The importance of uncertain data is growing quickly in many essential applications such as environmental surveillance, mobile object tracking and data integration. Recently, storing, collecting, processing, and analyzing uncertain data has attracted increasing attention from both academia and industry.

Analyzing and mining uncertain data needs collaboration and joint effort from multiple research communities. The focus of this workshop is to bring together and bridge research in reasoning under uncertainty, probabilistic databases and mining uncertain data. Work in statistics and probabilistic reasoning can provide support with models for representing uncertainty, work in the probabilistic database community can provide methods for storing and managing uncertain data, while work in the mining uncertain data can provide data analysis task and methods. It is important to build connections among those communities to tackle the overall problem of analyzing and mining uncertain data.

There are many common challenges among the communities. One is understanding the different modeling assumptions made, and how they impact the methods, both in terms of accuracy and efficiency. Different researchers hold different assumptions and this is one of the major obstacles in the research of mining uncertain data. Another is the scalability of proposed management and analysis methods. Finally, to make analysis and mining useful and practical, we need real data sets for testing. Unfortunately, uncertain data sets are often hard to get.

The goal of the First ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data (U'09) is to discuss in depth the challenges, opportunities and techniques on the topic of analyzing and mining uncertain data. The theme of this workshop is to make connections among the research areas of probabilistic databases, probabilistic reasoning, and data mining, as well as to build bridges among the aspects of models, data, applications, novel mining tasks and effective solutions. By making connections among different communities, we aim at understanding each other in terms of scientific foundation as well as commonality and differences in research methodology.