's strengths and weaknesses and thoroughly compare its performance with the state-of-the-art feature detection algorithms SIFT, ASIFT and SURF and show that for the given use case it performs almost equally well accuracy-wise, but strongly outperforms speed-wise.

The concept of Linked Data has made its entrance in the cultural heritage sector due to its potential use for the integration of heterogeneous collections and deriving additional value out of existing metadata. However, practitioners and researchers alike need a better understanding of what outcome they can reasonably expect of the reconciliation process between their local metadata and established controlled vocabularies which are already a part of the Linked Data cloud. This paper offers an in‐depth analysis of how a locally developed vocabulary can be successfully reconciled with the Library of Congress Subject Headings (LCSH) and the Arts and Architecture Thesaurus (AAT) through the help of a general‐purpose tool for interactive data transformation (OpenRefine). Issues negatively affecting the reconciliation process are identified and solutions are proposed in order to derive maximum value from existing metadata and controlled vocabularies in an automated manner.

There's a gap in consumer communications on how networks, devices, and services handle and identify space & time in media resources in the Web. W3C's Media Fragments URI specification, which we edited and contributed to over the last couple of years, not only bridges this gap by homogeneously opening up time-related media to the Internet crowd, but also makes time-related annotation feasible for hyperlinking into that media, thus finally also providing the necessary support for this already omnipresent `third dimension' time into the Internet. As such, this uniform approach using the standardised (semantic) web technology stack will improve search, discovery, usage, management, security, and linking of related media resources and its fragments.

HTTP adaptive streaming was introduced with the general idea that user agents interpret a manifest file (describing different representations and segments of the media); where-after they retrieve the media content using sequential HTTP progressive download operations. MPEG started with the standardization of an HTTP streaming protocol, defining the structure and semantics of a manifest file and additional restrictions and extensions for container formats. At the same time, W3C is working on a specification for addressing media fragments on the Web using Uniform Resource Identifiers. The latter not only defines the URI syntax for media fragment identifiers but also the protocol for retrieving media fragments over HTTP. In this paper, we elaborate on the role of Media Fragment URIs within HTTP adaptive streaming scenarios. First, we elaborate on how different media representations can be addressed by means of Media Fragment URIs, by using track fragments. Additionally, we illustrate how HTTP adaptive streaming is realized relying on the Media Fragments URI retrieval protocol. To validate the presented ideas, we implemented Apple's HTTP Live streaming technique using Media Fragment URIs.

In this demonstration, we present NinSuna, a fully integrated multimedia content adaptation platform based on Semantic Web technologies. Moreover, we show how NinSuna can be used as a server-side implementation of W3C Media Fragment URIs, which enables addressing media fragments on the Web using URIs.

In this paper, we describe two examples of implementations of the Media Fragments URI specification which is currently being developed by the W3C Media Fragments Working Group. The group's mission is to create standard addressing schemes for media fragments on the Web using Uniform Resource Identifiers (URIs). We describe two scenarios to illustrate the implementations. More specifically, we show how User Agents (UA) will either be able to resolve media fragment URIs without help from the server, or will make use of a media fragments-aware server. Finally, we present some ongoing discussions and issues regarding the implementation of the Media Fragments specification.

The health care sector is no longer imaginable without electronic health records. However, since the original idea of electronic health records was focused on data storage and not on data processing, a lot of current implementations do not take full advantage of the opportunities provided by computerization. This paper introduces the patient summary ontology for the representation of electronic health records and demonstrates the possibility to create next generation assisting clinical applications based on these semantic-aware electronic health records. Also, an architecture to interoperate with electronic health records formatted using other standards is presented.

This paper introduces the Movie Script Markup Language (MSML), a document specification for the structural representation of screenplay narratives for television and feature film drama production. Its definition was motivated by a lack of available structured and open formats that describe dramatic narrative but also support IT-based production methods of audiovisual drama. The MSML specification fully supports contemporary screenplay templates in a structured fashion, and adds provisions for drama manufacturing methods that allow drama crew to define how narrative can be translated to audiovisual material. A timing model based on timed petri nets is included to enable fine-grained event synchronization. Finally, MSML comprises an animation module through which narrative events can drive production elements like 3-D previsualization, content repurposing or studio automation. MSML is currently serialized into XML documents and is formally described by a complement of an XML Schema and ISO Schematron schema. The specification has been developed in close collaboration with actual drama production crew and has been implemented in a number of proof-of-concept demonstrators.

A multitude of platforms with widely varying capabilities is available today for the consumption of broadcasted audiovisual content. Each class of media consumption devices should receive a product not only adapted to its capabilities, but also in a manner that fully conveys the cinematic narrative and hence provides true universal media experiences. This paper presents semantic adaptation processes that integrate with other drama media manufacturing processes and allow directors and other creative staff to naturally carry over cinematic decision making into the content adaptation domain, thereby using narrative story elements already available from other production processes. We discuss how the adaptation process interacts with other processes and what parameters influence the adaptations applied to audiovisual material, followed by an evaluation of our current implementation.

Multimedia content adaptation is gaining importance because of the growing amount of multimedia content on the one hand and the growing diversity in usage environments on the other hand. Furthermore, to deal with the growing amount of coding formats for multimedia content, format-independent adaptation systems are desired. These systems support the exploitation of scalability to meet the usage environment, as well as semantic adaptations to meet the user preferences. In this demonstration, we present NinSuna, a fully integrated multimedia content adaptation platform based on semantic web technologies. It aims at being deployable in streaming environments and relies on format-independent semantic-aware adaptation engines.