Popularity Driven Data Integration
September 28, 2022 Β· Declared Dead Β· π Iberoamerican Conference on Knowledge Graphs and Semantic Web
"No code URL or promise found in abstract"
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Authors
Fausto Giunchiglia, Simone Bocca, Mattia Fumagalli, Mayukh Bagchi, Alessio Zamboni
arXiv ID
2209.14049
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.DB,
cs.DL
Citations
5
Venue
Iberoamerican Conference on Knowledge Graphs and Semantic Web
Last Checked
4 months ago
Abstract
More and more, with the growing focus on large scale analytics, we are confronted with the need of integrating data from multiple sources. The problem is that these data are impossible to reuse as-is. The net result is high cost, with the further drawback that the resulting integrated data will again be hardly reusable as-is. iTelos is a general purpose methodology aiming at minimizing the effects of this process. The intuition is that data will be treated differently based on their popularity: the more a certain set of data have been reused, the more they will be reused and the less they will be changed across reuses, thus decreasing the overall data preprocessing costs, while increasing backward compatibility and future sharing
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