Adaptive schema databases

The rigid schemas of classical relational databases help users in specifying queries and inform the storage organization of data. However, the advantages of schemas come at a high upfront cost through schema and ETL process design. In this work, we propose a new paradigm where the database system takes a more active role in schema development and data integration. We refer to this approach as adaptive schema databases (ASDs). An ASD ingests semi-structured or unstructured data directly using a pluggable combination of extraction and data integration techniques. Over time it discovers and adapts schemas for the ingested data using information provided by data integration and information extraction techniques, as well as from queries and user-feedback. In contrast to relational databases, ASDs maintain multiple schema workspaces that represent individualized views over the data, which are fine-tuned to the needs of a particular user or group of users. A novel aspect of ASDs is that probabilistic database techniques are used to encode ambiguity in automatically generated data extraction workflows and in generated schemas. ASDs can provide users with context-dependent feedback on the quality of a schema, both in terms of its ability to satisfy a user’s queries, and the quality of the resulting answers. We outline our vision for ASDs, and present a proof of concept implementation as part of the Mimir probabilistic data curation system.