Improved Linear-Time Streaming Algorithms for Maximizing Monotone Cardinality-Constrained Set Functions

Abstract

Many real-world applications arising from social networks, personalized recommendations, and others, require extracting a relatively small but broadly representative portion of massive data sets. Such problems can often be formulated as maximizing a monotone set function with cardinality constraints. In this paper, we consider a streaming model where elements arrive quickly over time, and create an effective, and low-memory algorithm. First, we provide the first single-pass linear-time algorithm, which is a a deterministic algorithm, achieves an approximation ratio of [Formula: see text] for any [Formula: see text] with a query complexity of [Formula: see text] and a memory complexity of [Formula: see text], where [Formula: see text] is a positive integer and [Formula: see text] is the submodularity ratio. However, the algorithm may produce less-than-ideal results. Our next result is to describe a multi-streaming algorithm, which is the first deterministic algorithm to attain an approximation ratio of [Formula: see text] with linear query complexity.