IoT-based Multi-party Process Monitoring

Business Processes are more and more exploiting Smart Objects to perform their tasks. This research envisions the adoption of Smart Objects to design reliable business processes and to assess their correct execution. Instead of using control-flow based languages to express the processes, declarative-based approaches like GSM (Guard Stage Milestone) and CMMN (Case Management Modeling Notation) are used.

Most relevant publications:

Meroni, G.: Artifact-Driven Business Process Monitoring – A Novel Approach to Transparently Monitor Business Processes, Supported by Methods, Tools, and Real-World Applications. LNBIP Volume 368. Springer International Publishing (2019)

Meroni, G., Baresi, L., Montali, M., Plebani, P.: Multi-party business process compliance monitoring through IoT-enabled artifacts. In: Information Systems. Volume 73, pp. 61 – 78. Elsevier (2018)

Meroni, G., Di Ciccio, C., Mendling, J.: An Artifact-Driven Approach to Monitor Business Processes Through Real-World Objects. In: Service-Oriented Computing – ICSOC 2017.  LNCS, pp. 297–313. Springer International Publishing (2017)

Blockchain and Artifact-driven Process Monitoring

Blockchain and distributed ledger platforms are recently gaining a lot of attention, as they allow to build trust among organizations without relying on trusted intermediaries, such as banks or government agencies. At the same time, artifact-driven process monitoring aims at collecting information on the execution of multi-party business processes in a transparent and independent fashion. This research work analyzes these two areas of interest, and aims at providing a platform to achieve trusted process monitoring.

Most relevant publications:

Meroni, G., Plebani, P., Vona, F.: Trusted Artifact-Driven Process Monitoring of Multi-party Business Processes with Blockchain. In: BPM Blockchain and CEE Forum 2019. LNBIP, pp 55-70. Springer International Publishing (2019)

Cappiello, C., Comuzzi, M., Daniel, F., Meroni, G.: Data Quality Control in Blockchain Applications. In: BPM Blockchain and CEE Forum 2019. LNBIP, pp 55-70. Springer International Publishing (2019)

Meroni, G., Plebani, P.: Combining Artifact-Driven Monitoring with Blockchain: Analysis and Solutions. In: CAiSE 2018 Workshops. LNBIP, pp 103-114. Springer International Publishing (2018)

Ontology-based Sensors and Sensor Data Retrieval

Applications for the Internet of Things, Smart Buildings and Smart Cities heavily rely on sensors and sensor data. However, being able to identify which sensors are suited for the application, and if sensor data are provided in a compatible format is far from trivial. To this aim, this research work exploits ontologies to classify sensors and to infer if they fit an application.

Most relevant publications:

Meroni, G., Plebani, P.: Artifact-Driven Monitoring for Human-Centric Business Processes with Smart Devices: Assessment and Improvement. In: BPM Forum 2017. LNBIP, pp 160-176. Springer International Publishing (2017)

Foglieni, C., Mazuran, M., Meroni, G., Plebani, P.: Retrieving Sensors Data in Smart Buildings Through Services: A Similarity Algorithm. In: Service-Oriented Computing – ICSOC 2014 Workshops. LNCS, pp 281-291. Springer International Publishing (2015)