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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yafei Wu Yongxin Zhu Tian Huang Xinyang Li Xinyi Liu Mengyun Liu |
| Copyright Year | 2015 |
| Description | Author affiliation: Sch. of Microelectron., Shanghai Jiao Tong Univ., Shanghai, China (Yafei Wu; Yongxin Zhu; Tian Huang; Xinyang Li; Xinyi Liu; Mengyun Liu) |
| Abstract | The computational complexity of discord discovery is $O(m^{2}),$ where m is the size of time series. Many promising methods were proposed to resolve this compute-intensive problem. These methods sequentially discover discords on standalone machine. The limited capability of standalone machine in terms of computing and memory capacity hinders these methods in discovering discords from large dataset in reasonable time. In this work, we propose a distributed discord discovery method. Our method is able to combine discord results from different computing nodes, which are non-combinable in previous literature. We mitigate the issue of the memory wall by using distributed data partitioning. We implement our method on distributed Spark computing framework and distributed HDFS (Hadoop Distributed File System) storage platform. The implementation exhibits superior scalability and enables discords discovery in multi-dimension time series. We evaluate our method with terabyte-sized dataset, which is larger than any dataset in previous literature. Evaluation results show that our method has clear advantage in terms of performance and efficiency over state-of-the-art algorithms. |
| Starting Page | 154 |
| Ending Page | 159 |
| File Size | 430387 |
| Page Count | 6 |
| File Format | |
| e-ISBN | 9781479989379 |
| DOI | 10.1109/HPCC-CSS-ICESS.2015.228 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-08-24 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis discord anomaly Time series analysis Force Clustering algorithms time series Microelectronics Spark Sparks Acceleration |
| Content Type | Text |
| Resource Type | Article |
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