近年來，各種時間空間資訊透過各種定位工具被大量產生，如何即時偵測符合需求的事件即是一個需要被討論的議題。 本篇論文探討如何在給訂多條時間空間條件的規則下，即時偵測符合各規則的事件集。 規則中的三個條件由使用者給訂，分別為時間區間、空間範圍、空間內需涵蓋多少事件，本篇演算法即能即時回傳所有符合此條件的所有結果。此項技術能被應用於偵測時間空間上的群聚事件、擴散行為的偵測，例如，在傳染病偵測，多利用短時間內在某一範圍內到達某個數量級，即可認為是一種群聚感染的爆發。然而，要在空間中搜尋範圍中涵蓋指定數量點的事件數，需要搜尋無限個圓空間範圍，會導致無限個計算量。現有的文獻多為找分群或是以格狀簡化空間資訊，判斷該區域中是否達到某個數量的事件點。然而這些相關參考文獻無法直接轉化應用於我們的目標，所以我們提出了「即時性時間空間多規則同時偵測技術」，根據使用者給訂的規則，找尋符合規則定義中的群聚事件集。

本方法能同時找出符合多條不同時間、空間、數量條件的所有群聚事件集，並做到線上即時偵測與刪除過期點。在實驗中也測試不同規則參數配置中，各方法的執行速度。我們同時實驗了多條規則同時運行與單條規則運行多次的時間比較。實驗中也加入去年登革熱資料模擬實際應用於疫情傳染並偵測時的狀況。

To prevent the spreading of infectious disease in the epidemiology field, it is an important task to monitor the burst of the infectious events, which, there are lots of people get the particular disease in the close proximity in terms of both time and geography. However, not all people with infectious disease have the clear syndrome, such as the dengue fever patients. Therefore, the reported events might not reveal the actual spreading situation of the disease. The existing methods such as the dense region query, density-based clustering methods cannot directly applied to meet our needs. Therefore, to find all the suspicious infectious clusters, we propose the STMS method which is able to online detect all the specified event sets based on the user given spatio-temporal constraints. Users simply give multiple specifications which include the time interval, the circle-shaped of the spatial range constraint, the threshold of the number of the events. The STMS method is able to online detect and return the specified event sets which meet the corresponding specification.

In this paper, we propose three methods which include the intuitive method, the method based on the concept of the minimum enclosing circle (MEC), and the STMS method. The synthetic data and the real data set is applied to evaluate different performance of the method in different characteristic of the data set. We find that the STMS method has the better performance in the clustering-like data; the MEC method is suited for situation of the larger number of the event threshold.

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