@misc{bdva_data_sharing_space_2020, title = {{Towards a European-governed Data Sharing Space. Enabling data exchange and unlocking AI potential. BDVA Position Paper v2}}, url = {https://www.bdva.eu/sites/default/files/BDVA%20DataSharingSpaces%20PositionPaper%20V2_2020_Final.pdf}, publisher = {{Big Data Value Association}}, author = {{BVDA}}, urldate = {2024-02-10}, date = {2020-11}, year = {2020} } @misc{design_principles_data_spaces_2021, title = {Design Principles for Data Spaces. Position paper. Version 1.0.}, url = {https://doi.org/10.5281/zenodo.5244997}, publisher = {Open {DEI}}, editor = {Nagel, Lars and Lycklama, Douwe}, urldate = {2024-02-10}, date = {2021-04}, year = {2021}, month = {04}, doi = {10.5281/zenodo.5244997}, } @misc{dataspace_for_cci_2022, title = {Dataspace for Cultural and Creative Industries. Position paper. v.2.0}, url = {https://gaia-x.eu/wp-content/uploads/2022/10/EBU_position-paper_Media-Data-Space.pdf}, publisher = {{Gaia-X}}, author = {{EBU} and {Gaia-X}}, urldate = {2024-02-10}, date = {2022-10-10}, year = {2022}, month = {10}, day = {10}, note = {{WG} {CCI} v.2.0 – 08.18.2022}, } @report{commission_recommendation_common_culture_dataspace_2021, title = {Commission Recommendation ({EU}) 2021/1970 of 10 November 2021 on a common European data space for cultural heritage}, url = {http://data.europa.eu/eli/reco/2021/1970/oj/eng}, author = {{European Commission}}, urldate = {2024-02-10}, date = {2021-11-10}, year = {2021}, langid = {english}, note = {Code Number: 401 Code: {OJ} L Legislative Body: {COM}, {CNECT}} } @book{eccch_ex-ante_2022, author = {European Commission and Directorate-General for Research and Innovation and Brunet, P and De Luca, L and Hyvönen, E and Joffres, A and Plassmeyer, P and Pronk, M and Scopigno, R and Sonkoly, G}, title = {Report on a European collaborative cloud for cultural heritage – Ex–ante impact assessment}, publisher = {Publications Office of the European Union}, year = {2022}, doi = {doi/10.2777/64014}} @inproceedings{jarke_culture_data_space_2023, location = {Vancouver, Canada}, title = {Culture Data Space: A Case Study in Federated Data Ecosystems}, abstract = {In several national and even continental data strategies worldwide, the decentralized data space concept aims to address concerns of data sovereignty among organizations. A significant number of projects and a few already operational data spaces address application domains in industrial domains such as manufacturing, mobility and logistics, or health. However, data sovereignty has also become a key concern of artists and cultural institutions who are pursuing the two-pronged and sometimes conflicting goals of creating added value from data sharing, and protecting their intellectual property and personal privacy rights. Additional challenges of this sector include an orders-of-magnitude larger number of potential players compared to existing data spaces, frequently limited {IT} capabilities, a complex differentiated system of data types and regulations, and a novel interplay between heterogeneous data integration and analytics with human creativity, among many others. Also, the different evolution paths of the involved sub-communities require a sophisticated concept for federated data space evolution. This keynote talk reports experiences of the ”Data Space Culture”, a lighthouse project of the German Chancellors Office aiming at investigating these issues and demonstrating and evaluating a suitable data ecosystem around four use cases in the fields of theaters, museums, music training, and networking local culture communities. We also discuss the potential synergies and interoperation challenges with the many other culture digitization initiatives in Europe and beyond.}, eventtitle = {Joint Workshops at 49th International Conference on Very Large Data Bases ({VLDBW}’23) — Data Ecosystems ({DEco}), August 28 - September 1, 2023}, booktitle = {{CEUR} Workshop Proceedings}, year = {2023}, date = {2023}, author = {Jarke, Matthias}, langid = {english}, keywords = {culture Informatics, data ecosystem, data exchange, data sovereignty, data space, federated data integration, {GAIA}-X} } @Inbook{curry_enabling_knowledge_flows_2020, author="Curry, Edward and Ojo, Adeboyega", title="Enabling Knowledge Flows in an Intelligent Systems Data Ecosystem", bookTitle="Real-time Linked Dataspaces: Enabling Data Ecosystems for Intelligent Systems", year="2020", publisher="Springer International Publishing", address="Cham", pages="15--43", abstract="In data ecosystems, vast amounts of data move among actors within complex information supply chains that can form in different ways around an organisation, community technology platforms, and within or across sectors. This chapter explores the role a data ecosystem can play in the design of intelligent systems to support data-rich Internet of Things (IoT)-based smart environments. The chapter examines different elements of an intelligent systems data ecosystem that are critical to understanding the data management and sharing challenges they present.", isbn="978-3-030-29665-0", doi="10.1007/978-3-030-29665-0_2", url="https://doi.org/10.1007/978-3-030-29665-0_2" } @Inbook{curry_dataspaces_2020, author="Curry, Edward", title="Dataspaces: Fundamentals, Principles, and Techniques", bookTitle="Real-time Linked Dataspaces: Enabling Data Ecosystems for Intelligent Systems", year="2020", publisher="Springer International Publishing", address="Cham", pages="45--62", abstract="A dataspace is an emerging approach to data management which recognises that in large-scale integration scenarios, involving thousands of data sources, it is difficult and expensive to obtain an upfront unifying schema across all sources. Data is integrated on an ``as-needed'' basis with the labour-intensive aspects of data integration postponed until they are required. Dataspaces reduce the initial effort required to set up data integration by relying on automatic matching and mapping generation techniques. This results in a loosely integrated set of data sources. When tighter semantic integration is required, it can be achieved in an incremental ``pay-as-you-go'' fashion by detailed mappings between the required data sources. This chapter introduces dataspaces and the fundamentals of ``best-effort'' data management.", isbn="978-3-030-29665-0", doi="10.1007/978-3-030-29665-0_3", url="https://doi.org/10.1007/978-3-030-29665-0_3" } @Inbook{curry_fundamentals_rlt_dataspaces_2020, author="Curry, Edward", title="Fundamentals of Real-time Linked Dataspaces", bookTitle="Real-time Linked Dataspaces: Enabling Data Ecosystems for Intelligent Systems", year="2020", publisher="Springer International Publishing", address="Cham", pages="63--80", abstract="Dataspaces can provide an approach to enable data management in smart environments that can help to overcome technical, conceptual, and social/organisational barriers to information sharing. However, there has been limited work on the use of dataspaces within smart environments and the necessary support services for real-time events and data streams. This chapter introduces the Real-time Linked Dataspace (RLD) as a data platform for intelligent systems within smart environments. The RLD combines the pay-as-you-go paradigm of dataspaces with linked data, knowledge graphs, and (near) real-time processing capabilities. The RLD has been specifically designed to support the sharing and processing of data between intelligent systems within smart environments. We propose a set of specialised dataspace support services to enable the requirements of loose administrative proximity and semantic integration for event and stream systems. These requirements form the foundation of the techniques and models used to process events and streams within the RLD.", isbn="978-3-030-29665-0", doi="10.1007/978-3-030-29665-0_4", url="https://doi.org/10.1007/978-3-030-29665-0_4" } @Inbook{ulHassan_catalog_entity_management_2020, author="ul Hassan, Umair and Ojo, Adegboyega and Curry, Edward", title="Catalog and Entity Management Service for Internet of Things-Based Smart Environments", bookTitle="Real-time Linked Dataspaces: Enabling Data Ecosystems for Intelligent Systems", year="2020", publisher="Springer International Publishing", address="Cham", pages="89--103", abstract="A fundamental requirement for intelligent decision-making within a smart environment is the availability of information about entities and their schemas across multiple data sources and intelligent systems. This chapter first discusses how this requirement is addressed with the help of catalogs in dataspaces; it then details how entity data can be more effectively managed within a dataspace (and for its users) with the use of an entity management service. Dataspaces provide a data co-existence approach to overcome problems in current data integration systems in a pay-as-you-go manner. The idea is to bootstrap the integration with automated integration, followed by incremental improvement of entity consolidation and related data quality. The catalog and entity management services are core services needed to support the incremental data management approach of dataspaces. We provide an analysis of existing data catalogs that can provide different forms of search, query, and browse functionality over datasets and their descriptions. In order to cover the entity requirements, the catalog service is complemented with an entity management service that is concerned with the management of information about entities.", isbn="978-3-030-29665-0", doi="10.1007/978-3-030-29665-0_6", url="https://doi.org/10.1007/978-3-030-29665-0_6" } @Inbook{freitas_query_heterogeneous_kg_2020, author="Freitas, Andr{\'e} and O'Ri{\'a}in, Se{\'a}n and Curry, Edward", title="Querying and Searching Heterogeneous Knowledge Graphs in Real-time Linked Dataspaces", bookTitle="Real-time Linked Dataspaces: Enabling Data Ecosystems for Intelligent Systems", year="2020", publisher="Springer International Publishing", address="Cham", pages="105--124", abstract="As the volume and variety of data sources within a dataspace grow, it becomes a semantically heterogeneous and distributed environment; this presents a significant challenge to querying the dataspace. Approaches used for querying siloed databases fail within large dataspaces because users do not have an a priori understanding of all the available datasets. This chapter investigates the main challenges in constructing query and search services for knowledge graphs within a linked dataspace. Search and query services within a linked dataspace do not follow a one-size-fits-all approach and utilise a range of different techniques to support different characteristics of data sources and user needs.", isbn="978-3-030-29665-0", doi="10.1007/978-3-030-29665-0_7", url="https://doi.org/10.1007/978-3-030-29665-0_7" } @Inbook{derguech_enhancing_discovery_dataspace_2020, author="Derguech, Wassim and Curry, Edward and Bhiri, Sami", title="Enhancing the Discovery of Internet of Things-Based Data Services in Real-time Linked Dataspaces", bookTitle="Real-time Linked Dataspaces: Enabling Data Ecosystems for Intelligent Systems", year="2020", publisher="Springer International Publishing", address="Cham", pages="125--137", abstract="A dataspace is an emerging data management approach used to tackle heterogeneous data integration in an incremental manner. Data sources that are participants in a dataspace can be of various types such as online services, open datasets, sensors, and smart devices. Given the dynamicity of dataspaces and the diversity of their data sources and user requirements, finding appropriate sources of data can be challenging for users. Thus, it is important to describe and organise data sources in the dataspace efficiently. In this chapter, we present an approach for organising and indexing data services based on their semantic descriptions and using a feature-oriented model. We apply Formal Concept Analysis for organising and indexing the descriptions of sensor-based data services. We have experimented and validated the approach in a real-world smart environment which has been retrofitted with Internet of Things-based sensors observing energy, temperature, motion, and light.", isbn="978-3-030-29665-0", doi="10.1007/978-3-030-29665-0_8", url="https://doi.org/10.1007/978-3-030-29665-0_8" } @Inbook{ulHassan_human-in-the-loop_2020, author="ul Hassan, Umair and Curry, Edward", title="Human-in-the-Loop Tasks for Data Management, Citizen Sensing, and Actuation in Smart Environments", bookTitle="Real-time Linked Dataspaces: Enabling Data Ecosystems for Intelligent Systems", year="2020", publisher="Springer International Publishing", address="Cham", pages="139--158", abstract="Humans are playing critical roles in the management of data at large scales, through activities including schema building, matching data elements, resolving conflicts, and ranking results. The application of human-in-the-loop within intelligent systems in smart environments presents challenges in the areas of programming paradigms, execution methods, and task design. This chapter examines current human-in-the-loop approaches for data management tasks, including data integration, data collection (e.g. citizen sensing), and query refinement. A comparison of approaches (Augmented Algorithms, Declarative Programming, and Stand-alone Platforms) that can enable human tasks within data management is presented. The chapter also covers spatial tasks where users within the smart environment are requested to take physical actions in the environment in the form of citizen actuation.", isbn="978-3-030-29665-0", doi="10.1007/978-3-030-29665-0_9", url="https://doi.org/10.1007/978-3-030-29665-0_9" } @Inbook{qin_dissemination_iot_streams_2020, author="Qin, Yongrui and Sheng, Quan Z. and Curry, Edward", title="Dissemination of Internet of Things Streams in a Real-time Linked Dataspace", bookTitle="Real-time Linked Dataspaces: Enabling Data Ecosystems for Intelligent Systems", year="2020", publisher="Springer International Publishing", address="Cham", pages="191--208", abstract="The Internet of Things (IoT) envisions smart objects and intelligent systems collecting and sharing data on a global scale to enable smart environments. One challenging data management issue is how to disseminate data to relevant consumers efficiently. This chapter leverages semantic technologies, such as linked data, which can facilitate machine-to-machine communications to build an efficient stream dissemination system for Semantic IoT. The system integrates linked data streams generated from various collectors and disseminates matched data to relevant consumers based on user queries registered in the system. We design two new data structures to suit the needs of high-performance linked data stream dissemination in the following two scenarios: (1) stream dissemination in point-to-point systems; and (2) stream dissemination in wireless broadcast systems. The evaluation of the approaches using real-world datasets shows that they can disseminate linked data streams more efficiently than existing techniques.", isbn="978-3-030-29665-0", doi="10.1007/978-3-030-29665-0_12", url="https://doi.org/10.1007/978-3-030-29665-0_12" }