大數據分析已是現今社會所被重要關注的議題之一，我們每一天接收到的資訊極為龐大 且複雜，若能更好的利用這些資訊，必能為人類帶來不少的好處以及便利。隨著大數據時代 的推進，物聯網裝置的開發也在不斷地進步增長，而最常見的應用服務之一便是事件偵測。 在大量數據的情況下執行事件偵測，系統往往需要提供足夠的資源來為每個用戶分配事件偵 測的工作量。因此，這會花費大量 CPU 資源和時間。
在大量用戶的情況下，我們得到的龐大數據所包含的事件發生數量是非常稀疏的，而在 所得資料類別不均的情況下，我們提出了 Spatial-Temporal Candidate Sampling for Target Selection (STCS)，一個專門為大量用戶所設計的事件偵測目標選取技術，通過運用空間局 部性以及時間局部性兩種方法來偵測數據中有可能發生事件的目標使用者，達到降低事件偵 測次數，進而降低運算資源。
本論文分別在大型漏水事件偵測以及冷器開關偵測兩項實際場域上進行實驗。我們所提 出的方法在 80% 的事件達成率下，在漏水資料上僅使用了 17% 的運算資源，而在冷器開關 事件上僅使用了 45% 的運算資源。因此，我們所提出的技術可以很好的應用在大量使用者 的事件偵測任務上，透過非常少的運算資源，達到非常好的事件偵測達成率。

Recently, big data analysis has become one of the most important topics in current society. We constantly acquire a vast amount of complicated information every day. These complicated data will be very beneficial and convenient for people if we can utilize this big data more effectively. With the advancement of the era of big data, the development of IoT devices is also growing, and one of the most common application services is event detection. To perform event detection with a large amount of data, the system must supply sufficient resources to allocate the effort of event detection among each user to accomplish event detection. Therefore, a lot of CPU time and resources are used.
The event data we acquired from large-scale users are extremely scarce. Therefore, the un- equal data categories situation makes this task more challenging, so in this study, we propose an event detection target selection technique for a large number of users, named Spatial-Temporal Candidate Sampling for Target Selection (STCS), by utilizing two methods: spatial locality and temporal locality, to identify target users who may be aberrant in the large data and so lower the number of event detection and the computational resources required.
In this paper, we test our proposed method in two real-life event detection problems, such as water leakage events and air conditioner on-off events as our experimental scenarios. Our proposed method uses only 17% of computational costs on water leakage events and 45% of computational costs on air conditioner on-off events at an 80% total recall rate. Therefore, the technique we propose can be well applied to the event detection task of large-scale users, achieving a very good event detection rate with very few computational costs.

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