Causality has been recently introduced in databases, to model, characterize, and possibly compute causes for query answers. Connections between QA-causality and consistency-based diagnosis and database repairs (wrt. integrity constraint violations) have already been established. In this work we establish precise connections between QA-causality and both abductive diagnosis and the view-update problem in databases, allowing us to obtain new algorithmic and complexity results for QA-causality. We also obtain new results on the complexity of view-conditioned causality, and investigate the notion of QA-causality in the presence of integrity constraints, obtaining complexity results from a connection with view-conditioned causality. The abduction connection under integrity constraints allows us to obtain algorithmic tools for QA-causality.

Causality has been recently introduced in databases, to model, characterize and possibly compute causes for query results (answers). Connections between query causality and consistency-based diagnosis and database repairs (wrt. integrity constrain violations) have been established in the literature. In this work we establish connections between query causality and abductive diagnosis and the view-update problem. The unveiled relationships allow us to obtain new complexity results for query causality -the main focus of our work- and also for the two other areas.

Increasing complexity of technical systems requires a precise fault localization in order to reduce maintenance costs and system downtimes. Model-based diagnosis has been presented as a method to derive root causes for observed symptoms, utilizing a description of the system to be diagnosed. Practical applications of model-based diagnosis, however, are often prevented by the initial modeling task and computational complexity associated with diagnosis. In the proposed thesis, we investigate techniques addressing these issues. In particular, we utilize a mapping function which converts fault information available in practice into propositional horn logic sentences to be used in abductive model-based diagnosis. Further, we plan on devising algorithms which allow an efficient computation of explanations given the obtained models.