2D TSA-tree: a wavelet-based approach to improve the efficiency of multi-level spatial data mining

C. Shahabi, Seokkyung Chung, Maytham Safar, G. Hajj
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引用次数: 16

Abstract

Due to the large amount of the collected scientific data, it is becoming increasingly difficult for scientists to comprehend and interpret the available data. Moreover typical queries on these data sets are in the nature of identifying (or visualizing) trends and surprises at a selected sub-region in multiple levels of abstraction rather than identifying information about a specific data point. The authors propose a versatile wavelet-based data structure, 2D TSA-tree (Trend and Surprise Abstractions Tree), to enable efficient multi-level trend detection on spatial data at different levels. We show how 2D TSA-tree can be utilized efficiently for sub-region selections. Moreover, 2D TSA-tree can be utilized to precompute the reconstruction error and retrieval time of a data subset in advance in order to allow the user to trade off accuracy for response time (or vice versa) at query time. Finally, when the storage space is limited, our 2D Optimal TSA-tree saves on storage by storing only a specific optimal subset of the tree. To demonstrate the effectiveness of our proposed methods, we evaluated our 2D TSA-tree using real and synthetic data. Our results show that our method outperformed other methods (DFT and SVD) in terms of accuracy, complexity and scalability.
二维tsa树:一种提高多层次空间数据挖掘效率的小波方法
由于收集的科学数据量很大,科学家越来越难以理解和解释现有数据。此外,对这些数据集的典型查询本质上是在多个抽象层次上识别(或可视化)选定子区域的趋势和意外,而不是识别有关特定数据点的信息。作者提出了一种通用的基于小波的数据结构,2D TSA-tree(趋势和惊喜抽象树),以实现对不同层次空间数据的高效多级趋势检测。我们展示了二维tsa树如何有效地用于子区域选择。此外,2D tsa树可以用来预先计算数据子集的重建误差和检索时间,以便允许用户在查询时以响应时间(反之亦然)来权衡准确性。最后,当存储空间有限时,我们的2D最优tsa树通过仅存储树的特定最优子集来节省存储空间。为了证明我们提出的方法的有效性,我们使用真实和合成数据评估了我们的二维tsa树。结果表明,该方法在精度、复杂度和可扩展性方面优于其他方法(DFT和SVD)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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