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Extra info for Apache Hadoop YARN: Moving beyond MapReduce and Batch Processing with Apache Hadoop 2 (Addison-Wesley Data & Analytics Series)
The following are the main performance metrics often used to evaluate the efﬁciency and effectiveness of the trajectory data reduction techniques: • Processing time: the execution time spent to run a trajectory data reduction algorithm; • Compression rate: the ratio in the size of an approximate trajectory vs. the size of its original trajectory; • Error measure: the deviation of an approximate trajectory from its original trajectory. Among them, the processing time assesses how efﬁciently a trajectory data reduction technique processing a given trajectory data set.
It is important to note that ﬁltering is not always necessary. In fact, we rarely use it for GPS data. Filtering is important in those situations where the trajectory data is particularly noisy, or when one wants to derive other quantities from it, like speed or direction. 9. The GPS logger recorded 1075 points at a rate of one per second during a short walk around the Microsoft campus in Redmond, Washington USA. For plotting, we converted the latitude/longitude points to (x, y) in meters. While the walk itself followed a casual, smooth path, the recorded trajectory shows many small spikes due to measurement noise.
18 Wang-Chien Lee and John Krumm S S S S S S S S S 3 S 3 S S S S S Fig. 8 The construction of the safe area and the data reduction strategy in a threshold-guided sampling algorithm. Only the location points fallen out of the projected safe areas are reported to the location server. 8 Therefore, a decision to include a particular location point or not needs to take into account changes in speed and direction. g. driving on a highway, moving objects usually do not make dramatic speed and direction changes.