Manual data annotation is often slow, expensive, and difficult to scale-especially for tasks that are subjective and context-dependent, like detecting hate speech and stereotypes. With recent progress in Large Language Models (LLMs), there is growing potential to automate this process. In this study, first, we explore the use of a committee of LLMs (GPT-4omini, Gemini-1.5-flash, and DeepSeek-R1) to generate annotations for Italian social media content. Then, the quality of LLM-generated labels is evaluated by following a teacher-student approach, where we trained a smaller student model (phi3.5-miniinstruct) on them and testing its performance against a humanlabeled dataset. The results indicate that fine-tuning the student model over LLM-generated labels, with careful dataset balancing and hyperparameter tuning, can yield a significant improvement over the baseline (approximately $17 \%$ improvement), suggesting that committee-based LLM annotation can provide high-quality labels. Therefore, this work shows the proposed approach can be a reliable and scalable alternative to manual labeling by properly addressing challenges like class imbalance to be sure about the fairness and accuracy of the fine-tuned models.

Encoding methods are employed across several process mining tasks, including predictive process monitoring, anomalous case detection, trace clustering, etc. These methods are usually performed as preprocessing steps and are responsible for mapping complex event data information into a numerical feature space. Most papers choose existing encoding methods arbitrarily or employ a strategy based on expert domain knowledge. Moreover, existing methods are employed by using their default parameters without evaluating other options. This practice can lead to several drawbacks, such as suboptimal performance and unfair comparisons with the state-of-the-art. Therefore, this work aims at providing a comprehensive survey and benchmark on event log encoding by comparing 27 methods, from different natures, in terms of expressivity, scalability, correlation, and domain agnosticism. To the best of our knowledge, this is the most comprehensive study so far focusing on trace encoding in process mining. It contributes to maturing awareness about the role of trace encoding in process mining pipelines and sheds light on issues, concerns, and future research directions regarding the use of encoding methods to bridge the gap between machine learning models and process mining.

Online process mining refers to a class of techniques for analyzing in real-time event streams generated by the execution of business processes. These techniques are crucial in the reactive monitoring of business processes, timely resource allocation and detection/prevention of dysfunctional behavior. Many interesting advances have been made by the research community in recent years, but there is no consensus on the exact set of properties these techniques have to achieve. This paper tills the gap by identitying a set of evaluation goals for online process mining and examining their fulfillment in the state of the art. We discuss parameters and techniques regulating the balance between conflicting goals and outline research needed for their improvement. Concept drift detection is crucial in this sense but, as demonstrated by our experiments, it is only partially supported by current solutions.

Online process mining refers to a class of techniques for analyzing in real-time event streams generated by the execution of business processes. These techniques are crucial in the reactive monitoring of business processes, timely resource allocation and detection/prevention of dysfunctional behavior. Many interesting advances have been made by the research community in recent years, but there is no consensus on the exact set of properties these techniques have to achieve. This article fills the gap by identifying a set of evaluation goals for online process mining and examining their fulfillment in the state of the art. We discuss parameters and techniques regulating the balance between conflicting goals and outline research needed for their improvement. Concept drift detection is crucial in this sense but, as demonstrated by our experiments, it is only partially supported by current solutions.

Abstract Criminal investigation adopts Artificial Intelligence to enhance the volume of the facts that can be investigated and documented in trials. However, the abstract reasoning implied in legal justification and argumentation requests to adopt solutions providing high precision, low generalization error, and retrospective transparency. Three requirements that hardly coexist in today’s Artificial Intelligence solutions. In a controlled experiment, we then investigated the use of graph embeddings procedures to retrieve potential criminal actions based on patterns defined in enquiry protocols. We observed that a significant level of accuracy can be achieved but different graph reformation procedures imply different levels of precision, generalization, and transparency.

Events happening at defined moments in time and involving specific entities from a social or physical system can be organized in networks or graphs. The study of such event graphs may reveal causal relations between subsequent events or compound events that we define as “typed events”. Moreover, characteristic sequences of events or patterns can arise in consequence of phenomena affecting the system. Methods to build the event graph and to search for the typed events and their significance are described in detail. An embedding strategy to encode typed events in low dimensional vectors is defined, and both supervised and unsupervised learning is applied to search for meaningful patterns. Experiments have been conducted using data from a real investigation and some synthetic data.

As we have entered the Internet-of-Things (IoT) era, further blessed with rapid advances in several key technological areas including DevOps, AI/ML, 5G/6G/, neurocomputing, to name a few, it is imperative we think big and aim high. This new venture will require professionals in both software engineering and services computing to collaborate with an unprecedented intensity, and jointly develop the new interdisciplinary field hereby named Software Services Engineering (SSE). In SSE, the ever-deepening system dynamics emerging from both environments and humans in varying contexts are imposing steep challenges to both researchers and practitioners. Humans, both developers and the vast number of end users, are embedded ever closer to IoT environments, and are being afforded ample opportunities to continuously inject inputs during system development and after deployment. In fact, humans are increasingly playing the roles of both sensor and actuator. Traditional requirements engineering researchers are being lured more than ever into exploiting the IoT environments where human users are deeply embedded, to gather contextual information that inevitably introduces lots of ambiguity and uncertainty. Provisioning of highly adaptable and scalable microservices would be key to timely meeting ever-changing human desires and ever-evolving system requirements in the nimblest manner. As such, an ultra-agile and field-programmable development methodology and environment will be imperative to achieving such ultrafine grained microservices provisioning. Such ultra-agility and ultrafine granularity requirements imposed to the services industry obligate company executives to expect extreme manageability assurance to become the centroid of system operations and administration. The ultimate goal in pursuit of such a noble dream will be to provide genuinely individualized and trustworthy service, possibly enabled by AI, but it should be both explainable and ethical. Facing such grand challenges, this declaration samples a subset of burning issues in SSE through observations in seven themes, only meant to be starting points for the SSE community to further investigate. Through our declarations we also call for heightened attention to an assorted array of existing, barely emerging or non-existent services computing and software engineering methods for a concerted effort to research and explore.

Summary As now well established, the world population is aging rapidly and, according to World Health Organization (WHO), the amount of people aged 60 years and older is expected to total 2 billion in 2050. For this reason, an emerging important issue is the definition of a new generation of healthcare platforms capable of monitoring people's quality of life. In this article, we propose a new methodology that supports the entire requirement elicitation process starting from the initial phase of gathering the requirements, both clinical, technological, and end‐user, up to the choice of the most suitable solution. Our proposal provides a new new iterative model in the smart healthcare field research area. Furthermore we apply our proposal in a real scenario and we report the end‐to‐end implementation of the proposed methodology.

The Big Data revolution promises to build a data-driven ecosystem where better decisions are supported by enhanced analytics and data management.However, major hurdles still need to be overcome on the road that leads to commoditization and wide adoption of Big Data Analytics (BDA).Big Data complexity is the first factor hampering the full potential of BDA.The opacity and variety of Big Data technologies and computations, in fact, make BDA a failure prone and resource-intensive process, which requires a trial-and-error approach.This problem is even exacerbated by the fact that current solutions to Big Data application development take a bottom-up approach, where the last technology release drives application development.Selection of the best Big Data platform, as well as of the best pipeline to execute analytics, represents then a deal breaker.In this paper, we propose a return to roots by defining a Model-Driven Engineering (MDE) methodology that supports automation of BDA based on model specification.Our approach lets customers declare requirements to be achieved by an abstract Big Data platform and smart engines deploy the Big Data pipeline carrying out the analytics on a specific instance of such platform.Driven by customers' requirements, our methodology is based on an OWL-S ontology of Big Data services and on a compiler transforming OWL-S service compositions in workflows that can be directly executed on the selected platform.The proposal is experimentally evaluated in a real-world scenario focusing on the threat detection system of SAP.

Assuring anomaly-free business process executions is a key challenge for many organizations. Traditional techniques address this challenge using prior knowledge about anomalous cases that is seldom available in real-life. In this work, we propose the usage of word2vec encoding and One-Class Classification algorithms to detect anomalies by relying on normal behavior only. We investigated 6 different types of anomalies over 38 real and synthetics event logs, comparing the predictive performance of Support Vector Machine, One-Class Support Vector Machine, and Local Outlier Factor. Results show that our technique is viable for real-life scenarios, overcoming traditional machine learning for a wide variety of settings where only the normal behavior can be labeled.

Knowledge graphs are exploited in criminal investigation to integrate heterogeneous data sources and scale up the operational efficiency of enquiry protocols. Using a declarative perspective, protocols can be viewed as a set of data ingestion procedures and nested exact queries. This meets the probating nature of procedural justice that has to proceed from established facts. At the same time, the exact specification of queries represents a limit for enquiry protocols that can exclusively retrieve those facts in adherence to the designed queries. We then investigated the use of graph em-beddings procedures to extend the scope of a protocol by returning sub-graphs partially matching to its specification. Because exploring the entire set of sub-graphs quickly become computationally intractable, we developed an approach based on a hierarchical filtering procedure. A controlled experiment we executed has shown the feasibility of our approach.

The requirement elicitation process of a Smart Healthcare platform is challenging as conflicting needs must be conciliated. Stakeholders such as clinicians, patients, and caregivers, legal, organizational, and technological constraints, all these elements must be accounted for and coordinated. Starting from a real case study, this paper offers an overview of the aspects that was perceived as innovative by the stakeholders and experts involved in the desgn process.

This article aims at introducing a new configurable and multipurpose electronic voting service based on the blockchain infrastructure. The objective is to design an architecture to automatically translate service configuration defined by the end user into a cloud-based deployable bundle, automating business logic definition, blockchain configuration, and cloud service provider selection. The article presents the preliminary results of the system and a SOA-based services definition implemented with smart contracts.

The advent of Big Data has revolutionized the way in which data are collected, analyzed, and processed, becoming a pre-requisite for each enterprise that competes in the global market. In this respect, the commodization of Big Data analytics is an essential goal to be faced in the near future. Recently, some preliminary approaches have been presented mostly focusing on distributing Big Data platforms as a service, while less has been done on cross-platform Big Data analytics. In this paper, we propose a model-based methodology for Big Data Analytics-as-a-Service that extends existing techniques by supporting cross-communication between batch and stream processing, deployment on multiple platforms, and end-to-end verification against users' requirements.

Considering the usefulness of monitoring students' response to available task-level feedback in confidence-based assessment, in this paper, we introduce a novel approach to classify students problem-solving activities into various engagement and disengagement behaviors and study their occurrences during complete learning sessions. Then by clustering these sessions, we obtained four distinct groups which varied both in terms of students' (dis)engagement behaviors and their quantitative performance scores in confidence-based assessment. Moreover, a qualitative analysis shows that high and low performance students (determined based on their final scores in the course) relate differently to the obtained clusters. Based on these findings we highlight that our approach of investigating students' engagement by observing traces of performed problem-solving activities is promising and opens new avenues of research. Also, our approach is more generic as it does not contain human-expert defined time limits which are usually determined by analyzing students' data who participated in the experimental study.

Confidence-based assessment is a two-dimensional assessment paradigm which considers confidence or expectancy level a student has about the answer, to ascertain his/her actual knowledge. Several researchers have discussed the usefulness of this model over traditional one-dimensional assessment approach, which takes number of correctly answered questions as a sole parameter to calculate the test scores of a student. However, very little is investigated about students' behavior in relation to different confidence-outcome knowledge regions. This Ph.D. research work aims to analyze and construct behavioral profiles of the students by focusing on their confidence level, performance, and navigation in a computer-based assessment system. The final goal is to provide a framework for an adaptive confidence-based assessment system which may be exploited to provide personalized feedback to improve students' confidence accuracy and knowledge by considering their behavioral dynamics.

This work aims at investigating and quantifying the Urban Transport System (UTS) resilience enhancement enabled by the adoption of emerging technology such as Internet of Everything (IoE) and the new trend of the Connected Community (CC). A conceptual extension of Functional Resonance Analysis Method (FRAM) and its formalization have been proposed and used to model UTS complexity. The scope is to identify the system functions and their interdependencies with a particular focus on those that have a relation and impact on people and communities. Network analysis techniques have been applied to the FRAM model to identify and estimate the most critical community-related functions. The notion of Variability Rate (VR) has been defined as the amount of output variability generated by an upstream function that can be tolerated/absorbed by a downstream function, without significantly increasing of its subsequent output variability. A fuzzy-based quantification of the VR based on expert judgment has been developed when quantitative data are not available. Our approach has been applied to a critical scenario as flash flooding considering two cases: when UTS has CC and IoE implemented or not. However, the method can be applied in different scenarios and critical infrastructures. The results show a remarkable VR enhancement if CC and IoE are deployed.

Big Data domain is one of the most promising ICT sectors with substantial expectations both on the side of market growing and design shift in the area of data storage managment and analytics. However, today, the level of complexity achieved and the lack of standardisation of Big Data management architectures represent a huge barrier towards the adoption and execution of analytics especially for those organizations and SMEs not including a sufficient amount of competences and knowledge. The full potential of Big Data Analytics (BDA) can be unleashed only through the definition of approaches that accomplish Big Data users' expectations and requirements, also when the latter are fuzzy and ambiguous. Under these premises, we propose Big Data Analytics-as-a-Service (BDAaaS) as the next-generation Big Data Analytics paradigm and we discuss issues and challenges from the BDAaaS design and development perspective.

In this study we analyzed the curricula of 65 university students to investigate the impact of activities progression on student performances. Clustering curricula based on activity order and type we discovered a significant incidence on performance, validating the predictive power of curricula. Nevertheless, we discovered that the characterization of clusters is mainly due to non mandatory activities, selected by a student to personalize his curriculum, while activity order is very less relevant. This observation rejects the idea that activities progression has impact on performance, resulting rather as a consequence of student choices.

Organizational risk management should not only rely on protecting data and information but also on protecting knowledge which is underdeveloped in many cases or measures are applied in an uncoordinated, dispersed way. Therefore, we propose a consistent top-down translation from the organizational risk management goals to implemented controls to overcome these shortcomings. Our approach adopted from the domain of IT security management allows to measure how well knowledge protection is actually pursued in organizations. This affects organizations' abilities to prove compliance to risk management standards, laws, guidelines, or frameworks and creates transparency throughout the whole knowledge protection processes. After introducing our integrated risk management framework, we demonstrate how the technical part of the framework can be implemented by using process mining in a case study of an Italian aerospace company.

In this paper we propose a new architecture and methodology to define a collaborative environment aimed at supporting the innovation process. In the first part of this paper we analyse the data collected during an experiment for testing a collaborative environment and, in the second part, we propose an architecture to support and stimulate innovation processes. Our solution is based on three components, namely a (i) collaborative platform, (ii) a tool able to extract knowledge from shared documents, external data sources, and collaborative activities, and (iii) a recommender system. More specifically, we focus on how knowledge items are extracted from incoming knowledge flows to be proposed to a team, whose members are capable of proposing values for design issues and/or evaluating these choices from her own specific perspective. Furthermore, the aim of the proposed framework is not limited to the selection of relevant knowledge but, more broadly, on aligning the team on a restricted set of information items, producing a convergence of objectives that accelerates the kinetics of the collaborative work.

Bringing sensor- and context-awareness to the Web promises to open new, tremendous business opportunities. However, context-awareness involves difficult problems such as modeling social / human affairs (e.g. law, personal preferences, etc.), handling incomplete information, and managing uncertainty of sensor data. We argue that fully-fledged, Semantic-Web-style reasoning in presence of uncertainty is neither always feasible, nor always necessary for a context-based Web. As an alternative, this paper proposes a highly expressive, simple, and efficient context representation for sensor-enabled Web services. Our representation is based on a syntax inspired by standard Semantics of Business Vocabulary and Business Rules (SBVR), enriched by annotations expressing prioritized modality and trust values. The feasibility of our approach is demonstrated via a case study for developing context/ambient-aware applications that adapt to changes in the environment.

We present an overview of ODDI an Ontology Driven Data Integration system based on Formal concept analysis and instance comparison. Data Integration systems are used to integrate heterogeneous data sources in a single view. Following the Global-as-View approach the data is retrieved through a common conceptualization, that in our system is modeled as an ontology. This paper focuses on the problem of matching and mapping of elements between the common ontology and the data sources. The problem of query translation is also mentioned for sake of completeness, but it will be treated in detail in a future paper. Recent works on Business Intelligence do highlight the need of trustable and sound data access systems. We propose a system based on FCA to generate the mapping to the common representation and the relations between the heterogeneous data sources.

We present a way of building ontologies that proceeds in a bottom-up fashion, defining concepts as clusters of concrete XML objects. Our rough bottom-up ontologies are based on simple relations like association and inheritance, as well as on value restrictions, and can be used to enrich and update existing upper ontologies. Then, we show how automatically generated assertions based on our bottom-up ontologies can be associated with a flexible degree of trust by nonintrusively collecting user feedback in the form of implicit and explicit votes. Dynamic trust-based views on assertions automatically filter out imprecisions and substantially improve metadata quality in the long run

We describe the Extreme Programming Ontology (XPO), a formal model specifying the main concepts used in the extreme programming methodology and their properties. XPO's modular structure was developed using the usual normative top down approach to software engineering process modeling. It relies on a set of core components rooted in three main concepts: organisational role, product and phase. Besides being useful for indexing relevant documents and XP artifacts such as user stories and Wiki pages, XPO is aimed at being a sound basis for nonintrusive analysis of agile processes, mining process data about programmers activity and repositories content in order to extract new concepts potentially identifying critical factors in agile software development. Extension to XPO are also discussed, including other agile methodologies and more general software engineering concepts.