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 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.