Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data

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 including reasoning under uncertainty, uncertain databases and mining uncertain data. For example, statistics and probabilistic reasoning can provide support with models for representing uncertainty. The uncertain database community can provide methods for storing and managing uncertain data, while research in mining uncertain data can provide data analysis tasks 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 to understand 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 uncertain 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. The workshop program is very stimulating and exciting. We are pleased to feature two invited talks by pioneers in mining uncertain data. Christopher Jermaine will give an invited talk titled Managing and Mining Uncertain Data: What Might We Do Better? Matthias Renz will address the topic Querying and Mining Uncertain Data: Methods, Applications, and Challenges. Moreover, 8 accepted papers in 4 full presentations and 4 concise presentations will cover a bunch of interesting topics and on-going research projects about uncertain data mining.

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