DRSP：时间序列数据流相似性匹配和修剪的降维
DRSP : Dimension Reduction For Similarity Matching And Pruning Of Time Series Data Streams
摘要：
时间序列数据流的相似性匹配和连接在当今拥有大量流数据的世界中获得了很多相关性。该过程在位置跟踪、传感器网络、对象定位和监视等领域中得到了广泛的应用。然而，随着数据流的大小增加，为了帮助相似性匹配的过程而保留所有数据所涉及的成本也增加。我们开发了一个新的框架，以解决以下目标。首先，在预处理阶段进行降维，其中大的流数据被分割和减少到一个紧凑的表示，这样它保留了所有的关键信息，通过一种技术称为多级段均值（MSM）。这降低了与大的时间序列数据流的存储相关联的空间复杂度。其次，它采用了有效的相似性匹配技术来分析新的数据对象是否对称于现有的数据流。最后是剪枝技术，过滤掉伪数据对象对，只连接相关的对。MSM的计算成本是O（l*ni），修剪的成本是O（DRF*wsize*d），其中DRF是降维因子。我们已经进行了详尽的实验测试，以表明所提出的框架是有效的和称职的，与早期的作品相比。
英文摘要：
Similarity matching and join of time series data streams has gained a lot of relevance in today's world that has large streaming data. This process finds wide scale application in the areas of location tracking, sensor networks, object positioning and monitoring to name a few. However, as the size of the data stream increases, the cost involved to retain all the data in order to aid the process of similarity matching also increases. We develop a novel framework to addresses the following objectives. Firstly, Dimension reduction is performed in the preprocessing stage, where large stream data is segmented and reduced into a compact representation such that it retains all the crucial information by a technique called Multi-level Segment Means (MSM). This reduces the space complexity associated with the storage of large time-series data streams. Secondly, it incorporates effective Similarity Matching technique to analyze if the new data objects are symmetric to the existing data stream. And finally, the Pruning Technique that filters out the pseudo data object pairs and join only the relevant pairs. The computational cost for MSM is O(l*ni) and the cost for pruning is O(DRF*wsize*d), where DRF is the Dimension Reduction Factor. We have performed exhaustive experimental trials to show that the proposed framework is both efficient and competent in comparison with earlier works.
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