Generating Unified Famous Objects (UFOs) from the classified object tables

It is difficult to access data generated by different data sources due to the representation and format differences. ETL, KETL, Jedox, Apatar are some examples of data translation and fusion packages that can be used to resolve representation differences of data coming from different data sources have been favored. However, most tools require significant manual effort to map the data sources. Structural mismatch of data between objects with the same semantics reduces the accessibility of data. Here we discuss our initial efforts toward a scalable unsu-pervised system and algorithms to generate Unified Famous Objects (UFO) - the self-learning “intelligent” data structures that help automate data fusion at scale [Gubanov et al., 2009], [Gubanov et al., 2011]. UFO is a data structure encapsulating different representations of the same data object (e.g. Songs), hence capable of automatically recognizing and mapping such object in different data sources, and significantly reducing manual effort during data integration process. We evaluate our algorithms on a large-scale Web tables corpus having ≈ 64 million of tuples.