We consider the problem of incorporating outerjoins into uncertain databases. We motivate why outerjoins are useful, but tricky, in uncertain databases, arguing that standard possible-worlds semantics may be inappropriate for outerjoins. We explore a variety of alternative semantics through a

We address schema design in uncertain databases. Since uncertain data is relational in nature, decomposition becomes a key issue in design. Decomposition relies on dependency theory, and primarily on functional dependencies. We study the theory of functional dependencies (FDs) for uncertain

Abstract. Existing uncertain databases have difficulty managing data when exact confidence values or probabilities are not available. Confi-dence values may be known imprecisely or coarsely, or even be missing altogether. We propose a generalized uncertain database that can man-age data

Abstract. We discuss an approach to collaborative data management based on uncertain databases. Note that, in a collaborative data management system, users may have contradicting opinions about the correct values of data items. In our approach, we propose to store all conflicting data versions

Recently, many new applications, such as sensor data monitoring and mobile device tracking, raise up the issue of uncertain data management. Compared to “certain ” data, the data in the uncertain database are not exact points, which, instead, often locate within a region. In this paper, we study

Top-k processing in uncertain databases is semantically and computationally different from traditional top-k processing. The interplay between score and uncertainty makes traditional techniques inapplicable. We introduce new probabilistic formulations for top-k queries. Our formulations are based

Abstract — This work introduces novel polynomial-time algorithms for processing top-k queries in uncertain databases, under the generally adopted model of x-relations. An x-relation consists of a number of x-tuples, and each x-tuple randomly instantiates into one tuple from one or more alternatives

Abstract. There are a lot of application domains, e.g. sensor databases, traffic management or recognition systems, where objects have to be compared based on vague and uncertain data. Feature databases with uncertain data require special methods for effective similarity search. In this paper, we

Abstract — We develop an approach to incorporate additional knowledge, in the form of general-purpose integrity constraints (ICs), to reduce uncertainty in probabilistic databases. While incorporating ICs improves data quality (and hence quality of answers to a query), it significantly complicates

Abstract—The importance of query processing over uncertain data has recently arisen due to its wide usage in many real-world applications. In the context of uncertain databases, previous works have studied many query types such as nearest neighbor query, range query, top-k query, skyline query