The Top-K problem is defined as follows. Given L lists of real numbers, find the top K scoring L-tuples. A tuple is scored by the sum of its components. Rare event modeling and event ranking are often reduced to the Top-K problem. In this paper, we present the application of a fixedmemory heuristic search algorithm (namely, SMA*) and its distributed-memory extension to the Top-K problem. Our approach has efficient runtime complexity and super-linear speedup in distributed-memory setting. Experimental studies on both synthetic and real-world data sets show the effectiveness of our approach
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