SMART OPERATIONS BASED ON INSIGHTS FROM DEEP MINING The solution included organizing existing workflows and knowledgebase, labeling data for common issues and resolutions, then building ML model-based data to identify anomalies and find associations. By combining repeating alerts and categorizing similar ones, the volume of alerts was reduced 95.2%. Efficiency was further improved by automating the alert handling process where automated detection and diagnosis were implemented to minimize human engagement. Alerts were also batch processed to increase the throughput. At the system and data level, Beyondsoft performed the association analysis to identify the hidden relationships among alerts. 40 such relationships were revealed. With the help of the expert system, this effo

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