Abstract
In the last two decades, data matching has been addressed for different purposes and in different application contexts, ranging from data integration, to ontology evolution, to semantic data clouding, until more recent exploratory data analysis over large/big datasets. This paper describes the evolution of research activity on matching techniques for data integration and exploration at the ISLab group of the Università degli Studi di Milano. We analyze the matching techniques according to the structure of target data, the algorithmic pattern of the matching process, and the application focus, and we discuss the results of using our techniques for exploratory analysis of a real dataset composed by all the SEBD proceedings publications in the timeframe 1993–2016.
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Notes
- 1.
Data have been collected from the DBLP database (http://dblp.org), except for the year 2013 that is missing from DBLP. 1993 data have been collected from the Scopus DB (https://www.scopus.com).
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Castano, S., Ferrara, A., Montanelli, S. (2018). Matching Techniques for Data Integration and Exploration: From Databases to Big Data. In: Flesca, S., Greco, S., Masciari, E., Saccà, D. (eds) A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years. Studies in Big Data, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-319-61893-7_4
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DOI: https://doi.org/10.1007/978-3-319-61893-7_4
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