Dataspaces

We use the term dataspace to describe a virtual environment that accommodates semantically enriched data. Like a database, a dataspace stores data persistently. However, it goes beyond traditional databases by using an ontology to provide semantic context, enhancing understanding, accessibility, and ensuring traceability and reproducibility across complex data workflows.

To support organizations in managing advanced knowledge bases, we developed the Dataspace Management System (DSMS). This comprehensive technology stack powers dataspace solutions by leveraging the knowledge embedded in diverse and heterogeneous data sources, turning raw information into actionable insights and enabling the discovery of new knowledge. A key part of our mission is to make this powerful yet complex technology intuitive and accessible, bringing it closer to engineers and domain experts through user-friendly interfaces and practical integration into existing workflows.

Our approach is specifically designed to meet the unique needs of the materials science and manufacturing fields. It ensures that data is structured and organized in accordance with the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. These principles guide the management of data to maximize its usability and value.

Through our dataspace solutions, we provide a range of capabilities, including data integration, exploration, visualization, processing, and sharing. Additionally, we offer expert consulting services to assist engineers and researchers in making informed decisions, designing innovative solutions, and optimizing processes. By delivering these services, we help organizations transform their data into a strategic asset for solving their most critical problems to achieve their goals.

 

Data integration

In industrial environments, data is typically stored across files and databases, using diverse formats and schemas. This heterogeneity poses significant challenges when trying to access, interpret, and connect data, particularly for advanced tasks such as semantic search, complex querying, or applying AI-based analytics.

Our data integration solutions address these challenges by harmonizing data from various sources and formats, creating a shared semantic framework that supports consistent interpretation across systems and domains. This unified view enables deeper insights and prepares the ground for scalable, data-driven applications.

Recognizing the importance of data sovereignty, we offer flexible deployment models that give organizations full control over their data. Whether through secure data transfer or by deploying our solutions directly on-premise or in private cloud environments, we ensure that integration happens in a way that respects internal policies, IP protection, and regulatory requirements.

The process typically begins by jointly identifying the most critical data integration needs. We then transform and structure the relevant data as a foundational step. Building on this, targeted applications and tools are introduced to extract deeper insights and address specific challenges. In many cases, these integrated solutions not only solve immediate problems but also serve as a robust foundation for future use cases and continuous innovation.
 

Data exploration, analysis and added-value creation

A harmonized data representation unlocks new opportunities for discovering and querying data, enabling meaningful analysis. To support this, we provide built-in tools that empower organizations to locate and utilize their data to solve problems effectively.

We have developed a Python SDK client package that significantly simplifies access to the dataspace. Users can write code to derive new insights and contribute these back into the dataspace, thereby expanding the system’s overall knowledge. For advanced users, a SPARQL interface is available to facilitate complex queries against the knowledge graph.

To ensure data security and control, we support access rights management. This feature allows users to define who can access and use their data, which is beneficial for both internal organizational needs and external collaborations.

We align our approach with leading initiatives designed to enhance connectivity and data ecosystem standards, such as GAIA-X, which aspires to enable a federated and secure European data infrastructure; Catena-X, which focuses on data exchange across the automotive value chain; and the Eclipse Dataspace Components (EDC), which offer open-source solutions for building and managing data spaces. Recognizing that these ecosystems are still maturing, we focus on providing companies with practical, future-ready tools that simplify integration, support interoperability, and remain adaptable to evolving requirements.