The document discusses how analytics and data mining can be used to gain insights from data generated by business processes. It describes how event data from processes can be analyzed in real-time for monitoring and over time to identify patterns and opportunities for process improvement. Key applications discussed include predictive modeling, simulation, optimization, and automated recommendations for resource allocation and process changes.
Analytics and Data Mining for Business Process Improvement
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4. Overview Business Operations Control Event Detection &Correlation Predictive Simulation Data Mining Optimization Event Bus ERP BPM ECM Legacy EAI Custom Historical Analytics Real Time Dashboards Alerts & Actions
8. Analytics Architecture Publish AE Relational Database Events OLAP and DataMining Databases Process Analysis Engine Queries Context Data Client Reports Participants, UDFs, XPDL Staging and Event Queue Tables Fact and Dimension Tables Process Engine Administration Controls Analysis Engine Exposes UDFs Triggers Cube Processing Monitors DBs Web Service Business Operations Historical Analytics
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11. Actions & Alerts Process Metrics Action Schedule Rules Engine Email and Cellphone notification Process Event Triggers Goals Thresholds Risk Mitigation KPI Evaluation Web Service Call or Execute Script Actions
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16. Optimization BottleNeck Analysis Determine task most understaffed Cross-train most idle, feasible person Alternatively hire new one Predict (simulate altered scenario) Wait Time Reduction by Load Balancing Analyze current situation Predict (simulate) Alter scenario Propose measure for improvement