In an increasingly digital world, collecting, processing, and exchanging personal data are critical drivers for enacting enterprise business processes. However, the long-term retention and access of personal data expose organizations to data breaches, in which sensitive and protected data are disclosed and exploited unauthorizedly. To mitigate the damage that data breaches can cause, in the European Union (EU), the right to data privacy is enforced through the General Data Protection Regulation (GDPR), which defines how organizations must store and manage EU citizens’ data. GDPR is highly influencing how organizations approach data privacy, forcing them to rethink and upgrade their business processes to become GDPR compliant, which can be daunting. In this paper, in line with the privacy-by-design principles of GDPR, we propose a methodology that shows how to capture the main privacy GDPR constraints in the form of design patterns and integrate them into business process models specified in BPMN (Business Process Model and Notation). This allows us to achieve full transparency of privacy constraints in business processes, making it possible to ensure their compliance with GDPR at design-time. We adopt a design science research approach to present our methodology and make design decisions explicit. We also introduce GDPR-Pilot, a BPMN editor that assists process designers and Data Controllers in integrating GDPR patterns into existing models. The methodology is evaluated through real-world use cases against structural, usage, and environmental requirements.

Organizations increasingly rely on Process Performance Indicators (PPIs) to evaluate how effectively their business processes achieve strategic and operational goals. Recent approaches compute PPIs directly from event logs leveraging the increasing availability of event data in organizations, but they assume that PPIs have been previously defined using well-established reference models and that their linkage to the event log has been explicitly specified. However, this definition of appropriate PPIs remains challenging, as managers often lack detailed insights into real process executions and must adapt generic frameworks to organizational contexts. To address this gap, this paper introduces PPIPilot, an approach and tool that supports managers in defining measurable PPIs by suggesting a set of relevant PPIs and their computations directly from a business process description, an associated event log, and a specified organizational goal. PPIPilot leverages large language models for contextual PPI suggestion and structured translation, operating through a three-component pipeline of suggestion, translation, and computation, with a fallback mechanism to address computation errors. The quantitative evaluation shows that PPIPilot has a strong ability to suggest and compute PPIs, and that the fallback mechanism reduces failures, increasing the share of PPIs that can be computed without errors. Complementing these findings, an in-depth qualitative study indicates that users find the tool intuitive, transparent and helpful for easing both the conceptual and technical challenges of defining PPIs.

Declarative process models allow us to capture the behavior of a business process through temporal constraints on the evolution of process activities. In process mining, declarative process discovery focuses on deriving these constraints from event logs. Although the semantic aspects of declarative processes have been extensively investigated, there has been less focus on designing declarative visual notations that enhance model understanding and support analysts in solving process mining tasks. To improve the human understandability of declarative process models, in this paper, we present easyDeclare , a novel visual notation to specify declarative process models using the Declare language. easyDeclare was developed with consideration of the well-established Moody’s design principles. We conducted extensive user experiments to demonstrate that easyDeclare , when compared with the original graphical representation of Declare , reduces the cognitive load required to interpret Declare models of increasing complexity, making it a promising alternative to enhancing overall comprehension of declarative process discovery tasks.

This paper presents a manifesto that articulates the conceptual foundations of Agentic Business Process Management (APM), an extension of Business Process Management (BPM) for governing autonomous agents executing processes in organizations. From a management perspective, APM represents a paradigm shift from the traditional view on business processes. This shift is driven by the realization of process awareness by agent-oriented abstractions: software and human agents act as primary functional entities that perceive, reason, and act within explicit process frames. Thus, APM moves away from automation-oriented BPM towards systems in which autonomy is constrained, aligned, and made operational through process aware agents. We introduce the core abstractions and architectural elements required to realize APM systems and elaborate on four key capabilities that agents in APM systems must support: framed autonomy , explainability , conversational actionability , and self-modification . These capabilities jointly ensure that agents’ goals are aligned with organizational goals and that agents behave in a framed yet proactive manner in pursuing those goals. We discuss the extent to which the capabilities can be realized and identify research challenges whose resolution requires further advances in BPM, AI, and multi-agent systems. The manifesto thus serves as a roadmap for bridging these communities and for guiding the development of APM systems in practice.

The Business Process Modeling Notation (BPMN) is the de facto standard for business process modeling. While widely adopted for its intuitive graphical notation, its execution semantics described in natural language lacks a commonly agreed formal foundation, leading to variability in execution across different BPM systems (BPMSs) and increasing the risk of creating models with semantic errors costly to correct at runtime. Although many formalisms have been used to model portions of BPMN, their reasoning capabilities are mostly restricted to control-flow, making them unsuitable for semantic analysis where data and global exception handling play a central role in execution. To address this, we propose a formalization from BPMN to ConGolog, a logical concurrent processes language based on the Situation Calculus, for representing and reasoning about dynamic domains. A major innovation is using ConGolog to rigorously capture the semantics of BPMN global exceptions. Our framework supports advanced reasoning, allowing for semantic analysis of BPMN models before execution to predict runtime errors within a safe simulation setting, while laying the foundation for reasoning layers in next-generation AI-augmented BPMSs. We validate the approach through a prototype and comprehensive evaluation, demonstrating the computational feasibility of the translation and the semantic correctness of reasoning tasks.

Next activity prediction is one of the main tasks of Predictive Process Monitoring (PPM), enabling organizations to forecast the execution of business processes and respond accordingly. Deep learning models are effective at predictions, but with the price of intensive training and feature engineering, rendering them less generalizable across domains. Large Language Models (LLMs) have been recently suggested as an alternative, but their capabilities in Process Mining tasks are still to be extensively investigated. This work introduces a framework leveraging LLMs and Retrieval-Augmented Generation to enhance their capabilities for predicting next activities. By leveraging sequential information and data attributes from past execution traces, our framework enables LLMs to make more accurate predictions without additional training. We evaluate the approach on a wide range of event logs and compare it with state-of-the-art techniques. Findings show that our framework achieves competitive performance while being more adaptable across domains. Moreover, we assess early prediction capabilities, validate the significance of observed differences through statistical testing, and explore the impact of fine-tuning. Despite these advantages, we also report the framework’s limitations, mainly related to interleaving activity sensitivity and concept drifts. Our findings highlight the potential of retrieval-augmented LLMs in PPM while identifying the need for future research into handling evolving process behaviors and the development of standard benchmarks.

This paper focuses on the aspects of responsibility and ethics when using AI systems in the contexts of health and genomics. The present proposal also aims to address the important need to consider how the use of AI systems in the field of genomics affects human life and its implications. As AI systems increasingly participate in diagnostic and predictive decision-making, they introduce new challenges concerning moral agency, transparency, and human oversight. The discussion aims to examine how these technologies, while offering unprecedented analytical capabilities, simultaneously reshape the ethical landscape of medical practice and genomic research.

AI-augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems empowered by AI technology for autonomously unfolding and adapting the execution flow of business processes (BPs) within a set of potentially conflicting procedural and declarative constraints, called process framing . In this respect, framed autonomy enables an ABPMS to autonomously decide how to progress the execution of a BP, as long as the boundaries imposed by the frame are respected. Among these constraints, there could be a partial BP execution that needs to be completed, activating a different near-optimal framing that enables the BP to progress its execution. In this paper, we present an automata-based technique that pairs constraint-based framing with automated planning in AI to recommend, given a partial BP execution trace, the continuation of that trace that minimizes the violation cost of the conforming space defined by the process frame. We report on the results of experiments of increasing complexity to showcase our technique’s performance and scalability.

The Incident Management (IM) process is one of the core activities for increasing the overall security level of organizations and better responding to cyber attacks. Different security frameworks (such as ITIL and ISO 27035) provide guidelines for designing and properly implementing an effective IM process. Currently, assessing the compliance of the actual process implemented by an organization with such frameworks is a complex task. The assessment is mainly manually performed and requires much effort in the analysis and evaluation. In this paper, we first propose a taxonomy of compliance deviations to classify and prioritize the impacts of non-compliant causes. We combine trace alignment techniques with a new proposed cost model for the analysis of process deviations rather than process traces to prioritize interventions. We put these contributions into use in a system that automatically assesses the IM process compliance with a reference process model (e.g., the one described in the chosen security framework). It supports the auditor with increased awareness of process issues to make more focused decisions and improve the process’s effectiveness. We propose a benchmark validation for the model, and we show the system’s capability through a usage scenario based on a publicly available dataset of a real IM log. The source code of all components, including the code used for benchmarking, is publicly available as open source on GitHub.

Abstract Traditionally, process-aware Decision Support Systems (DSSs) have been enhanced with AI functionalities to facilitate quick and informed decision-making. In this context, AI-Augmented Business Process Management Systems have emerged as innovative human-centric information systems, blending flexibility, autonomy, and conversational capability. Large Language Models (LLMs) have significantly boosted such systems, showcasing remarkable natural language processing capabilities across various tasks. Despite the potential of LLMs to support human decisions in business contexts, empirical validations of their effectiveness for process-aware decision support are scarce in the literature. In this paper, we propose the Business Process Large Language Model (BPLLM) framework, a novel approach for enacting actionable conversations with human workers. BPLLM couples Retrieval-Augmented Generation with fine-tuning, to enrich process-specific knowledge. Additionally, a process-aware chunking approach is incorporated to enhance the BPLLM pipeline. We evaluated the approach in various experimental scenarios to assess its ability to generate accurate and contextually relevant answers to users’ questions. The empirical study shows the promising performance of the framework in identifying the presence of particular activities and sequence flows within the considered process model, offering insights into its potential for enhancing process-aware DSSs.

Within the process mining field, Deterministic Finite State Automata (DFAs) are largely employed as foundation mechanisms to perform formal reasoning tasks over the information contained in the event logs, such as conformance checking, compliance monitoring and cross-organization process analysis, just to name a few. To support the above use cases, in this paper, we investigate how to leverage Model Learning (ML) algorithms for the automated discovery of DFAs from event logs. DFAs can be used as a fundamental building block to support not only the development of process analysis techniques, but also the implementation of instruments to support other phases of the Business Process Management (BPM) lifecycle such as business process design and enactment. The quality of the discovered DFAs is assessed wrt customized definitions of fitness, precision, generalization, and a standard notion of DFA simplicity. Finally, we use these metrics to benchmark ML algorithms against real-life and synthetically generated datasets, with the aim of studying their performance and investigate their suitability to be used for the development of BPM tools.

Process Mining is a family of techniques for analyzing business process execution data recorded in event logs. Process models can be obtained as output of automated process discovery techniques or can be used as input of techniques for conformance checking or model enhancement. In Declarative Process Mining, process models are represented as sets of temporal constraints (instead of procedural descriptions where all control-flow details are explicitly modeled). An open research direction in Declarative Process Mining is whether multi-perspective specifications can be supported, i.e., specifications that not only describe the process behavior from the control-flow point of view, but also from other perspectives like data or time. In this article, we address this question by considering SAT (Propositional Satisfiability Problem) as a solving technology for a number of classical problems in Declarative Process Mining, namely, log generation, conformance checking, and temporal query checking. To do so, we first express each problem as a suitable FO (First-Order) theory whose bounded models represent solutions to the problem, and then find a bounded model of such theory by compilation into SAT.

Process Mining is a discipline that sits between data mining and business process management. The starting point of process mining is an event log, which is analyzed to extract useful insights and recurrent patterns about how processes are executed within organizations. However, often its concrete application is hampered by the considerable preparation effort that needs to be conducted by human experts to collect the required data for building a suitable event log. Instead, event logs need to be extracted from different and heterogeneous data sources, often using customized extraction scripts whose implementation requires both technical and domain expertise. While this is recognized as a relevant issue in the process mining community, literature solutions tend to be ad-hoc for particular application contexts, or not enough structured to be easily applied in practice. In this paper, we tackle this issue by proposing an interactive and general-purpose approach to support organizations in generating simulated event logs that can be employed to discover the structure of the data pipelines executed within a business process. A data pipeline is a composite workflow for processing data that is enacted as part of process execution. To assess the practical applicability of the approach, we show the results of a preliminary evaluation performed in a digital marketing scenario in the range of the recently funded H2020 DataCloud project.

Trace alignment is the problem of finding the best possible execution sequence of a business process (BP) model that reproduces an (observed) execution trace of the same BP by pinpointing where it deviates. One limiting assumption that governs the state-of-the-art alignment algorithms relies in a static cost function assigning fixed costs to all the possible types of deviations related to a BP activity, thus neglecting the specific context in which the deviation takes place and flattening the analysis of its potential impact. In this paper, we relax this assumption by providing a technique based on theoretic manipulations of deterministic finite state automata (DFAs) to build optimal alignments driven by dedicated cost models that assign context-dependent variable costs to the deviations. We show how the algorithm can be implemented relying on automated planning in Artificial Intelligence (AI), which is proven to be an effective tool to address the alignment task in the case of BP models and event logs of remarkable size. Finally, we report on the results of experiments conducted in a real-life case study on incident management and on larger synthetic ones performed through three well-known planning systems to showcase the performance, scalability and versatility of our technique.

As social robots become more prominent in our lives, their interaction with humans takes an increasing role, and new collaborative scenarios emerge. This development brings the need to realize robust test methods enabling the design and evaluation of Human-Robot Interaction (HRI) teaming tasks to prove functionality and promote adoption. In this article, we present a general-purpose and repeatable methodology for conducting studies in collaborative HRI in the range of robotic competitions. The methodology includes a step-by-step approach to design HRI teaming tasks tailored to be enacted in a robotic competition and to evaluate the performance of social robots to execute the designed tasks, exploring the relationship between robots’ performance and user perceptions based on the feedback of the users participating to such tasks. We assess the feasibility of the methodology to design and evaluate an HRI teaming task in the context of “Smart CIties RObotics Challenges” (SciRoc) competition, which targets at investigating the impact of social of robots in smart cities.

Declarative business process (BP) models define the behavior of BPs as a set of temporal constraints, which can be summarized as a deterministic finite state automaton (DFA). Declarative BP discovery aims at inferring such constraints from event logs. To this aim, it requires as additional input the set of candidate constraints to be verified with respect to the event log. Intuitively, this restricts the discovery task to a conformance checking activity between a predefined set of constraint templates and an event log, preventing to learn any observed behavior that is not captured by those templates. In this paper, we investigate how to leverage Model Learning (ML) for the automated discovery of the DFA underlying the behavior of a declarative BP model, without using any further a-priori information in addition to the event log. To assess the quality of the discovered DFA, we introduce a novel definition of the standard process mining quality metrics, i.e., precision, generalization and simplicity, tailored to DFAs. Finally, a preliminary evaluation performed with real-life logs shows that ML enables to generate extremely simpler DFAs than state-of-the-art BP declarative discovery techniques, keeping similar values of precision and generalization.

Organisations possess and continuously generate huge amounts of static and stream data, especially with the proliferation of Internet of Things technologies. Collected but unused data, i.e., Dark Data, mean loss in value creation potential. In this respect, the concept of Computing Continuum extends the traditional more centralised Cloud Computing paradigm with Fog and Edge Computing in order to ensure low latency pre-processing and filtering close to the data sources. However, there are still major challenges to be addressed, in particular related to management of various phases of Big Data processing on the Computing Continuum. In this paper, we set forth an ecosystem for Big Data pipelines in the Computing Continuum and introduce five relevant real-life example use cases in the context of the proposed ecosystem.

In recent years and with its accessibility, the use of online shopping for clothing has increased. Virtual Dressing Rooms (VDRs) represent an effective way to enact the ability to "try before buying, thus removing an important obstacle for online shopping. While most of the VDR tools that have been realized so far are based on Augmented Reality and are installed directly inside the retail shops, this paper proposes a real-time VDR application titled V-DOOR that leverages the features of Oculus Rift to create an immersive experience that enables customers to try on clothes virtually in the comfort of their own home rather than physically in the retail shop.

The use of videogames has become an established tool to educate users about various topics. Videogames can promote challenges, co-operation, engagement, motivation, and the development of problem-solving strategies, which are all aspects with an important educational potential. In this paper, we present the design and realization of CyberVR, a Virtual Reality (VR) videogame that acts as an interactive learning experience to improve the user awareness of cybersecurity-related issues. We report the results of a user study showing that CyberVR is equally effective but more engaging as learning method toward cybersecurity education than traditional textbook learning.

In this paper, we present the outcomes of the first user study designed and evaluated in the context of the Smart City Robotics Challenge (SciRoc Challenge). The study presented in this paper has the main novelty of having been devised and implemented in a realistic environment: a robot competition where robot tasks were developed by participant teams, robots were fully autonomous, and user questionnaires were part of the competition score. Specifically, our study was performed over a scenario configured to instruct a robot to take an elevator of a shopping mall asking for customers support. Leveraging the dedicated questionnaire designed for the tested scenario, we validated the experimental hypothesis if user perception of robots' behaviour may be influenced by the user's gender. In the end, we discuss the results of our study.

The arising of new technologies ranging from smartphones to social networks is constantly increasing interactions between people. In the Information and Communications Technology (ICT) community, adapting technology to human nature is the key concern of User-Centered Design (UCD). However, UCD tends to neglect the emerging social dimension of technology: users are consulted in the design process, but they do not have any direct involvement or creative control over the developed technological solutions. On the other hand, the collaborative and social nature of the design process is getting increasingly explicit in the Product Design community, where well-established participatory approaches are applied to involve stakeholders, designers, and end-users in the creative process of new products. In this paper, we first provide a deep analysis of the state of the art of participatory approaches in the research literature. Then, we investigate how their integration with UCD leads not only to empower the role of end-users as active collaborators of designers towards a more democratic crowd-based UCD process, but also to create innovation in the design process. We advocate that such innovation can be obtained by giving the right voice not only to the users who reach consensus in the design process but also to the marginals. We provide an explorative model, some experiments, and a sketch of the user interface to support our claim.

Process mining methods allow analysts to use logs of historical executions of business processes in order to gain knowledge about the actual behavior of these processes. One of the most widely studied process mining operations is automated process discovery. An event log is taken as input by an automated process discovery method and produces a business process model as output that captures the control-flow relations between tasks that are described by the event log. In this setting, this paper provides a systematic comparative evaluation of existing implementations of automated process discovery methods with domain experts by using a real life event log extracted from an international software engineering company and four quality metrics: understandability, correctness, precision, and usefulness. The evaluation results highlight gaps and unexplored trade-offs in the field and allow researchers to improve the lacks in the automated process discovery methods in terms of usability of process discovery techniques in industry.

In this paper, we present the results of a recent user study that investigates if user perception of HRI in social contexts may be affected by changing the interaction modality with the robot. Leveraging on Robot Social Attribute Scale (RoSAS) survey and on a statistical analysis, our results show that, in some interaction modalities, a greater feeling of discomfort is felt by users interacting with the robot. Interestingly, results also show the influence of users' gender on the user perception.

In this paper, we present an approach to develop a new type of questionnaire for evaluating customers' perceptions in the upcoming Smart CIty RObotic Challenge (SciRoc). The approach consists of two steps. First, it relies on interviewing experts on Human-Robot Interaction (HRI) to understand which robot's behaviours can potentially affect the users' perceptions during a HRI task. Then, it leverages a user survey to filter out those robot's behaviours that are not significantly relevant from the end user perspective. We concretely enacted our approach over a specific scenario developed in the context of SciRoc, which instructs a robot to take an elevator of a shopping mall asking support to the customers of the mall. The results of the survey have allowed us to derive a final list of 17 behaviours to be captured in the questionnaire, which has been finally developed relying on a 5-point Likert-scale.

The increasing importance of data in business processes has led to the emergence of data-centric business process management, which deviates from the widely used activity-centric paradigm. Data-centric approaches set their focus on data, aiming at supporting data-intensive business processes and increased process flexibility. The objective of this article is to gain profound insights into the maturity of different data-centric approaches as well as their capabilities. In particular, this article will provide a framework for systematically evaluating and comparing data-centric approaches, with regard to the phases of the business process lifecycle. To this end, a systematic literature review (SLR) was conducted with the goal of evaluating the capabilities of data-centric process management approaches. The SLR comprises 38 primary studies which were thoroughly analyzed. The studies were categorized into different approaches, whose capabilities were thoroughly assessed. Special focus was put on the tooling and software of the approaches. The article provides the empirically grounded DALEC framework to evaluate and compare data-centric approaches. Furthermore, the results of the SLR offer insights into existing data-centric approaches and their capabilities. Data-centric approaches promise better support of loosely structured and data-intensive business processes, which may not be adequately represented by activity-centric paradigms.

Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy, and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures, and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering 12 publicly-available real-life event logs, 12 proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.

A learnable system allows a user to know how to perform correctly any task of the system after having executed it a few times in the past. In this paper, we propose an approach to measure the learnability of interactive systems during their daily use. We rely on recording in a user log the user actions that take place during a run of the system and on replaying them over the system interaction models, which describe the expected ways of executing system tasks. Our approach identifies deviations between the interaction models and the user log and assesses their weight through a fitness value. By measuring the rate of the fitness value for subsequent executions of the system we are able not only to understand if the system is learnable with respect to its tasks, but also to quantify its degree of learnability over time and to identify potential learning issues.

In the framework of the AVI 2018 Conference, the interuniversity center ECONA has organized a thematic workshop on "Digital Interaction: where are we going?". Six contributions from the ECONA members investigate different perspectives around this thematic.

Declarative approaches to business process modeling allow to represent loosely-structured (declarative) processes in flexible scenarios as a set of constraints on the allowed flow of activities. However, current graphical notations for declarative processes are difficult to interpret. As a consequence, this has affected widespread usage of such notations, by increasing the dependency on experts to understand their semantics. In this paper, we tackle this issue by introducing a novel visual declarative notation targeted to a more understandable modeling of declarative processes.

The world is entering its 4th Industrial Revolution, a new era of manufacturing characterized by ubiquitous digitization and computing. One industry to benefit and grow from this revolution is the fashion industry, in which Europe (and Italy in particular) has long maintained a global lead. To evolve with the changes in technology, we developed the IT-SHIRT project. In the context of this project, a key challenge relies on developing a virtual dressing room in which the final users (customers) can virtually try different clothes on their bodies. In this paper, we tackle the aforementioned issue by providing a critical analysis of the existing body scanning solutions, identifying their strengths and weaknesses towards their integration within the pipeline of virtual dressing rooms.

One major task in business process management is that of aligning real process execution traces to a process model by (minimally) introducing and eliminating steps. Here, we look at declarative process specifications expressed in Linear Temporal Logic on finite traces (LTLf). We provide a sound and complete technique to synthesize the alignment instructions relying on finite automata theoretic manipulations. Such a technique can be effectively implemented by using planning technology. Notably, the resulting planning-based alignment system significantly outperforms all current state-of-the-art ad-hoc alignment systems. We report an in-depth experimental study that supports this claim.

The increasing application of process-oriented approaches in new challenging dynamic domains beyond business computing (e.g., healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex knowledge-intensive processes in such domains. A knowledge-intensive process is influenced by user decision making and coupled with contextual data and knowledge production, and involves performing complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and knowledge-intensive processes must be robust to unexpected conditions and adaptable to unanticipated exceptions, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. To tackle this issue, in this paper we present SmartPM, a model and a prototype Process Management System featuring a set of techniques providing support for automated adaptation of knowledge-intensive processes at runtime. Such techniques are able to automatically adapt process instances when unanticipated exceptions occur, without explicitly defining policies to recover from exceptions and without the intervention of domain experts at runtime, aiming at reducing error-prone and costly manual ad-hoc changes, and thus at relieving users from complex adaptations tasks. To accomplish this, we make use of well-established techniques and frameworks from Artificial Intelligence, such as situation calculus, IndiGolog and classical planning. The approach, which is backed by a formal model, has been implemented and validated with a case study based on real knowledge-intensive processes coming from an emergency management domain.

In this short poster paper, we present an application for suggesting attractions to be visited by users, based on social signal processing techniques.

We propose an approach to measure the learnability of an interactive system. Our approach relies on recording in a user log all the user actions that take place during a run of the system and on replaying them over one or more interaction models of the system. Each interaction model describes the expected way of executing a relevant task provided by the system. The proposed approach is able to identify deviations between the interaction models and the user log and to assess the weight of such deviations through a fitness value, which estimates how much a log adheres to the models. Our thesis is that by measuring the rate of such a fitness value for subsequent executions of the system we can not only understand if the system is learnable with respect to its relevant tasks, but also to identify potential learning issues.

Providing operational support to clinicians during their daily activities in hospital wards is a challenge for information technologies. In particular, solutions should provide very usable user interfaces, possibly deployed on mobile devices, and should be able to enact and monitor the execution of clinical guidelines. In this paper, we present the preliminary outcomes of the TESTMED project, a small project in which vocal and touch interfaces are being experimented as a viable solution for clinicians' interaction with the system, and a process-aware approach has been undertaken for the (semi-)automation of clinical guidelines.

Process Management Systems (PMSs, a.k.a. Workflow Management Systems - WfMSs) are currently more and more used as a supporting tool to coordinate the enactment of processes. In real world scenarios, the environment may change in unexpected ways so as to prevent a process from being successfully c