Ricerc@Sapienza

Process mining is the research field studying how to automatically analyze logs coming from informations systems in order to obtain a deep knowledge of the underneath business process aimed at monitoring its performance, predicting future situations and proactively acting on the process itself. While this field has been very active in the last decades, proposed techniques can be only applied when actions and activities inside logs are very high-level (e.g., a worker switched on a specific machine). In the last few years though, it has been noticed how vast majority of processes produce a lot of low level data directly coming from sensors monitoring the so called cyber-physical environments where the business processes are executed. Notable examples of cyber-physical environments are represented by smart environments and smart homes in particular, which are increasingly equipped with sensors and actuators. In this scenario, it has been argued that, human habits can be considered as example of processes that are carried on by persons during their daily lives.

Unfortunately, sensor logs produced by such environments are far from being optimal for currently available process mining techniques. For example, a common prerequisite of process mining techniques is to have a trace log explicitly segmented in traces (process instances). This assumption is usually not met by sensor logs as labeling is generally an expensive task to be performed by humans. Additionally, there is no one-to-one correspondence between sensor measurements and actions performed by a person. Techniques from the machine learning field can be used to aggregate sensor measurements in order to recognize actions, but they are very sensor-specific. Finally, discovered processes, in the case of human habits, can be so unregulated to resemble a so called "spaghetti" process, i.e., a process which is hard to visualize.

The aim of this project is to find valuable solutions to overcome these challenges.