ARIS vs bupaR

ARIS combines business process management, process mining, and AI in an environment that connects process designs with execution data. ARIS Process Mining reconstructs flows from event data, showing performance, bottlenecks, and deviations from designed processes; it can ingest logs from SAP, Salesforce, and ServiceNow. ARIS Simulation runs browser-based BPMN, eBPMN, and EPC scenarios and compares time, cost, throughput, and service performance. Its digital-twin process context describes processes, rules, roles, and systems for designing, deploying, and improving enterprise AI agents. APIs can let third-party systems create, read, update, and manage ARIS information, although API licenses may be required. Documentation covers cloud editions and on-premise server installations, giving the platform a hybrid deployment model. The Trust Center lists ISO/IEC 27001, ISO/IEC 27017, ISO/IEC 27018, SOC 2, SOC 3, HIPAA, and other materials; some documents require a valid point of contact. ARIS says it was founded in 1992 and became independent in 2025. Pricing is available on request after contacting sales.

bupaR is a free, open-source suite of R packages for working with business process data. It supports importing and preparing event data, calculating descriptive statistics, visualising processes, and checking conformance. Analysts can examine control flow, performance, and organisational aspects, while including custom process attributes in their workflow. It handles event logs, where each row represents an event, and activity logs, where each row represents an activity instance. Process maps help summarise processes and can be used in Shiny apps or exported to PDF, PNG, PS, or EPS. The bupaverse core contains bupaR, edeaR, eventdataR, processcheckR, and processmapR. The xesreadR package reads XES files from a local path or URL and writes event logs to XES. Installation instructions cover CRAN and GitHub development versions. The package requires R 3.5.0 or later and is licensed under MIT plus a license file. For prediction with processpredictR, Python, TensorFlow, and Keras are required. Validation of larger event logs can take time, though the documentation allows it to be disabled when the data meets requirements.