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首页> 外文期刊>ACM transactions on sensor networks >iDiary: From GPS Signals to a Text-Searchable Diary
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iDiary: From GPS Signals to a Text-Searchable Diary

机译：iDiary：从GPS信号到文本可搜索的日记

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This article describes iDiary, a system that takes as input GPS data streams generated by users' phones and turns them into textual descriptions of the trajectories. The system features a user interface similar to Google Search that allows users to type text queries on their activities (e.g., "Where did I buy books?") and receive textual answers based on their GPS signals. iDiary uses novel algorithms for semantic compression and trajectory clustering of massive GPS signals in parallel to compute the critical locations of a user. We encode these problems as follows. The k-segment mean is a k-piecewise linear function that minimizes the regression distance to the signal. The (k, m)-segment mean has an additional constraint that the projection of the k segments on R-d consists of only m <= k segments. A coreset for this problem is a smart compression of the input signal that allows computation of a (1 + epsilon)-approximation to its k-segment or (k, m)-segment mean in O(nlog n) time for arbitrary constants e, k, and m. We use coresets to obtain a parallel algorithm that scans the signal in one pass, using space and update time per point that is polynomial in log n. Using an external database, we then map these locations to textual descriptions and activities so that we can apply text mining techniques on the resulting data (e.g., LSA or transportation mode recognition). We provide experimental results for both the system and algorithms and compare them to existing commercial and academic state of the art. This is the first GPS system that enables text-searchable activities from GPS data.
机译：本文介绍了iDiary，该系统将用户电话生成的GPS数据流作为输入，并将其转换为轨迹的文字描述。该系统具有类似于Google搜索的用户界面，该界面允许用户在自己的活动中键入文字查询（例如“我在哪里买书？”），并根据其GPS信号接收文字答案。 iDiary使用新颖的算法对大量GPS信号进行语义压缩和轨迹聚类，以并行计算用户的关键位置。我们将这些问题编码如下。 k段平均值是k分段线性函数，可最大程度地减少与信号的回归距离。 （k，m）段平均值具有一个附加约束，即k个段在R-d上的投影仅包含m <= k个段。这个问题的核心是输入信号的智能压缩，它允许在任意常数e的O（nlog n）时间内计算与其k段或（k，m）段平均值的（1 + epsilon）近似值。 ，k和m。我们使用核集来获得并行算法，该算法一次扫描信号，使用空间和每个点的更新时间（log n中的多项式）。然后，我们使用外部数据库将这些位置映射到文字说明和活动，以便我们可以将文本挖掘技术应用于生成的数据（例如LSA或运输方式识别）。我们提供系统和算法的实验结果，并将它们与现有的商业和学术水平进行比较。这是第一个从GPS数据启用文本可搜索活动的GPS系统。

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