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MAGIC: A Multi-Activity Graph Index for Activity Detection

机译:MAGIC:用于活动检测的多活动图索引

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Suppose we are given a set A of activities of interest, a set O of observations, and a probability threshold p. We are interested in finding the set of all pairs (a, O''), where a A and O'' O, that minimally validate the fact that an instance of activity a occurs in O with probability p or more. The novel contribution of this paper is the notion of the multi-activity graph index (MAGIC), which can index very large numbers of observations from interleaved activities and quickly retrieve completed instances of the monitored activities. We introduce two complexity reducing restrictions of the problem (which takes exponential time) and develop algorithms for each. We experimentally evaluate our exponential algorithm as well as the restricted algorithms on both synthetic data and a real (depersonalized) travel data set consisting of 5.5 million observations. Our experiments show that MAGIC consumes reasonable amounts of memory and can retrieve completed instances of activities in just a few seconds. We also report appropriate statistical significance results validating our experimental hypotheses.
机译:假设我们得到了一组感兴趣的活动,一组观察结果O和一个概率阈值p。我们感兴趣的是找到所有对的集合(a,O''),其中A和O''O最小化了以下事实,即活动a的实例在O中以p或更大的概率发生的事实。本文的新颖贡献是多活动图索引(MAGIC)的概念,它可以索引来自交错活动的大量观察结果,并快速检索受监视活动的完整实例。我们介绍了两个减少问题复杂度的限制(这需要花费指数时间),并针对每个问题开发算法。我们对合成数据和由550万个观测值组成的真实(非个性化)旅行数据集进行了实验性评估,并采用了指数算法和受限算法。我们的实验表明,MAGIC占用了合理的内存量,并且可以在几秒钟内检索完成的活动实例。我们还将报告适当的统计显着性结果,以验证我们的实验假设。

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