Dataset for Blockchain-Enhanced Architecture for Federated Process Mining in IoT Environments

This dataset contains the key elements used in the paper Blockchain-Enhanced Architecture for Federated Process Mining in IoT Environments which range from complex event processing to process mining applied over multiple datasets. The information included is organized into the following sections:

1.- CEPApp.siddhi: It contains the rules and configurations used for pattern detection and real-time event processing.

2.- ProcessStorage.sol: Smart contract code used in the case study implemented on solidity using Polygon blockchain platform.

3.- Datasets Used ({adlinterweave_dataset, adlmr_dataset, twor_dataset}.zip): Three datasets used in the study, each with events that have been processed using the CEP engine.

4.- CEP Engine Processing Results ({cepresult_adlinterweave, cepresult_adlmr, cepresult_twor}.json): Output generated by the Siddhi CEP engine, stored in JSON format.

5.- Federated Event Logs ({xesresult_adlinterweave, xesresult_adlmr, xesresult_twor}.xes): Federated event logs in XES format, standard in process mining. Contains event traces obtained after the execution of the Event Log Integrator.

6.- Process Mining Results: Models generated from the processed event logs:

• Process Trees ({procestree_adlinterweave, procestree_adlmr, procestree_twor}.svg): structured representation of the detected workflows.
• Petri Nets ({petrinet_adlinterweave, petrinet_adlmr, petrinet_twor}.svg): Mathematical model of the discovered processes, useful for compliance analysis and simulations.
• Disco Results ({disco_adlinterweave, disco_adlmr, disco_twor}.pdf): Process models discovered with the Disco tool.
• ProM Results ({prom_adlinterweave, prom_adlmr, prom_twor}.pdf): Models generated with ProM tool.