隨著雲端運算及物聯網的迅速發展，企業與個人產生的資料量越來越大，速度也越來越快，如何運用與管理如此龐大的資料成為一重要任務，而現今較具規模的企業均認知到資料就是金錢的定律下，如何維持資料庫系統的正常運作就落在具有高度專業資料庫管理技術的資料庫管理師的肩上。而專業合格的資料庫管理師養成極度不易，因此如何將資料庫管理工作自動化成為極具重要性與挑戰性的課題。在本研究中，我們蒐集了實際發生於製造現場資料庫的重大錯誤訊息，此類錯誤導致了企業工廠生產線的重大損失，研究中根據資料厙原廠提供的資訊深入分析了問題發生的真實原因，在研究過程中我們也同時分析了問題發生前資料庫系統的量測數據的變化，並探討了量測數據的監控方式如何能客觀地定義出正常與異常的狀況，進而降低誤判而產生的假警報。最後，在定義出問題真因與狀況的監控手法後，本硏究探討了如何透過建置一模糊專家系統，以針對資料庫系統所產生的量測數據進行數據歸納，並且設計了自動化程序用以監控數據變化的模式，進而達到即時監控以防患於未然的目標。

With the rapid development of cloud technology and Internet of things, the amount of data in corporate and individual production increases at a very fast speed. The way of appropriately using and managing large-scale databases has already become a crucial task. Nevertheless, it is usually difficult to train qualified and experienced database administrators. Therefore, we propose to devise automation approaches for database monitoring in this work. We collect real alarm messages and perform thorough analysis so as to identify corresponding causes and reduce severe losses. Specifically, the fuzzy theory is introduced to develop an expert system for monitoring variations of significant indicators. Empirical studies show that our proposed scheme is feasible in handling possible alarm messages. Furthermore, early warning can be delivered to the monitoring staff in some cases.

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