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Mining Transaction Data for Process Instance Monitoring in Legacy Systems

REQUIRED) End-to-End business processes in organizations are implemented across multiple applications, legacy systems, ERP systems and products. In such scenarios where applications are developed over a period of time and with varying technologies, monitoring end-to-end business processes is a challenge. Typical methods for providing process monitoring capabilities are intrusive methods like changing code and introducing probes; or introducing new software tools like EAI and BAM. We propose a non-intrusive process instance monitoring (PIM) method that uses the persistent data generated by the business transactions to monitor the process instances in Legacy Information Systems. We propose a slightly unconventional data mining method where the transaction data is parsed from the application data stores, loaded into custom schema and then associated to the process flow for monitoring the state of individual process instances. The approach further provides for alerting when business events like an SLA violation occur.