Another Word For It

Constraint-Based XML Query Rewriting for Data Integration by Cong Yu and Lucian Popa.

Abstract:

We study the problem of answering queries through a target schema, given a set of mappings between one or more source schemas and this target schema, and given that the data is at the sources. The schemas can be any combination of relational or XML schemas, and can be independently designed. In addition to the source-to-target mappings, we consider as part of the mapping scenario a set of target constraints specifying additional properties on the target schema. This becomes particularly important when integrating data from multiple data sources with overlapping data and when such constraints can express data merging rules at the target. We define the semantics of query answering in such an integration scenario, and design two novel algorithms, basic query rewrite and query resolution, to implement the semantics. The basic query rewrite algorithm reformulates target queries in terms of the source schemas, based on the mappings. The query resolution algorithm generates additional rewritings that merge related information from multiple sources and assemble a coherent view of the data, by incorporating target constraints. The algorithms are implemented and then evaluated using a comprehensive set of experiments based on both synthetic and real-life data integration scenarios.

Who does this sound like?:

Data merging is notoriously hard for data integration and often not dealt with. Integration of scientific data, however, offers many complex scenarios where data merging is required. For example, proteins (each with a unique protein id) are often stored in multiple biological databases, each of which independently maintains different aspects of the protein data (e.g., structures, biological functions, etc.). When querying on a given protein through a target schema, it is important to merge all its relevant data (e.g., structures from one source, functions from another) given the constraint that protein id identifies all components of the protein.

When target constraints are present, it is not enough to consider only the mappings for query answering. The target instance that a query should “observe” must be defined by the interaction between all the mappings from the sources and all the target constraints. This interaction can be quite complex when schemas and mappings are nested and when the data merging rules can enable each other, possibly, in a recursive way. Hence, one of the first problems that we study in this paper is what it means, in a precise sense, to answer the target queries in the “best” way, given that the target instance is specified, indirectly, via the mappings and the target constraints. The rest of the paper will then address how to compute the correct answers without materializing the full target instance, via two novel algorithms that rewrite the target query into a set of corresponding source queries.

Wrong!

The ACM reports sixty-seven (67) citations of this paper as of today. (Paper published in 2004.) Summaries of any of the citing literature welcome!

The question of data integration persists to this day. I take that to indicate that whatever the merits of this approach, data integration issues remain unsolved.

What are the merits/demerits of this approach?