Schema Mapping Generation in the Wild: A Demonstration with Open Government Data

Mihaela Mazilu, Nikolaos Konstantinou, Norman Paton, Alvaro Fernandes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Schema mapping generation identifies how data sets can be combined to create views that are relevant to an application. Where the data sets to be combined lack declared relationships, such as foreign keys, schema mapping generation can be considered to
be in the wild. In this paper, we describe an approach to schema mapping generation in the context of open government data, in particular, the London Datastore. Mapping generation is informed by inferred profiling data about the data sets and their relationships, where the data sets are made available as csv files. We outline the mapping generation algorithm, and describe a demonstration of the approach, in which the user can: (i) specify the target to be populated by the generated mappings over a collection of sources from The London Datastore; (ii) browse the generated candidate mappings and the evidence that informed their creation; and (iii) steer the mapping generation process, to make use of preferred sources and dependable profiling results.
Original languageEnglish
Title of host publicationProceedings of the 23rd International Conference on Extending Database Technology (EDBT)
DOIs
Publication statusPublished - 2020
Event23rd International Conference on Extending Database Technology - Copenhagen, Denmark
Duration: 30 Mar 20202 Apr 2020

Conference

Conference23rd International Conference on Extending Database Technology
Country/TerritoryDenmark
CityCopenhagen
Period30/03/202/04/20

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