Data Integration Beyond Alignments Between Two Sources

  title={Data Integration Beyond Alignments Between Two Sources},
  author={Gerard de Melo},
New data sources are appearing every day. In data integration, one often merges two sources by first computing similarity scores between items, and then selecting a 1-to-1 alignment of maximal weight, e.g. via the Hungarian algorithm for bipartite matching. Often, however, we need to operate on n > 2 sources and go beyond strict 1-to-1 alignments. Instead, we may consider arbitrary weighted links indicating possible identity, as well as one or more groups of sets of items indicating likely… CONTINUE READING