My research activity concerns theoretical, methodological, and practical aspects in different areas of Computer Science, including Business Process Management and Modelling, Process Mining, also applied to Data Pipelines, and Human-Computer Interaction. Such topics are challenged in the application domains of smart manufacturing and visualizations’ optimization.
Since October 2020, I have been part of an international team, involved in a research activity about modelling visualizations and exploiting those models to optimize queries performed against a database, to reduce latency in particular cases of user interaction.
Since January 2021, I have been been also part of an international team, which is working in the context of the European project DataCloud, that investigate the discovery, modeling, deployment, and adaptation of big data pipelines.
My main research accomplishments in all the areas of interest are summarized below:
- Business Process Management (BPM) is an active research area that is based on the observation that each product and/or service that a company provides to the market is the outcome of several activities performed. Business processes are the key instruments for organizing such activities and understanding their interrelationships. In the context of the BPM field, my research concentrates on the management of data aware processes representing the pipelines running behind workflows. This topic is currently investigated in the context of the DataCloud project.
- Process Modeling is the first and most important step in the BPM lifecycle, which intends to provide a high-level specification of a business process that is independent from implementation and serves as a basis for process automation and verification. On this topic, I am currently investigating, in the context of the DataCloud project, how to provide automatic techniques for process modeling that could discover and visualize the data pipelines laying behind process workflows. Process mining is about extracting knowledge from event logs commonly available in today's information systems. These techniques provide new means to discover, monitor and improve processes in a variety of application domains. In the context of process mining, trace alignment consists of verifying whether the observed behavior of a process, stored in an event log, is compliant with its underlying model that encodes how it is allowed to be executed, and to repair it to ensure that norms and regulations are not violated. On this topic, I am currently investigating, in the context of the DataCloud project, how to automatically apply process mining techniques on logs to generate insights about the data pipelines laying behind process workflows. Furthermore, I am researching on techniques to perform conformance checking, analytics, deployment and adaptation of the discovered big data pipelines.
- Human-Computer Interaction (HCI) is a research topic focusing on the interfaces between users and computers. In the context of HCI, my current research concentrates on modeling information visualizations and exploiting the models to build the expected users’ behavior that can be used to generate predictions and perform query optimization. My current research tackles a well-known (unsolved) challenge in this area, namely the automated quantification of usability of interactive systems. In this direction, I am focusing on developing a theoretical and practical framework that exploits Process Mining algorithms and technologies to automatically derive the usability of a system during its daily use. The impact of this research, which requires a strong background and expertise in BPM and HCI, is potentially ground-breaking in the HCI field, as it aims at superseding the expensive and time-consuming usability techniques for observing users in highly controlled environments over extended periods of time.