In the "big data" era, data integration is a popular activity both in
academia and in industry. Integrating hundreds of heterogeneous
sources on a daily basis requires a great amount of manual work in
order to have data that is polished enough to be useful in the final
applications, such as querying and mining. The problem is ever harder in practice, as data is often dirty in nature because of typos, duplicates, and so on, that can lead to poor results in the analytic tasks.

Over the last ten years, several successful systems have been proposed to tackle this challenge with a formal, declarative approach based on first order logic. However, despite the positive results, there is still a gap between these proposals and the leading commercial systems. The latter are harder to maintain, to debug, and to test, but provide the level of personalization and detail that are needed to solve “real-world” problems. In this talk, I will describe some of my results in tackling mapping and cleaning with a declarative approach, and how this experience has pushed me to explore a new way that can take the best of both worlds.

Short Bio:

Paolo Papotti is a scientist in the Data Analytics center at Qatar
Computing Research Institute (QCRI). He holds a Ph.D degree in
computer science from Roma Tre University (Italy, 2007), where he also was Assistant Professor before joining QCRI. He had visiting appointments at IBM Almaden (USA) and at the UC Santa Cruz (USA). His research topics are in the general area of information integration and data quality.