在及時資料串流監視應用程式中，許多連續查詢能夠產生有效的產出以觸發相對應的動作。但受制於無止盡的串流資料和有限的資源處理能力下，從不同來源的串流資料往往無法滿足應用程式要求的服務品質。及時資料串流監視應用程式有必要採用合適的排程技術去保留資源以便處理更有價值的資料，在本篇論文中，利用探偵串流資料語義和應用(m,k)模型，我們提出一套名為安全負荷卸載方法(SOSA)，並以SOSA 為基礎提出一個創新的(m,k)排程演算法名為SOSA-DBP。我們透過機率模型分析和實證結果去驗證該演算法的效果並比較目前已存在的演算法。

In Real-time data stream monitoring applications, many continuous queries registered in the system should generate valid output to trigger corresponding actions. Due to unbounded amount of stream and limited processing resources, some of the data stream processing tasks originated from different sources do not always satisfy QoS requirement of application.
It is necessary that real-time monitor stream applications save resources to service valuable tasks from other streams by using adequate scheduling techniques. To relief system burden, many load shedding methods have been proposed. The lack of traditional load shedding methods only random drop
incoming tuples excess system overloaded. In this thesis, we propose a Safe load Shedding Approach (SOSA) by exploiting the data semantic of sensor streams and application of the (m, k) deadline model. A novel (m, k)
scheduling algorithm called SOSA-DBP will be introduced based on the philosophy of SOSA. We present probabilistic analysis and experimental that characterizes the effectiveness of our approach compared with the existing algorithms.

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