Query Processing over Uncertain and Probabilistic Databases

Abstract

Recently, query processing over uncertain data has become increasingly important in many real applications like location-based services (LBS), sensor network monitoring, object identification, and moving object search. In many of these applications, data are inherently uncertain and imprecise, thus, we can either assign a probability to each data object or model each object as an uncertainty region. Based on these models, we have to re-define and study queries over uncertain data. In this tutorial, we will first introduce data models that are used to model uncertain and probabilistic data. Then, we will discuss various types of queries together with their query processing techniques. After that, we will introduce recent trends on query processing over uncertain non-traditional databases, such as sets and graphs. Finally, we will highlight some future research directions. The tutorial aims to introduce the state-of-the-art query processing techniques over uncertain and probabilistic data and discuss the potential research problems.