Enhanced-Sweep: Communication Cost Efficient Top-K Best Region Search.

The best region search (BRS) is one of the major research problems in geospatial data processing applications. The BRS problem objective is to discover the ideal location of a particular size specified rectangle, with a predetermined end goal of maximizing the user-defined scoring function. The existing solutions for finding the top-k best regions have focused on designing algorithms for centralized settings. These solutions are not suitable for processing massive datasets. In this paper, we enable a Hadoop MapReduce-based parallel and distributed computation to obtain significant improvement in the performance. In addition to the parallel and distributed setting, we also incorporate early pruning strategies to eliminate the need to process rectangles that are not part of the output to minimize the communication cost involved in computing k-BRS. We later introduced a redistribution strategy over the initially proposed methodology that handles skew inherited from the dataset. Our results are obtained from extensive experimentation, both synthetic and real-world datasets. [ABSTRACT FROM AUTHOR]