{"id":12643,"date":"2024-07-18T14:55:16","date_gmt":"2024-07-18T12:55:16","guid":{"rendered":"https:\/\/www.iese.fraunhofer.de\/blog\/?p=12643"},"modified":"2024-07-30T14:07:44","modified_gmt":"2024-07-30T12:07:44","slug":"interoperability-in-data-spaces","status":"publish","type":"post","link":"https:\/\/www.iese.fraunhofer.de\/blog\/interoperability-in-data-spaces\/","title":{"rendered":"Interoperability in data spaces: then, now, and next"},"content":{"rendered":"<p class=\"lead\"><span class=\"TextRun SCXW138404117 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW138404117 BCX0\">The topic of data spaces has gained increased traction in Europe and beyond. In this blog post, we will talk <\/span><span class=\"NormalTextRun SCXW138404117 BCX0\">in a compact way about <\/span><span class=\"NormalTextRun SCXW138404117 BCX0\">the history of the concept, its current definition, and the <\/span><span class=\"NormalTextRun SCXW138404117 BCX0\">significant<\/span><span class=\"NormalTextRun SCXW138404117 BCX0\"> challenges that lie ahead for consolidating the idea in practice.<\/span><\/span><span class=\"EOP SCXW138404117 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<h2><span data-contrast=\"auto\">THEN<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h2>\n<p><span data-contrast=\"auto\">The concept of data spaces was introduced in 2005 [1] and originally referred to a data management approach for handling several data sources in the context of an organization. In this context, the meaning of the term \u201corganization\u201d ranges from, for example, enterprises and government agencies (large) to smart homes and personal devices (small). What these organizations have in common is that they need to handle data sources that are increasingly diverse, numerous, and interrelated, which entails many information management challenges, for example:<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"-\" data-font=\"Calibri\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Calibri&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;-&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Retrieval:<\/span><\/b><span data-contrast=\"auto\"> How to search and query data across heterogeneous data sources?<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"-\" data-font=\"Calibri\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Calibri&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;-&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"2\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Rules:<\/span><\/b> <span data-contrast=\"auto\">How to enforce policies? How to ensure integrity? How to manage naming conventions?<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"-\" data-font=\"Calibri\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Calibri&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;-&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"3\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Access:<\/span><\/b><span data-contrast=\"auto\"> How to control data access? How to ensure availability?<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"-\" data-font=\"Calibri\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Calibri&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;-&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"4\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Evolution:<\/span><\/b><span data-contrast=\"auto\"> How to manage not only the data, but also the metadata, over time?<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">As the authors emphasized, data spaces (back then spelled \u201cdataspaces\u201d) are not a data integration approach [1]. Data integration approaches require significant upfront effort, as they need semantic integration before any services can be provided. Instead, the focus of data spaces is put on data co-existence. Therefore, neither upfront integration nor exclusive control over the data is required to enable a data space.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The services needed to realize data spaces focus on base functionality over all data sources, support to all types of data within the data space, only on-demand integration efforts, and \u201cbest-effort&#8220; results regarding data querying. The envisioned enabling services of data spaces included, for example, data cataloging, data search and query, data discovery, data monitoring, data updating, and event detection.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">One important feature of the original definition of data spaces is that it focuses on data management needs in the context of an organization. Although the authors mention examples of organizations consisting of multiple (sub-)organizations (e.g., large science-related collaborations, multiple government agencies, etc.), they did not emphasize it. Interestingly, the opportunities for collaboration across organizations have become the focus of data space initiatives we see nowadays.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<h2><span data-contrast=\"auto\">NOW<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h2>\n<p><span data-contrast=\"auto\">Many of the recent initiatives that have been working on how to realize data spaces have used a definition that does not underline the diversity, abundance, and relationships of the data sources, even though these characteristics still hold true. Instead, current definitions of data spaces as a data management approach give prominence to the existence of many stakeholders exchanging (or interested in exchanging) data. The main stakeholders in a data space are the data provider (who is typically, but not necessarily, the data owner) and the data consumer, followed by the data service provider [2]. Data providers provide raw data to the data space through catalogs, and data consumers benefit from either the raw data or processed data, which can be made available in a data space by the data service providers. Data service providers can be perceived as participants in the data space who process raw data and offer analytic results to data consumers.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Recent research [2] organizes key enablers of data spaces in three layers:<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"-\" data-font=\"Calibri\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Calibri&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;-&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"5\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Data connectors and infrastructure:<\/span><\/b><span data-contrast=\"auto\"> This is the bottom layer, which provides the fundamental building blocks for data spaces. It tackles data management at a meta-level, i.e., it focuses on standardizing methods and formats to describe the data and the participants in a data space, to find them, to identify them, and to negotiate contracts based on agreed data usage policies.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"-\" data-font=\"Calibri\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Calibri&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;-&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"6\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Data interoperability: <\/span><\/b><span data-contrast=\"auto\">On this middle layer, the goal is to enable data interoperability, i.e., to support the actual data exchange (not metadata exchange) among participants. This involves technological interfacing and data modeling measures. Syntactic and semantic interoperability among heterogeneous stakeholders must be achieved at this level.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"-\" data-font=\"Calibri\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Calibri&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;-&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"7\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Data value: <\/span><\/b><span data-contrast=\"auto\">At the top, the data value layer leverages data interoperability to offer data-related services such as data analytics and data quality improvements (cleaning, feature selection, feature engineering, etc.).<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">To date, most existing data space initiatives have focused on data connectors and infrastructure, and there are at least two reasons for this. First, since they refer to the bottom layer, they provide a basis for the layers above. Second, given the increasing concerns about data sovereignty, data spaces have been envisioned as an opportunity to enable trust among participants engaged in data exchange. Two of the most prominent data space initiatives in Europe that have emphasized the data sovereignty aspect are <\/span><a href=\"https:\/\/internationaldataspaces.org\/\"><span data-contrast=\"none\">IDS<\/span><\/a><span data-contrast=\"auto\"> and <\/span><a href=\"https:\/\/gaia-x.eu\/\"><span data-contrast=\"none\">Gaia-X<\/span><\/a><span data-contrast=\"auto\">. As described on the Gaia-X website, \u201cGaia-X enables and boosts the creating of data spaces through trusted platforms that comply with common rules, allowing users and providers to trust each other on an objective technological basis, to safely and freely share and exchange data across multiple actors.\u201d [3] Concerning specifically the implementation of data connectors, the <\/span><a href=\"https:\/\/projects.eclipse.org\/projects\/technology.edc\/developer\"><span data-contrast=\"none\">Eclipse Data Components<\/span><\/a><span data-contrast=\"auto\"> (EDC) framework is a leading initiative. There, data space connectors are described as \u201clogical gatekeepers that integrate into each [data space] participant\u2019s infrastructure and communicate with each other\u201d [4]. <\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p>&nbsp;<\/p>\n<figure id=\"attachment_12644\" aria-describedby=\"caption-attachment-12644\" style=\"width: 698px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-12644 size-large\" src=\"https:\/\/www.iese.fraunhofer.de\/blog\/wp-content\/uploads\/2024\/07\/fig1-698x341.png\" alt=\"Interoperability in data spaces\" width=\"698\" height=\"341\" srcset=\"https:\/\/www.iese.fraunhofer.de\/blog\/wp-content\/uploads\/2024\/07\/fig1-698x341.png 698w, https:\/\/www.iese.fraunhofer.de\/blog\/wp-content\/uploads\/2024\/07\/fig1-400x196.png 400w, https:\/\/www.iese.fraunhofer.de\/blog\/wp-content\/uploads\/2024\/07\/fig1-768x375.png 768w, https:\/\/www.iese.fraunhofer.de\/blog\/wp-content\/uploads\/2024\/07\/fig1.png 1385w\" sizes=\"auto, (max-width: 698px) 100vw, 698px\" \/><figcaption id=\"caption-attachment-12644\" class=\"wp-caption-text\">Figure 1: Participants and key enablers of data spaces (adapted from [2])<\/figcaption><\/figure>\n<h2><span data-contrast=\"auto\">NEXT<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h2>\n<p><span data-contrast=\"auto\">The realization of functional data spaces depends on the availability of the three key enablers (data connectors and infrastructure, data interoperability, and data value). On the one hand, major initiatives have been set out to create trust frameworks to provide the means for data exchange at a meta-level (i.e., exchange about the participants and their data, not the data itself). Still, there is work to be done. The lack of common definitions for data spaces and connectors can impede interoperability even at the meta-level. For example, different policy settings languages have been used by different initiatives (e.g., ODRL, IDS Usage control language, LUCON), not to mention the incompatibility between different connector implementations.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">While the work on data connectors and infrastructure for data spaces is not yet finished, the next challenges await around the corner: data interoperability and data value. Concerning data interoperability, initiatives related to semantic data, including the Next Generation Service Interfaces Linked Data [5] and the Smart Data Models Program [6], have contributed to the technological basis for new interoperable data and services. However, most existing data sources and related applications are legacy systems built independently, and semantic interoperability was not considered in their design. Therefore, the integration effort is very high. Matching data from different data sources is a multi-level challenge: As mentioned in [2], it is necessary to find semantic matchings between different ontologies (or vocabularies), between data schemes and ontologies, and between data entities in different organizations. NEC Lab has put forward one example of research work to address these matching challenges with their solution TrioNET [7].<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p>&nbsp;<\/p>\n<figure id=\"attachment_12645\" aria-describedby=\"caption-attachment-12645\" style=\"width: 698px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-12645 size-large\" src=\"https:\/\/www.iese.fraunhofer.de\/blog\/wp-content\/uploads\/2024\/07\/fig2-698x267.png\" alt=\"interoperability problems in data spaces\" width=\"698\" height=\"267\" srcset=\"https:\/\/www.iese.fraunhofer.de\/blog\/wp-content\/uploads\/2024\/07\/fig2-698x267.png 698w, https:\/\/www.iese.fraunhofer.de\/blog\/wp-content\/uploads\/2024\/07\/fig2-400x153.png 400w, https:\/\/www.iese.fraunhofer.de\/blog\/wp-content\/uploads\/2024\/07\/fig2-768x294.png 768w, https:\/\/www.iese.fraunhofer.de\/blog\/wp-content\/uploads\/2024\/07\/fig2-1536x588.png 1536w, https:\/\/www.iese.fraunhofer.de\/blog\/wp-content\/uploads\/2024\/07\/fig2.png 1615w\" sizes=\"auto, (max-width: 698px) 100vw, 698px\" \/><figcaption id=\"caption-attachment-12645\" class=\"wp-caption-text\">Figure 2: Data interoperability problems in data spaces (according to [2]).<\/figcaption><\/figure>\n<h2><span data-contrast=\"auto\">WRAP UP<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h2>\n<p><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"-\" data-font=\"Calibri\" data-listid=\"3\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Calibri&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;-&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Then,<\/span><\/b><span data-contrast=\"auto\"> data spaces were a data management approach that did not focus on data integration. <\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"-\" data-font=\"Calibri\" data-listid=\"3\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Calibri&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;-&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"2\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Now,<\/span><\/b><span data-contrast=\"auto\"> data spaces refer to data management with many stakeholders exchanging data in a trusted environment. Data spaces have three key enabling layers: connectors and infrastructure, data interoperability, and data value. The current development of most data spaces focuses on connectors and infrastructure.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"-\" data-font=\"Calibri\" data-listid=\"3\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Calibri&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;-&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"3\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Next,<\/span><\/b><span data-contrast=\"auto\"> data interoperability must be addressed. Pressing challenges refer to matching mechanisms and the compatibility of connectors.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<h3><span data-contrast=\"auto\">Data spaces at IESE<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h3>\n<p><span data-contrast=\"auto\">We have been involved in the shaping of the current concept of data spaces for many years as participants in the International Dataspaces Association. In 2020, we researched the suitability of Digital Twins in the creation of a data space for the agricultural domain in the Fraunhofer lighthouse project <\/span><a href=\"https:\/\/www.iese.fraunhofer.de\/en\/project\/cognitive-agriculture.html\"><span data-contrast=\"none\">COGNAC<\/span><\/a><span data-contrast=\"auto\">. More recently, <\/span><a href=\"https:\/\/www.iese.fraunhofer.de\/de\/projekt\/x-kit\/gaia-x-agrar.html\"><span data-contrast=\"none\">we investigated the applicability of Gaia-X and EDC technologies to address the interoperability issues in the agricultural domain<\/span><\/a><span data-contrast=\"auto\">. We derived several use cases from this domain and compared what Gaia-X is currently able to support and how this takes interoperability into account. To further tackle the challenge of entity matching, we are currently exploring the use of LLMs to improve interoperability between systems of different organizations in the agricultural domain that adopt distinct standards.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p>&nbsp;<\/p>\n<div class=\"info-box\">\n<p><span style=\"font-size: 1.2rem;\"><span data-contrast=\"auto\"><strong>Do you need conceptual or technological support to build or join a data space?<\/strong> Our experts in the Architecture-Centric Engineering and Security Engineering departments can help you.<\/span> <\/span><a style=\"font-size: 1.2rem;\" href=\"mailto:rodrigo.falcao@iese.fraunhofer.de?Subject=&quot;Blog post: The Pursuit of Smartness in Software-based Systems&quot;\">Get in touch with us<\/a><span style=\"font-size: 1.2rem;\">!<\/span><\/p>\n<\/div>\n<p>&nbsp;<\/p>\n<h3><span data-contrast=\"auto\">References:<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h3>\n<p><span data-contrast=\"auto\">[1] <\/span><span data-contrast=\"none\">Franklin, Michael, Alon Halevy, and David Maier. &#8222;From databases to dataspaces: a new abstraction for information management.&#8220; <\/span><i><span data-contrast=\"none\">ACM Sigmod Record<\/span><\/i><span data-contrast=\"none\"> 34.4 (2005): 27-33.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">[2] Solmaz, G\u00fcrkan, et al. &#8222;Enabling data spaces: Existing developments and challenges.&#8220; <\/span><i><span data-contrast=\"none\">Proceedings of the 1st International Workshop on Data Economy<\/span><\/i><span data-contrast=\"none\">. 2022.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">[3] Gaia-X. \u201cGaia-X: a federated secure data infrastructure\u201d. <\/span><a href=\"https:\/\/gaia-x.eu\/\"><span data-contrast=\"none\">https:\/\/gaia-x.eu\/<\/span><\/a><span data-contrast=\"auto\">. Accessed on March 4, 2024.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">[4] Eclipse Dataspace Components. \u201cEclipse Dataspace Components\u201d. <\/span><a href=\"https:\/\/eclipse-edc.github.io\/docs\/#\/README\"><span data-contrast=\"none\">https:\/\/eclipse-edc.github.io\/docs\/#\/README<\/span><\/a><span data-contrast=\"auto\">. Accessed on March 4, 2024.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">[5] ETSI Industry Specification Group (ISG). \u201cContext Information Management (CIM); NGSI-LD API\u201d. Technical Report. 2021. <\/span><a href=\"https:\/\/www.etsi.org\/deliver\/etsi_gs\/CIM\/001_099\/009\/01.04.01_60\/gs_cim009v010401p.pdf\"><span data-contrast=\"none\">https:\/\/www.etsi.org\/deliver\/etsi_gs\/CIM\/001_099\/009\/01.04.01_60\/gs_cim009v010401p.pdf<\/span><\/a><span data-contrast=\"auto\">. Accessed on March 4, 2024.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">[6] Smart Data Model Program. \u201cSmart data models\u201d. <\/span><a href=\"https:\/\/smartdatamodels.org\/\"><span data-contrast=\"none\">https:\/\/smartdatamodels.org\/<\/span><\/a><span data-contrast=\"auto\">. Accessed on March 4, 2024.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">[7] NEC Laboratories Europe. \u201cData Ecosystems and Standards\u201d. <\/span><a href=\"https:\/\/www.neclab.eu\/research-areas\/system-platforms-for-ai-and-des\/des\"><span data-contrast=\"none\">https:\/\/www.neclab.eu\/research-areas\/system-platforms-for-ai-and-des\/des<\/span><\/a><span data-contrast=\"auto\">. Accessed on March 4, 2024.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The topic of data spaces has gained increased traction in Europe and beyond. In this blog post, we will talk in a compact way about the history of the concept, its current definition, and the significant challenges that lie ahead&#8230;<\/p>\n","protected":false},"author":130,"featured_media":12647,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[211,94],"tags":[198],"coauthors":[555,519,38],"class_list":["post-12643","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-digitale-transformation","category-industrie-4-0","tag-english"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Interoperability in data spaces: then, now, and next - Blog des Fraunhofer IESE<\/title>\n<meta name=\"description\" content=\"Interoperability in data spaces: history of the concept, current definition, and challenges for consolidating the idea in practice\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.iese.fraunhofer.de\/blog\/interoperability-in-data-spaces\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Interoperability in data spaces: then, now, and next - 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Im Jahr 2022 promovierte er an der Technischen Universit\u00e4t Kaiserslautern in Informatik. Sein Schwerpunkt lag dabei auf datengetriebenen Ans\u00e4tzen zur Unterst\u00fctzung der Erhebung kontextbezogener Funktionalit\u00e4ten. Zu seinen Forschungsinteressen geh\u00f6ren Kontextbewusstsein, datengetriebene Kontextmodellierung, empirisches Software Engineering und Interoperabilit\u00e4t. Bevor er zum IESE kam, arbeitete er 15 Jahre lang in der Softwareentwicklungsbranche, wo er u.a. als Projektleiter, Systemanalytiker und Entwickler t\u00e4tig war. Aus diesem Grund liegt ihm die Vorstellung eines Br\u00fcckenschlags zwischen Industrie und Wissenschaft ganz besonders am Herzen. --- Dr. Rodrigo Falc\u00e3o has been a software engineer at Fraunhofer IESE since 2017, where he works as a Senior Architect &amp; Project Manager in the Architecture-Centric Engineering department. In 2022, he completed his PhD in Computer Science at the Technical University of Kaiserslautern, focusing on data-driven approaches to support the elicitation of context-aware functionalities. His research interests include context awareness, data-driven context modeling, empirical software engineering, and interoperability. Before joining IESE, he worked for 15 years in the software development industry, holding roles such as project leader, system analyst, and developer. 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Im Jahr 2022 promovierte er an der Technischen Universit\u00e4t Kaiserslautern in Informatik. Sein Schwerpunkt lag dabei auf datengetriebenen Ans\u00e4tzen zur Unterst\u00fctzung der Erhebung kontextbezogener Funktionalit\u00e4ten. Zu seinen Forschungsinteressen geh\u00f6ren Kontextbewusstsein, datengetriebene Kontextmodellierung, empirisches Software Engineering und Interoperabilit\u00e4t. Bevor er zum IESE kam, arbeitete er 15 Jahre lang in der Softwareentwicklungsbranche, wo er u.a. als Projektleiter, Systemanalytiker und Entwickler t\u00e4tig war. Aus diesem Grund liegt ihm die Vorstellung eines Br\u00fcckenschlags zwischen Industrie und Wissenschaft ganz besonders am Herzen. --- Dr. Rodrigo Falc\u00e3o has been a software engineer at Fraunhofer IESE since 2017, where he works as a Senior Architect &amp; Project Manager in the Architecture-Centric Engineering department. 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