inblandas

Inblandas is a hypothetical framework for blending data from heterogeneous sources into a unified representation while preserving data provenance and enabling probabilistic matching. It is used in academic and conceptual discussions to illustrate how multiple datasets can be integrated without forcing rigid, one-size-fits-all schemas.

The central idea behind inblandas is a dedicated blending layer that sits between source data and the consumer applications. This layer handles schema mapping, value normalization, and entity resolution, producing merged records with associated confidence scores. The approach emphasizes transparent lineage, so users can trace how each blended value was derived and updated.

A typical inblandas architecture includes data connectors that ingest diverse formats (for example, CSV, JSON-LD, or relational feeds), a blending core that performs schema alignment and probabilistic linking, and a provenance or policy module that records transformations and access controls. The design favors modularity and interoperability, often supporting standard data representations and APIs such as REST or GraphQL to expose blended results.

Applications for inblandas span digital humanities, open data portals, scientific data integration, and cross-institutional reporting. By enabling flexible, explainable blending, it aims to improve cross-source search, reproducibility, and data completeness while maintaining transparency about uncertainty and data quality.

Critiques focus on complexity, potential performance overhead, and the need for well-defined governance over blending rules. Inblandas remains a conceptual reference point for discussions of data fusion, entity resolution, and provenance-aware data integration.