Modeling and Querying Data Series and Data Streams with Uncertainty

Many real applications consume data that is intrinsically uncertain and error-prone. An uncertain data series is a series whose point values are uncertain. An uncertain data stream is a data stream whose tuples are existentially uncertain and/or have an uncertain value. Typical sources of uncertainty in data series and data streams include sensor data, data synopses, privacy-preserving transformations and forecasting models. In this thesis, we focus on the following three problems: (1) the formulation and the evaluation of similarity search queries in uncertain data series; (2) the evaluation of nearest neighbor search queries in uncertain data series; (3) the adaptation of sliding windows in uncertain data stream processing to accommodate existential and value uncertainty. We demonstrate experimentally that the correlation among neighboring time-stamps in data series can be leveraged to increase the accuracy of the results. We further show that the "possible world" semantics can be used as underlying uncertainty model to formulate nearest neighbor queries that can be evaluated efficiently. Finally, we discuss the relation between existential and value uncertainty in data stream applications, and verify experimentally our proposal of uncertain sliding windows.