Bulk sorted access for efficient top-k retrieval

Efficient top-k retrieval of records from a database has been an active research field for many years. We approach the problem from a real-world application point of view, in which the order of records according to some similarity function on an attribute is not unique: Many records have same values in several attributes and thus their ranking in those attributes is arbitrary. For instance, in large person databases many individuals have the same first name, the same date of birth, or live in the same city. Existing algorithms, such as the Threshold Algorithm (TA), are ill-equipped to handle such cases efficiently. We introduce a variation of TA, the Bulk Sorted Access Algorithm (BSA), which retrieves larger chunks of records from the sorted lists using fixed thresholds, and which focusses its efforts on records that are ranked high in more than one ordering and are thus more promising candidates. We experimentally show that our method outperforms TA and another previous method for top-k retrieval in those very common cases.