Data ambiguity is inherent in applications such as data integration, location-based services, and sensor monitoring. In many situations, it is possible to "clean", or remove, ambiguities from these databases. For example, the GPS location of a user is inexact due to measurement errors, but context information (e.g., what a user is doing) can be used to reduce the imprecision of the location value. In order to obtain a database with a higher quality, we study how to disambiguate a database by appropriately selecting candidates to clean. This problem is challenging because cleaning involves a cost, is limited by a budget, may fail, and may not remove all ambiguities. Moreover, the statistical information about how likely database objects can be cleaned may not be precisely known. We tackle these challenges by proposing two types of algorithms. The first type makes use of greedy heuristics to make sensible decisions; however, these algorithms do not make use of cleaning information and require user input for parameters to achieve high cleaning effectiveness. We propose the Explore-Exploit (or EE ) algorithm, which gathers valuable information during the cleaning process to determine how the remaining cleaning budget should be invested. We also study how to fine-tune the parameters of EE in order to achieve optimal cleaning effectiveness. Experimental evaluations on real and synthetic datasets validate the effectiveness and efficiency of our approaches.

The skyline operator was first proposed in 2001 for retrieving interesting tuples from a dataset. Since then, 100+ skyline-related papers have been published; however, we discovered that one of the most intuitive and practical type of skyline queries, namely, group-by skyline queries remains unaddressed. Group-by skyline queries find the skyline for each group of tuples. In this paper, we present a comprehensive study on processing group-by skyline queries in the context of relational engines. Specifically, we examine the composition of a query plan for a group-by skyline query and develop the missing cost model for the BBS algorithm. Experimental results show that our techniques are able to devise the best query plans for a variety of group-by skyline queries. Our focus is on algorithms that can be directly implemented in today's commercial database systems without the addition of new access methods (which would require addressing the associated challenges of maintenance with updates, concurrency control, etc.).