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Parallel GPU-based Plane-Sweep Algorithm for Construction of iCPI-Trees

Witold Andrzejewski, Pawel Boinski

Source Title: Journal of Database Management (JDM) 26(3)

DOI: 10.4018/JDM.2015070101

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Abstract

This article tackles the problem of efficient construction of iCPI trees, frequently used in co-location pattern discovery in spatial databases. It discusses the methods for parallelization of iCPI-tree construction and plane-sweep algorithms used in state-of-the-art algorithms for co-location pattern mining. The main contribution of this paper is threefold: (1) a general algorithm for parallel iCPI-tree construction is presented, (2) two variants of parallel plane-sweep algorithm (which can be used in conjunction with the aforementioned iCPI-tree construction algorithm) are introduced and (3) all three algorithms are implemented on CUDA GPU platform and their performance is tested against an efficient multithreaded parallel implementation of iCPI-tree construction on CPU. Experiments prove that our solutions allow for large speedups over CPU version of the algorithm. This paper is an extension of the conference paper (Andrzejewski & Boinski, 2014).

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Co-Location Pattern Mining

A spatial dataset consists of spatial objects. Each such object has assigned: its location, unique identifier and a category called a spatial feature . A spatial object is an instance of the feature if has a type . A set of spatial features of objects in is denoted by . To prevent duplicates in results of the co-location pattern mining we assume, without loss of generality, that there is a total order (e.g., lexicographical) among spatial features.