text: Classical algorithms for query optimization presuppose the absence of inconsistencies or uncertainties in the database and exploit only valid semantic knowledge provided, e.g., by integrity constraints. Data inconsistency or uncertainty, however, is a widespread critical issue in ordinary databases: total integrity is often, in fact, an unrealistic assumption and violations to integrity constraints may be introduced in several ways. In this report we present an approach for semantic query optimization that, differently from the traditional ones, relies on not necessarily valid semantic knowledge, e.g., provided by violated or soft integrity constraints, or induced by applying data mining techniques. Query optimization that leverages invalid semantic knowledge cannot guarantee the semantic equivalence between the original user's query and its rewriting: thus a query optimized by our approach yields approximate answers that can be provided to the users whenever fast but possibly partial responses are required. Also, we evaluate the impact of use of invalid semantic knowledge in the rewriting of a query by computing a measure of the quality of the answer returned to the user, and we rely on the recent theory of Belief Logic Programming to deal with the presence of possible correlation in the semantic knowledge used in the rewriting.
  @cite_0: We propose a database model that allows users to annotate data with belief statements. Our motivation comes from scientific database applications where a community of users is working together to assemble, revise, and curate a shared data repository. As the community accumulates knowledge and the database content evolves over time, it may contain conflicting information and members can disagree on the information it should store. For example, Alice may believe that a tuple should be in the database, whereas Bob disagrees. He may also insert the reason why he thinks Alice believes the tuple should be in the database, and explain what he thinks the correct tuple should be instead. We propose a formal model for Belief Databases that interprets users' annotations as belief statements. These annotations can refer both to the base data and to other annotations. We give a formal semantics based on a fragment of multi-agent epistemic logic and define a query language over belief databases. We then prove a key technical result, stating that every belief database can be encoded as a canonical Kripke structure. We use this structure to describe a relational representation of belief databases, and give an algorithm for translating queries over the belief database into standard relational queries. Finally, we report early experimental results with our prototype implementation on synthetic data.
summary: For instance, @cite_0 introduces the model of belief databases, where the tuples of a (relational) database are annotated by users' beliefs, which express their agreement or disagreement on the reliability of the data. Beliefs do not measure uncertainty of knowledge, but provide a qualitative evaluation of the presence of conflicts in the information contained in the database: thus, they are taken into account for query answering, but they are not exploited to optimize a query or to provide approximate results.