Achieving data interoperability requires addressing challenges across three key dimensions. Organizationally, effective coordination and collaboration among stakeholders with diverse goals, priorities, and workflows is essential. Clear governance structures must define roles, responsibilities, and accountability. Regulatory and ethical considerations involve addressing privacy, consent, and ownership issues while ensuring compliance with regulations like GDPR. Technical challenges include overcoming disparate data formats, incompatible schemas, and differing standards across systems.
Several approaches have emerged to address these challenges. Metadata standards provide information about data characteristics, helping to organize, find, and understand it. Technical standards for formats and communication protocols facilitate smooth exchange and integration. Data contracts define structure and usage, establishing clear connections between producers and consumers. Data warehouses centralize storage and management, integrating information from various sources into a unified view.
The impact of effective interoperability extends far beyond technical efficiency. It supports sustainable urban development by enabling more informed decision-making and resource allocation. It improves public services by facilitating the integration of diverse information sources to create more responsive and citizen-centric solutions. It fosters economic growth through data-driven innovation and collaboration across different sectors and stakeholders.
As we move forward, pursuing interoperability requires continued investment in technological solutions, governance frameworks, and stakeholder engagement. By addressing all dimensions comprehensively, we can create systems that exchange data in ways that are secure, ethical, and beneficial to all involved. The ultimate goal is harnessing the full potential of our data resources to create more efficient, sustainable, and livable communities for the future.
D4A Publications on interperability
References to academic publications
Technical
Akhter, R., & Sofi, S. A. (2022). Precision agriculture using IoT data analytics and machine learning.
Aksu, D., & Aydin, M. A. (2019). A survey of IoT architectural reference models.
Albouq, S. S., Abi Sen, A. A., Almashf, N., Yamin, M., Alshanqiti, A., & Bahbouh, N. M. (2022). A survey of interoperability challenges and solutions for dealing with them in IoT environment.
Aumueller, D., Do, H., Massmann, S., & Rahm, E. (2005). _Schema and ontology matching with COMA++.
Azimirad, E., Haddadnia, J., & Izadipour, A. (2015). A comprehensive review of multi-sensor data fusion architectures.
Balasubramani, B. S., & Cruz, I. F. (2019). Spatial data integration.
Belhiah, M., & Bounabat, B. (2017). A user-centered model for assessing and improving open government data quality.
Boniotti, G., Cocca, P., Marciano, F., Marini, A., Stefana, E., & Vernuccio, F. (2021). A conceptual reference model for smart factory production data.
Bonifati, A., & Ileana, I. (2019). Graph data integration and exchange.
Crusoe, J., Simonofski, A., Clarinval, A., & Gebka, E. (2019). The impact of impediments on open government data use: Insights from users.
Dai, W., Wang, P., Sun, W., Wu, X., Zhang, H., Vyatkin, V., & Yang, G. (2019). Semantic integration of plug-and-play software components for industrial edges based on microservices.
FIWARE.
Frimpong, R. A. (2017). Ontology matching algorithms for data model alignment in big data.
Gal, A. (2019). Uncertain schema matching.
Gebka, E., & Castiaux, A. (2021). A typology of municipalities' roles and expected users' roles in open government data release and reuse.
Guo, J. (2014). SDF: A sign description framework for cross-context information resource representation and interchange.
Hazra, A., Adhikari, M., Amgoth, T., & Satish, N. (2023). A comprehensive survey on interoperability for IIoT: Taxonomy, standards, and future directions.
Janssen, M., Estevez, E., & Janowski, T. (2014). Interoperability in big, open, and linked data—organizational maturity, capabilities, and data portfolios.
Kadadi, A., Agrawal, R., Nyamful, C., & Atiq, R. (2014). Challenges of data integration and interoperability in big data.
Kashyap, V., & Sheth, A. (1996). Semantic and schematic similarities between database objects: A context-based approach.
Khatoon, P. S., & Ahmed, M. (2022). Importance of semantic interoperability in smart agriculture systems.
Lu, J., Yang, L. T., Guo, B., Li, Q., Su, H., & Li, G. (2022). A sustainable solution for IoT semantic interoperability: Dataspaces model via distributed approaches.
Louge, T., Karray, M.-H., & Archimède, B. (2022). Using adaptive logics for expression of context and interoperability in DL ontologies.
Maratsi, M. I., Ali, M., Alexopoulos, C., Saxena, S., & Rizun, N. (2023). Analyzing open government high-value datasets: Availability, publishers’ contribution, and technical specifications.
Meddeb, A. (2016). Internet of things standards: Who stands out from the crowd?
Milic, P., Veljkovic, N., & Stoimenov, L. (2021). Comparative analysis of metadata models on e-government open data platforms.
Nagarajan, M., Verma, K., Sheth, A., & Miller, J. (2006). Semantic interoperability of web services: Challenges and experiences.
Nikiforova, A., Alexopoulos, C., Rizun, N., & Ciesielska, M. (2023). Identification of high-value dataset determinants: Is there a silver bullet for efficient sustainability-oriented data-driven development?
Nikiforova, A., Rizun, N., Ciesielska, M., Alexopoulos, C., & Miletić, A. (2023). Towards high-value datasets determination for data-driven development: A systematic literature review.
Nilsson, J. (2022). Machine learning concepts for service data interoperability.
OMA. (2012). Next generation service interfaces architecture.
Papotti, P. (2019). Schema mapping.
Raça, V., Veljković, N., Velinov, G., Stoimenov, L., & Kon-Popovska, M. (2020). Real-time monitoring and assessing open government data: A case study of the Western Balkan countries.
Radzio, F., & Sjut, B. (2022). Übersicht Plattform Mobility Live Access.
Rahm, E., & Do, H. (2000). Data cleaning: Problems and current approaches.
Rahm, E., & Peukert, E. (2019). Holistic schema matching.
Rejeb, A., et al. (2022). Charting past, present, and future research in the semantic web and interoperability.
Sachdeva, S., & Bhalla, S. (2022). Using knowledge graph structures for semantic interoperability in electronic health records data exchanges.
Singh, P., & van Sinderen, M. J. (2018). Big data interoperability challenges for logistics.
Song, S., Zhang, X., & Qin, G. (2017). Multi-domain ontology mapping based on semantics.
Sony, P., & Sureshkumar, N. (2022). Semantic interoperability model in healthcare Internet of Things using healthcare sign description framework.
Thangaraj, M., et al. (2016). Agent-based semantic Internet of Things (IoT) in smart healthcare.
Ursino, D., Takama, Y., & Castanedo, F. (2013). A review of data fusion techniques.
Verhagen, M., et al. (2015). The LAPPS interchange format.
Wasserman, A. I. (1990). Tool integration in software engineering environments.
Organizational
Albouq, S. S., Abi Sen, A. A., Almashf, N., Yamin, M., Alshanqiti, A., & Bahbouh, N. M. (2022). A survey of interoperability challenges and solutions for dealing with them in IoT environment.
Belhiah, M., & Bounabat, B. (2017). A user-centered model for assessing and improving open government data quality.
Bertolini, M., Mezzogori, D., Neroni, M., & Zammori, F. (2021). Machine learning for industrial applications: A comprehensive literature review.
Bokolo, A. J., Petersen, S. A., Ahlers, D., & Krogstie, J. (2021). Big data driven multi-tier architecture for electric mobility as a service in smart cities: A design science approach.
Ciuriak, D. (2020). Economic rents and the contours of conflict in the data-driven economy.
Crusoe, J., Simonofski, A., Clarinval, A., & Gebka, E. (2019). The impact of impediments on open government data use: Insights from users.
De Carolis, A., Macchi, M., Negri, E., & Terzi, S. (2017). Guiding manufacturing companies towards digitalization: A methodology for supporting manufacturing companies in defining their digitalization roadmap.
Erdem, A., & Şeker, F. (2021). Tourist experience and digital transformation.
Gebka, E., & Castiaux, A. (2021). A typology of municipalities' roles and expected users' roles in open government data release and reuse.
George, G., Merrill, R. K., & Schillebeeckx, S. J. D. (2021). Digital sustainability and entrepreneurship: How digital innovations are helping tackle climate change and sustainable development.
Ghoreishi, M. (2023). The role of digital technologies in a data-driven circular business model: A systematic literature review.
Hummel, P., Braun, M., Tretter, M., & Dabrock, P. (2021). Data sovereignty: A review.
Janssen, M., Estevez, E., & Janowski, T. (2014). Interoperability in big, open, and linked data—organizational maturity, capabilities, and data portfolios.
Kraus, S., Jones, P., Kailer, N., Weinmann, A., Chaparro-Banegas, N., & Roig-Tierno, N. (2021). Digital transformation: An overview of the current state of the art of research.
Maroufkhani, P., Desouza, K. C., Perrons, R. K., & Iranmanesh, M. (2022). Digital transformation in the resource and energy sectors: A systematic review.
Matt, C., Hess, T., Benlian, A., & Wiesböck, F. (2016). Options for formulating a digital transformation strategy.
Nikiforova, A., Rizun, N., Ciesielska, M., Alexopoulos, C., & Miletić, A. (2023). Towards high-value datasets determination for data-driven development: A systematic literature review.
Philip, J. (2021). Viewing digital transformation through the lens of transformational leadership.
Psara, K., Papadimitriou, C., Efstratiadi, M., Tsakanikas, S., Papadopoulos, P., & Tobin, P. (2022). European energy regulatory, socioeconomic, and organizational aspects: An analysis of barriers related to data-driven services across electricity sectors.
Quach, S., Thaichon, P., Martin, K. D., Weaven, S., & Palmatier, R. W. (2022). Digital technologies: Tensions in privacy and data.
Radzio, F., & Sjut, B. (2022). Übersicht Plattform Mobility Live Access.
Vogelsang, K., Liere-Netheler, K., Packmohr, S., & Hoppe, U. (2019). Barriers to digital transformation in manufacturing: Development of a research agenda.
Zaoui, F., & Souissi, N. (2020). Roadmap for digital transformation: A literature review.
Regulatory
Ciuriak, D. (2020). Economic rents and the contours of conflict in the data-driven economy.
George, G., Merrill, R. K., & Schillebeeckx, S. J. D. (2019). Digital sustainability and entrepreneurship: How digital innovations are helping tackle climate change and sustainable development.
Ghoreishi, M. (2023). The role of digital technologies in a data-driven circular business model: A systematic literature review.
Hummel, P., Braun, M., Tretter, M., & Dabrock, P. (2021). Data sovereignty: A review.
Maroufkhani, P., Desouza, K. C., Perrons, R. K., & Iranmanesh, M. (2022). Digital transformation in the resource and energy sectors: A systematic review.
Ethical
Cagnazzo, C. (2021). The thin border between individual and collective ethics: The downside of GDPR.
Harris, D., Samuel, S., & Probert, E. (2018). GDPR confusion.
Peloquin, D., DiMaio, M., Bierer, B., & Barnes, M. (2020). Disruptive and avoidable: GDPR challenges to secondary research uses of data.
Staunton, C., Slokenberga, S., & Mascalzoni, D. (2019). The GDPR and the research exemption: Considerations on the necessary safeguards for research biobanks.
Van de Vyvere, B., & Colpaert, P. (2022). Using ANPR data to create an anonymized linked open dataset on urban bustle.