Matches in SemOpenAlex for { <https://semopenalex.org/work/W4380088341> ?p ?o ?g. }
- W4380088341 endingPage "38" @default.
- W4380088341 startingPage "1" @default.
- W4380088341 abstract "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 Nextia DI , a tool implementing our approach. Finally, a comprehensive evaluation is presented to scrutinize our approach." @default.
- W4380088341 created "2023-06-10" @default.
- W4380088341 creator A5002297844 @default.
- W4380088341 creator A5013379001 @default.
- W4380088341 creator A5034865842 @default.
- W4380088341 creator A5040972086 @default.
- W4380088341 creator A5067693515 @default.
- W4380088341 creator A5077515264 @default.
- W4380088341 creator A5090054794 @default.
- W4380088341 date "2023-06-08" @default.
- W4380088341 modified "2023-09-28" @default.
- W4380088341 title "Incremental schema integration for data wrangling via knowledge graphs" @default.
- W4380088341 cites W1483205977 @default.
- W4380088341 cites W1549729095 @default.
- W4380088341 cites W1586198805 @default.
- W4380088341 cites W1978646358 @default.
- W4380088341 cites W1992673035 @default.
- W4380088341 cites W2004817090 @default.
- W4380088341 cites W2010008011 @default.
- W4380088341 cites W2010850723 @default.
- W4380088341 cites W2011492121 @default.
- W4380088341 cites W2029554959 @default.
- W4380088341 cites W2036544516 @default.
- W4380088341 cites W2043420170 @default.
- W4380088341 cites W2044102377 @default.
- W4380088341 cites W2096902478 @default.
- W4380088341 cites W2118100588 @default.
- W4380088341 cites W2131165032 @default.
- W4380088341 cites W2143677795 @default.
- W4380088341 cites W2148354277 @default.
- W4380088341 cites W2275216626 @default.
- W4380088341 cites W2293289691 @default.
- W4380088341 cites W2299775049 @default.
- W4380088341 cites W2406114359 @default.
- W4380088341 cites W2481124104 @default.
- W4380088341 cites W2611299143 @default.
- W4380088341 cites W2613666425 @default.
- W4380088341 cites W2750126764 @default.
- W4380088341 cites W2838709227 @default.
- W4380088341 cites W2945852147 @default.
- W4380088341 cites W2967736654 @default.
- W4380088341 cites W2981075922 @default.
- W4380088341 cites W3016734568 @default.
- W4380088341 cites W3097905336 @default.
- W4380088341 cites W3099478832 @default.
- W4380088341 cites W3123800493 @default.
- W4380088341 cites W3157618408 @default.
- W4380088341 cites W3198620528 @default.
- W4380088341 cites W4247756142 @default.
- W4380088341 cites W4286432951 @default.
- W4380088341 cites W4294106603 @default.
- W4380088341 cites W4300456194 @default.
- W4380088341 cites W4301500808 @default.
- W4380088341 cites W573124679 @default.
- W4380088341 cites W3043548488 @default.
- W4380088341 doi "https://doi.org/10.3233/sw-233347" @default.
- W4380088341 hasPublicationYear "2023" @default.
- W4380088341 type Work @default.
- W4380088341 citedByCount "0" @default.
- W4380088341 crossrefType "journal-article" @default.
- W4380088341 hasAuthorship W4380088341A5002297844 @default.
- W4380088341 hasAuthorship W4380088341A5013379001 @default.
- W4380088341 hasAuthorship W4380088341A5034865842 @default.
- W4380088341 hasAuthorship W4380088341A5040972086 @default.
- W4380088341 hasAuthorship W4380088341A5067693515 @default.
- W4380088341 hasAuthorship W4380088341A5077515264 @default.
- W4380088341 hasAuthorship W4380088341A5090054794 @default.
- W4380088341 hasBestOaLocation W43800883411 @default.
- W4380088341 hasConcept C124101348 @default.
- W4380088341 hasConcept C138496976 @default.
- W4380088341 hasConcept C148840519 @default.
- W4380088341 hasConcept C153440673 @default.
- W4380088341 hasConcept C154690210 @default.
- W4380088341 hasConcept C15744967 @default.
- W4380088341 hasConcept C165611974 @default.
- W4380088341 hasConcept C199360897 @default.
- W4380088341 hasConcept C23123220 @default.
- W4380088341 hasConcept C2777327318 @default.
- W4380088341 hasConcept C2780660560 @default.
- W4380088341 hasConcept C29275276 @default.
- W4380088341 hasConcept C30775581 @default.
- W4380088341 hasConcept C41008148 @default.
- W4380088341 hasConcept C52146309 @default.
- W4380088341 hasConcept C56310702 @default.
- W4380088341 hasConcept C72634772 @default.
- W4380088341 hasConceptScore W4380088341C124101348 @default.
- W4380088341 hasConceptScore W4380088341C138496976 @default.
- W4380088341 hasConceptScore W4380088341C148840519 @default.
- W4380088341 hasConceptScore W4380088341C153440673 @default.
- W4380088341 hasConceptScore W4380088341C154690210 @default.
- W4380088341 hasConceptScore W4380088341C15744967 @default.
- W4380088341 hasConceptScore W4380088341C165611974 @default.
- W4380088341 hasConceptScore W4380088341C199360897 @default.
- W4380088341 hasConceptScore W4380088341C23123220 @default.
- W4380088341 hasConceptScore W4380088341C2777327318 @default.
- W4380088341 hasConceptScore W4380088341C2780660560 @default.
- W4380088341 hasConceptScore W4380088341C29275276 @default.
- W4380088341 hasConceptScore W4380088341C30775581 @default.