1,721,401 research outputs found
Aggiornamenti in radioprotezione: markers tumorali e sorveglianza medica.
No full textBologna 11-14/10/1995
Predictive Analytics for Object-Centric Processes: Do Graph Neural Networks Really Help?
No full textThe object-centric process paradigm is increasingly gaining popularity in academia and industry. According to this paradigm, the process delineates through the parallel execution of different execution flows, each referring to a different object involved in the process. Object interaction is present, and takes place through bridging events where these parallel executions synchronize and exchange data. However, the complex intricacy of instances of such processes relating to each other via many-to-many associations makes a direct application of predictive process analytics approaches designed for single-id event logs impossible. This paper reports on the experience of comparing the predictions of two techniques based on gradient boosting or the Long Short-Term Memory (LSTM) network against two based on graph neural networks. The four techniques were empirically evaluated on event logs related to two real object-centric processes, and more than 20 different KPI definitions. The results show that graph-based neural networks generally perform worse than techniques based on Gradient Boosting. Considering that graph-based neural networks have training times that are 8-10 times larger, the conclusion is that their use does not seem to be justified
Event-log abstraction using batch session identification and clustering
No full textProcess-Mining techniques aim to use event data about past executions to gain insight into how processes are executed. While these techniques are proven to be very valuable, they are less successful to reach their goal if the process is flexible and, hence, it exhibits an extremely large number of variants. Furthermore, information systems can record events at very low level, which do not match the high-level concepts known at business level. Without abstracting sequences of events to high-level concepts, the results of applying process mining (to, e.g., discover a model) easily become very complex and difficult to interpret, which ultimately means that they are of little use. A large body of research exists on event abstraction but typically a large amount of domain knowledge is required, which is often not readily available. Other abstraction techniques are unsupervised, which ultimately return less accurate results and/or rely on stronger assumptions. This paper puts forward a technique that requires limited domain knowledge that can be easily provided. Traces are divided in batch sessions, and each session is abstracted as one single high-level activity execution. The abstraction is based on a combination of automatic clustering and visualization methods. The technique was assessed on two case studies about processes characterized by high variability. The results clearly illustrate the benefits of the abstraction to convey accurate knowledge to stakeholders
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