III: Small: Efficient Ranking and Aggregate Query Processing for Probabilistic Data

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When dealing with massive quantities of data, ranking and aggregatequeries are powerful techniques for focusing attention on the mostimportant answers. Many applications that produce such massivequantities of data inherently introduce uncertainty in the same time,for example, probabilistic match in data integration, imprecisemeasurements from sensors, fuzzy duplicates in data cleaning,inconsistency in scientific data. Hence, the importance of thesequeries is even greater in probabilistic data, where a relation canencode exponentially many possible worlds. Uncertainty opens the gateto many possible definitions for ranking and aggregate queries. Thisproject systematically examines the underlying properties associatedwith the rich semantics of ranking and aggregate queries for largeamounts of probabilistic data. More importantly, this projectinvestigates the issue of how to design novel and scalable algorithmsfor processing these queries efficiently in various settings, such asthe offline, centralized environment, distributed systems and thestreaming model.With the emergence of probabilistic data in many important applicationdomains, the demand for understanding and processing the ranking andaggregate queries efficiently from the scientific community and beyond(e.g., government and military agencies) is expected to intensify inthe coming years. The results of this project lay down a firmfoundation for this important problem. For further information, such as publications, data sets and source code, please see the project website at http://www.cs.fsu.edu/~lifeifei/rankaggprob