Continuous Skyline Computation Accelerator with Parallelizing Dominance Relation Calculations: (Abstract Only)

Abstract

Skyline Computation is a method for extracting interesting entries from a large population with multiple attributes. These entries, called skyline or Pareto optimal entries, are known to have extreme characteristics that cannot be found by using outlier detection methods. Skyline computation is an important task for characterizing large amounts of data and selecting interesting entries with extreme features. When the population changes dynamically, the task of calculating a sequence of skyline sets is called a continuous skyline computation. This task is known to be difficult for the following reasons: (1) information must be kept for non-skyline entries, since they may join the skyline in the future; (2) the appearance or disappearance of even a single entry can change the skyline drastically; and (3) it is difficult to adopt a geometric acceleration algorithm for skyline computation tasks with high-dimensional datasets. A new algorithm, called jointed rooted-tree (JR-tree), has been developed that manages entries using a rooted-tree structure. JR-tree delays extend the tree to deeper levels to accelerate tree construction and traversal. In this study, we propose the JR-tree based continuous skyline computation acceleration algorithm. Our hardware algorithm parallelizes the calculations of dominance relation between a target entry and the skyline entries. We implemented our hardware algorithm on an FPGA and showed that high-speed tree construction and traversal can be realized. Comparing our FPGA-based implementation with an Intel CPU running state-of-the-art software algorithms, it was found to reduce the query processing time for synthetic and real-world datasets. Our hardware implementation is 1.7x to 35x faster than the software implementations.