Incremental schema integration for data wrangling via knowledge graphs
Virtual data integration is the current approach to go for data wrangling in data-driven decision-making. In this paper, we focus on automating schema integration, which extracts a homogenised representation of the data source schemata and integrates them into a global schema to enable virtual data...
| Autores: | , , , , , , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2023 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositório: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglês |
| OAI Identifier: | oai:upcommons.upc.edu:2117/390416 |
| Acesso em linha: | https://hdl.handle.net/2117/390416 https://dx.doi.org/10.3233/SW-233347 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Big data Decision-making Schema integration Bootstrapping Virtual data integration Dades massives Decisió, Presa de Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació |
| Resumo: | Virtual data integration is the current approach to go for data wrangling in data-driven decision-making. In this paper, we focus on automating schema integration, which extracts a homogenised representation of the data source schemata and integrates them into a global schema to enable virtual data integration. Schema integration requires a set of well-known constructs: the data source schemata and wrappers, a global integrated schema and the mappings between them. Based on them, virtual data integration systems enable fast and on-demand data exploration via query rewriting. Unfortunately, the generation of such constructs is currently performed in a largely manual manner, hindering its feasibility in real scenarios. This becomes aggravated when dealing with heterogeneous and evolving data sources. To overcome these issues, we propose a fully-fledged semi-automatic and incremental approach grounded on knowledge graphs to generate the required schema integration constructs in four main steps: bootstrapping, schema matching, schema integration, and generation of system-specific constructs. We also present NextiaDI, a tool implementing our approach. Finally, a comprehensive evaluation is presented to scrutinize our approach. |
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