Efficient processing of exploratory top-k joins

In this paper, we address the problem of discovering a ranked set of k distinct main objects combined with additional (accessory) objects that best fit the given preferences. This problem is challenging because it considers object combinations of variable size, where objects are combined only if the combination produces a higher score, and thus becomes more preferable to a user. In this way, users can explore overviews of combinations that are more suited to their preferences than single objects, without the need to explicitly specify which objects should be combined. We model this problem as a rank-join problem where each combination is represented by a set of tuples from different relations and we call the respective query eXploratory Top-k Join query. Existing approaches fall short to tackle this problem because they impose a fixed size of combinations, they do not distinguish on combinations based on the main objects or they do not take into account user preferences. We introduce a more efficient bounding scheme that can be used on an adaptation of the rank-join algorithm, which exploits some key properties of our problem and allows earlier termination of query processing. Our experimental evaluation demonstrates the efficiency of the proposed bounding technique.