III: Small: Rural: Querying Rich Uncertain Data in Real Time

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Nowadays, a burgeoning amount of data is made available continuously. Example sources include various sensors, RFID, computer network traffic, phone conversations, and web searches. Much of this data is noisy, inconsistent, or even erroneous. Compared to deterministic data, uncertain data carries more information. Forcing uncertain data to be deterministic (e.g., by taking the expectations) can cause significant information loss in query results, possibly leading to wrong judgments for queries that aid decision making. Moreover, real-time decisions based on these data can have a significant impact on the quality of life, on the economy, and on our security. However, fast and real-time processing of uncertain data is a difficult problem. The broad goal of the RURAL (querying Rich Uncertain data in ReAL time) project is to provide techniques that make this query processing task feasible. To achieve this goal the project includes: (1) integrating data cleansing, distribution learning, and query processing through pipelining; (2) developing fast and online query processing algorithms on uncertain data; (3) providing a rich treatment of uncertain data distributions including compression, sharing, and gauging their reliability; (4) using predictive models to infer uncertain data distributions; and (5) answering top-k queries with consideration of typicality of results.The RURAL project extends the state of art in real-time query processing of uncertain data for decision-making. It is used by the Kentucky Transportation Center for the application of dynamic traffic routing and control, and by the Biomedical Division at the University of Kentucky for real-time monitoring applications. The research results are integrated with education through class projects in Computer Science graduate courses at the University of Kentucky and tutorials at conferences. Further information on the project can be found on the project web page: http://protocols.netlab.uky.edu/~ge/projects/rural.