Data Management & Integration
Unlocking the potential of digital twins through promoting the adoption of best practices in data management and integration.
Research Area 7 (RA7) will provide guidance and expertise with data challenges in projects initiated by other RAs, and help in identifying opportunities for data sharing and reuse. Data management and integration is not an end in itself - it is important that data management and integration research is relevant to the needs of stakeholders who are generating data and working with data, so we envisage working closely with other research areas and stakeholders to gain a better understanding of their data sets and their needs. RA7 research activities in connection with these projects could involve designing new data models, linking diverse data sets or exploring the utility of advanced database technology for specific applications.We recognise that it will be necessary to work with both the technical and the social challenges of data integration. On the technical side, selection of data models and standards, particularly for metadata are important. Connecting data from different sources can involve schema matching and data linking. Semantic Web technologies and ontologies can play a role in this. On the social side we need to understand stakeholders’ attitudes to data and data sharing. Who generates data? Who else uses these data? To what extent are data valued by various users and stakeholders? To what extent are data sets trusted? Data quality and integrity can be enhanced through curation and annotation - do those providing data have the necessary motivation or incentives to deliver data that can be used conveniently and with confidence by others? Recording the provenance of data is necessary to enable others to trust the data.
Computer Science and Engineering, Chalmers