串流資料中的離群值偵測演算法一直被視為很重要的問題，尤其是在IoT技術蓬勃發展的現在，大量的串流資料不斷的被產生，例如：智慧工廠製造、智能家電還有智慧城市當中的各種感測器，也讓離群值偵測演算法近年來越來越受重視，舉例來說，MILOF和DILOF都是串流資料中的離群值偵測演算法，兩者皆基於有名的LOF演算法所開發，LOF是一種基於密度的離群值偵測演算法，然而上述兩者皆沒有考慮到資料可能會隨著時間轉變的因素，因此，在這篇論文中我們提出了在數據流中基於時間感知與密度的漸增式區域異常檢測演算法(TADILOF)，還有基於過往資訊的LOF近似值演算法，在TADILOF演算法當中，我們結合了移除過時資料的概念，讓模型的能夠自適應資料的趨勢，在LOF近似值演算法當中，我們運用的過往的資訊，求取可能的LOF值，大幅增加了在記憶體限制下的效能，在實驗章節中，我們的結果贏過當今最好的演算法，另一方面，我們也運用此演算法建構一套汙染事件即時偵測系統，我們可以在汙染事件發生時通知使用者，讓使用者做好準備。

Outlier detection in data streams is crucial to data mining; however, this task is made increasingly difficult by enormous growth in the quantity of data generated by an expanding Internet of Things. Recent advances in outlier detection based on the density-based local outlier factor (LOF) algorithm (i.e., MILOF and DILOF) do not consider variations in data over time. This paper presents a novel algorithm for streaming data, referred to as time-aware density-based incremental local outlier detection (TADILOF) to overcome this issue. We also developed a means of estimating the LOF score based on historical information following the removal of outdated data. Experiment results demonstrated that TADILOF outperforms current state-ofthe-art methods. We also present an application of the proposed scheme to the development of an air-quality monitoring system.

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