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.

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

5. Process Model Example

6. Events WORKFLOWCREATE WORKFLOWTERMINATE CHILDCREATE CHILDTERMINATE ARRIVEACTIVITY BEGINACTIVITY COMPLETEACTIVITY SUSPENDACTIVITY CANCELACTIVITY CONTINUEACTIVITY BEGINTIMEDSEQUENCE COMPLETETIMEDSEQUENCE LOGGEDEVENT Activity Process Timed Sequence Logged Event Event Event Group <?xml version=&quot;1.0&quot; encoding=&quot;UTF-8&quot;?> <XPDLogEvents> <XPDLogEvent System=&quot;MortgageDemo&quot; Scenario=&quot;Mortgage Lending AsIs&quot; Run=&quot;8/28/2006 6:29:01 PM&quot; InstanceId=&quot;6505&quot; ParentInstanceId=&quot;&quot; WorkflowInstanceId=&quot;6505&quot; Timestamp=&quot;2006-08-01T07:00:00Z&quot; SequenceId=&quot;281010&quot; ProcessId=&quot;Mortgage Lending AsIs&quot; ProcessVersion=&quot;1&quot; EventType=&quot;WORKFLOWCREATE&quot; ActivitySetId=&quot;1&quot; ActivityId=&quot;15&quot; QueueId=&quot;-1&quot; ElapsedTimeDays=&quot;0&quot; ElapsedBusinessHours=&quot;0&quot; ElapsedBusinessDays=&quot;0&quot; AccruedWaitDays=&quot;0&quot; AccruedWaitBusinessDays=&quot;0&quot; AccruedWaitBusinessHours=&quot;0&quot; AccruedProcessingDays=&quot;0&quot; AccruedProcessingBusinessDays=&quot;0&quot; AccruedProcessingBusinessHours=&quot;0&quot;> <Participants> <Participant ParticipantId=&quot;System&quot; /> </Participants> <DataFields> <DataField Name=&quot;SIM_Cost&quot; Type=&quot;FLOAT&quot;>0</DataField> </DataFields> </XPDLogEvent> </XPDLogEvents>

7. Timing Data from Workflow Log Events

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

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

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

17. Process Model Example

20. Judging the Effect: Productivity Index Initially: After Load Balancing:

21. Judging the Effect: Throughput Analysis After role addition (e.g. cross training): After resource addition: Initially:

26. Choosing Columns from a Subset of the Data Source View

27. Process Model Example (previous example)

28. Mining Structure

29. A Tornado has knocked out a Processing Center

31. Predicting Cycle Time We can tell customers the expected wait time for a loan.

32. Predicting Cost

34. Discovering Duration Rule

35. Branching

36. Cluster Analysis