Curating Uncertainty for Reliable Exploitation and Collaboration (CURE-C)

Incomplete, noisy, or otherwise uncertain data is the way it is, no purely computational method can magically fix it. Numerous techniques developed by the database and programming languages communities exist to help users cope with uncertain data. Specifically, these tools allow users to define business logic over uncertain (i.e., incomplete, possibilistic, or probabilistic) data as if it were idealized deterministic data. The tool instruments the business logic (e.g., a relational database query) according to a model of uncertainty in the source data, to derive a model of uncertainty over the outputs. Tools for uncertain data management are crucial for working with real world data, but suffer from a range of usability challenges that this proposal aims to address.Curating Uncertainty for Reliable Exploitation and Collaboration (CURE-C), developed by XAnalytix Systems and University at Buffalo will solve this problem by building around a collaborative data store that manages both raw and curated data. Sensor data is ingested into the data store, where individuals that we refer to as Curators examine the data, address problems with it, and develop uncertainty profiles for the data. These efforts, once completed are recorded into a workflow and used to process new data as it arrives. These workflows apply the uncertainty profiles to the data, resulting in uncertainty-annotated data. Uncertainty profiles developed by the curators are also saved in a profile store to assist in future curation efforts. Planners can then pose queries over the uncertainty-annotated data, and receive results with both quantitative and qualitative uncertainty descriptions.At the completion of Phase I CURE-C will have a set of interfaces that streamline the development of uncertainty profiles by facilitating the re-use of previously developed profiles.nbsp; CURE-C will also have an integrated analysis of multiple classes of uncertainty.nbsp;