Probabilities and sets in preference querying
MetadataShow full item record
User preferences in databases are attracting increasing interests with the boom of information systems and the trend of personalization. In literature, there are two different frameworks dealing with this topic, namely quantitative approaches and qualitative approaches. The former assume the availability of a scoring function, while the latter do not. In qualitative approaches, preferences are expressed using preference formulas. We investigate three advanced topics on preferences stemming from those frameworks. First, we study top-k queries over uncertain data in the quantitative framework. We formulate three intuitive semantic properties for top- k queries in probabilistic databases, and propose Global-Top k query semantics which satisfies them to a great degree. We also design efficient dynamic programming algorithms for query evaluation. Second, we observe that all work on top- k queries in probabilistic database focus on ordinal scores, however there are applications where cardinal scores are more appropriate. This motivates our work on preference strength, where we consider the magnitude of score in addition to the order it establishes over tuples. Finally, as a counterpart to the top- k query in the quantitative framework, we explore the set preference problem in the qualitative framework. Observing the fact that preferences can also be collective in this case, our goal is to tackle this second-order problem with first-order tools. We propose a logical framework for set preferences. Candidate sets are represented using profiles consisting of scalar features. This reduces set preferences to tuple preferences over set profiles. We also propose algorithms to compute the “best” sets.