Self adaptive data quality : automating duplicate detection Carrying out business processes successfully is closely linked to the quality of the data inventory in an organization. Lacks in data quality lead to problems: Incorrect address data prevents (timely) shipments to customers. Erroneous orders lead to returns and thus to unnecessary effort. Wrong pricing forces companies to miss out on revenues or to impair customer satisfaction. If orders or customer records cannot be retrieved complaint management takes longer. Due to erroneous inventories too few or too much supplies might be reordered. A special problem with data quality and the reason for many of the issues mentioned above are duplicates in databases. Duplicates are different representations of same real world objects in a dataset. However these representations differ from each other and are for that reason hard to match by a computer. Moreover the number of required comparisons to find those duplicates grows with the square of the dataset size. To cleanse the data these duplicates must be detected and removed. Duplicate detection is a very laborious process. To achieve satisfactory results appropriate software must be created and configured (similarity measures partitioning keys thresholds etc.). Both requires much manual effort and experience. This thesis addresses automation of parameter selection for duplicate detection and presents several novel approaches that eliminate the need for human experience in parts of the duplicate detection process. [...]