Marrying Top-k with Skyline Queries: Operators with Relaxed Preference Input and Controllable Output Size

The two most common paradigms to identify records of preference in a multi-objective setting rely either on dominance (e.g., the skyline operator) or on a utility function defined over the records’ attributes (typically, using a top- k query). Despite their proliferation, each of them has its own palpable drawbacks. Motivated by these drawbacks, we identify three hard requirements for practical decision support, namely, personalization, controllable output size, and flexibility in preference specification. With these requirements as a guide, we combine elements from both paradigms and propose two new operators, \(\mathsf {ORD} \) and \(\mathsf {ORU} \) . We present a suite of algorithms for their efficient processing, dedicating more technical effort to \(\mathsf {ORU} \) , whose nature is inherently more challenging. Specifically, besides a sophisticated algorithm for \(\mathsf {ORD} \) , we describe two exact methods for \(\mathsf {ORU} \) , and one approximate. We perform a qualitative study to demonstrate how our operators work, and evaluate the performance of our algorithms against adaptations of previous work that mimic their output.