Understanding the stumbling blocks of Italian higher education system:A process mining approach

Nowadays universities strive to continuously enhance their educational programs to improve both the quality and quantity of their graduates. This is a sensitive problem, especially for Italian universities where only 30% of the students enrolled at the university succeed in graduating within a year after the normal duration of the study plan. Over the last few years, the Italian Ministry of University and Education has introduced several indicators to assess students’ careers and help universities identify possible criticality in their study programs. However, these indicators only provide a high-level overview of the graduation process without providing insights into students’ failure. To address this issue, in this work, we propose to model a study program as a process and exploit process analysis techniques to assess students’ performance. These techniques allow delving into students’ careers, thus enabling the investigation of their failures and delays. The findings obtained by applying our approach to the Bachelor program of an Italian university allowed us to determine common bottlenecks that seem to have an impact on students’ graduation time. Moreover, we were able to determine and compare the career paths of successful and late students. The insights gathered by our analysis can be used to support university personnel in delving into factors causing some exams to be a bottleneck, as well as to determine potential improvements in the overall curricula.</p

Understanding the stumbling blocks of Italian higher education system:A process mining approach

Nowadays universities strive to continuously enhance their educational programs to improve both the quality and quantity of their graduates. This is a sensitive problem, especially for Italian universities where only 30% of the students enrolled at the university succeed in graduating within a year after the normal duration of the study plan. Over the last few years, the Italian Ministry of University and Education has introduced several indicators to assess students’ careers and help universities identify possible criticality in their study programs. However, these indicators only provide a high-level overview of the graduation process without providing insights into students’ failure. To address this issue, in this work, we propose to model a study program as a process and exploit process analysis techniques to assess students’ performance. These techniques allow delving into students’ careers, thus enabling the investigation of their failures and delays. The findings obtained by applying our approach to the Bachelor program of an Italian university allowed us to determine common bottlenecks that seem to have an impact on students’ graduation time. Moreover, we were able to determine and compare the career paths of successful and late students. The insights gathered by our analysis can be used to support university personnel in delving into factors causing some exams to be a bottleneck, as well as to determine potential improvements in the overall curricula.</p

Preface of the 31st Italian Symposium on Advanced Database Systems

This volume contains the proceedings of the 31st Italian Symposium on Advanced Database Systems (SEBD - Sistemi Evoluti per Basi di Dati), held in Galzinagno Terme (Padua, Italy) from 2 to 5 July 2023.</p

Preface of the 31st Italian Symposium on Advanced Database Systems

This volume contains the proceedings of the 31st Italian Symposium on Advanced Database Systems (SEBD - Sistemi Evoluti per Basi di Dati), held in Galzinagno Terme (Padua, Italy) from 2 to 5 July 2023.</p

Semantic modeling and design patterns for IoT ecosystems

In recent years, there has been an increasing diffusion of IoT-based devices that collect and share sensor data for a wide variety of applications. These data are highly heterogeneous as they are obtained from various data sources utilizing different representation schemes, which represents a great richness in terms of stored information but also a strong limitation in terms of interoperability. Since traditional software modeling is not able to cope with this issue, several semantic approaches based on ontologies and/or linked data have been proposed in recent literature. Semantic modeling provides a potential basis for interoperating among different systems and applications in the IoT. The semantic interoperability can be implemented for just one specific IoT device or for entire IoT ecosystems in which different IoT devices interact with each other. The goal of this Special Issue is to provide insights on recent advances in semantic modeling for IoT by presenting original scientific contributions in the form of theoretical foundations, models, experimental research, and case studies

Automatic Annotation of Corpora For Emotion Recognition Through Facial Expressions Analysis

The massive adoption of social networks has madeavailable an unprecedented amount of user-generated content,which may be analyzed in order to determine people’s opinionsand emotions on a large variety of topics. Research has mademany efforts in defining accurate algorithms for the analysis ofemotions conveyed by texts, however their performance oftenrelies on the existence of large annotated datasets, whose currentscarcity represents a major issue. The manual creation of suchdatasets represents a costly and time-consuming activity andhence there is an increasing demand for techniques for theautomatic annotation of corpora. In this work we present amethodology for the automatic annotation of video subtitleson the basis of the analysis of facial expressions of peoplein videos, with the goal of creating annotated corpora thatmay be used to train emotion recognition algorithms. Facialexpressions are analyzed through machine learning algorithms,on the basis of a set of manually-engineered facial featuresthat are extracted from video frames. The soundness of theproposed methodology has been evaluated through an extensiveexperimentation aimed at determining the performance on realdatasets of each methodological step

Process-aware IIoT Knowledge Graph: A semantic model for Industrial IoT integration and analytics

The integration of the huge data streams produced by the Industrial Internet of Things (IIoT) can provide invaluable knowledge in the context of Industry 4.0, and is also an open research issue. The present paper proposes a semantic approach to this issue, centered around the notion of process as the backbone. We build an ontology describing the fundamental elements involved in IIoT and their relations, and discuss the construction of the Process-aware IIoT Knowledge Graph, where raw sensor data are enriched with information about process activities and the physical production environment. We also propose a framework for querying the Knowledge Graph, and we demonstrate its capabilities by considering the production of metal accessories as case study

Comparing data-driven meta-heuristics for the bi-objective Component Repairing Problem

Due to both the increasing use of automation in production processes and the budget devoted for purchasing equipment, maintenance plays a key role in making a company competitive in the marketplace. Moreover, the use of data analysis techniques and the advent of Internet of Things make the IoT-based predictive maintenance possible. In addition, since all the resources (e.g., budget and human) involved in the maintenance activities are usually limited, a company is also interested in defining optimized maintenance plans. In this paper, the integration of IoT-based predictive maintenance with optimization techniques is investigated by developing a data-driven Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic aimed at efficiently defining maintenance plans. In particular, we address the bi-objective component repairing problem (b-CRP), aimed at determining the set of components of a production system to repair that are more likely to fail. Having the breakage probability of each component, derived from historical data, the system reliability is maximized and the maximum time required to repair one component among those selected in the solution is minimized, under constraints on both budget and time for performing the maintenance activities. Then, we compare the solutions of GRASP with those of an already existing bi-objective Large Neighborhood Search meta-heuristic

BIG GUI: A Tool for Building and Analysing Instance Graphs

n this paper, we present BIG GUI, a novel tool supporting the generation and visualization of so-called instance graphs from an event log and a process model. Instance graphs are directed acyclic graphs representing both sequential and concurrent relations of process executions stored in the event log. The tool implements an IG generation algorithm robust to infrequent and non-compliant behaviours and provides a graphical interface for visualising the generated instance graph set

Semantic Representation of Key Performance Indicators Categories for Prioritization

Key Performance Indicators (KPIs) are crucial tools that are remarkably used to evaluate business performance. Recently, the management of KPIs has fascinated the focus of both academic and business professionals, and that lead to the development of research on various methods dealing with issues such as modeling, maintenance, and expressiveness of KPIs. As a need for organizations and processes to adapt to continuously changing demands, the KPIs used to measure their effectiveness evolve too. In order to make KPI management easier, this research aims to define the best sequence of KPIs evaluation based on semantic relations. After an extensive analysis of the literature on KPIs ontologies, it proposes the idea of KPIs prioritization on the basis of relations among different categories of kpis established by a KPIs ontology. Our approach can be used independently from the particular KPI’s management strategy being employed