Random sampling is an appealing approach to build synopses of large data streams because random samples can be used for a broad spectrum of analytical tasks. Users are often inter...
Real-world data -- especially when generated by distributed measurement infrastructures such as sensor networks -- tends to be incomplete, imprecise, and erroneous, making it impo...
An intelligent agent will often be uncertain about various properties of its environment, and when acting in that environment it will frequently need to quantify its uncertainty. ...
Abstract-- Despite of advances in machine learning technologies, a schema matching result between two database schemas (e.g., those derived from COMA++) is likely to be imprecise. ...
Programs written in type-unsafe languages such as C and C++ incur costly memory errors that result in corrupted data structures, program crashes, and incorrect results. We present...
Karthik Pattabiraman, Vinod Grover, Benjamin G. Zo...