Big Data Curation

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The wide-spread availability of vast amounts of structured data has made it possible to adopt algorithmic and statistical methods to solve data integration problems in new ways. A 2009 report indicated there were already over 100 million structured data tables on the Web. Efforts like Google's Fusion Tables make it easy to publish and share structured and semi-structured data. However, we are far from being able to automatically integrate and align this data so that we can easily use these rich data sources collectively in query answering, decision making, and prediction. Semantic integration at web-scale is a "grand challenge" problem. There is emerging evidence that some of the nuanced semantic differences that eluded enterprise-scale integration solutions, can now be discovered and resolved using knowledge from combinations of web data sources, some which have been curated by incentivized user communities who value the creation of reliable data. The deep heterogeneity and dynamic nature of this data demands new methods for not only alignment, but also for tracing the provenance of alignment decisions, and for maintaining and publishing consistent, properly aligned data. In this work, we will develop new methods for big data curation. Data curation involves the preparation of data for re-use or publishing, the proper modeling of context and semantics, and more generally the care of data to make it of maximal value to users. For big data, we must automate curation tasks to the extent possible and recognize the value of reusing data curated by user communities (such as open ontologies) to enhance and guide automation when publishing new data sources.