Min-D-Occur: Ensuring Future Occurrences In Streaming Sets

Given a set of n elements and a corresponding stream of its subsets, we consider the problem of selecting k elements that should appear in at least d such subsets arriving in the "near" future with high probability. For this min-d-occur problem, we present an algorithm that provides a solution with the success probability of at least 1 O(kd logn/D + 1/n),where D is a known constant. Our empirical observations on two streaming data sets show that this algorithm achieves high precision and recall values. We further present a sliding window adaptation of the proposed algorithm to provide a continuous selection of these elements. In contrast to the existing work on predicting trends based on potential increase in popularity, our work focuses on a setting with provable guarantees.