Services for data provisioning can be extremely valuable. Organizations wrestle with deciding whether to “expose or not to expose” their data through Web APIs and, depending on the nature of their business, mull over potential benefits and drawbacks. This is because data can be exploited and misused in unanticipated ways. Rate restriction techniques are frequently used to manage data access and protect back-end computing resources. The best choice for the maximum rate at which information can be “safely” provided to clients will determine its effectiveness. An important situation involves formal service-level agreements that govern the quality of services provided by public governments and private businesses. These firms might need to decide on a rate cap for their Web APIs that prevents unauthorized clients from accurately calculating service levels while still enabling the development of valuable value-added services. In this paper, we propose a statistical model for this problem and a technique, based on sampling tools, following brute-force, binary search, and heuristic search methods, to select an appropriate rate limit, and demonstrate its validity through a case study involving a large Italian bus company.

Integrating multiple (sub-)systems is essential to create advanced Information Systems. Difficulties mainly arise when integrating dynamic environments, e.g., the integration at design time of not yet existing services. This has been traditionally addressed using a registry that provides the API documentation of the endpoints. Large Language Models (LLMs) have shown to be capable of automatically creating system integrations (e.g., as service composition) based on this documentation but require concise input due to input token limitations, especially regarding comprehensive API descriptions. Currently, it is unknown how best to preprocess these API descriptions. In the present work, we (i) analyze the usage of Retrieval Augmented Generation (RAG) for endpoint discovery and the chunking, i.e., preprocessing, of state-of-practice OpenAPIs to reduce the input token length while preserving the most relevant information. To further reduce the input token length for the composition prompt and improve endpoint retrieval, we propose (ii) a Discovery Agent that only receives a summary of the most relevant endpoints and retrieves specification details on demand. We evaluate RAG for endpoint discovery using (iii) a proposed novel service discovery benchmark SOCBench-D representing a general setting across numerous domains and the real-world RestBench benchmark, first, for the different chunking possibilities and parameters measuring the endpoint retrieval accuracy. Then, we assess the Discovery Agent using the same test data set. The prototype shows how to successfully employ RAG for endpoint discovery to reduce the token count. Our experiments show that endpoint-based approaches outperform naüve chunking methods for preprocessing. Relying on an agent significantly improves precision while being prone to decrease recall, disclosing the need for further reasoning capabilities.

In recent years, growing attention to the gut microbiota has led to increasing use of complex clinical reports, such as those provided by microbiome and multi-omic analyses. Irritable bowel syndrome (IBS) is a relevant area of application, characterized by a chronic functional disorder of the gastrointestinal tract. Having clear and easily interpretable diagnostic tools increases the capacity of diagnosis and helps patients to better understand the content of the reports, which are often static and rich in scientific data, making them complex to understand for both patients and non-specialist physicians. This work proposes the development of a dynamic platform for the visualization and interactive management of microbiome clinical reports. Starting from existing reports, a prototype web interface capable of presenting data in a modular, adaptable, and customizable way has been designed and proposed. The prototype also allows the generation of reports in PDF format, both in a dynamic version (with the possibility of filtering and exploring different sections) and in a static version (useful for printing or clinical sharing). The added value of this solution lies in readability and quick access to the information.

Microbiome sequencing offers significant potential for advancing clinical diagnostics, but its adoption is hindered by challenges in data processing, standardization, and the translation of complex genomic data into actionable clinical insights. The GenDAI project addresses these challenges by developing a novel, integrated medical diagnostics platform that leverages Artificial Intelligence (AI), powered by Genomic Foundation Models (GFMs), to accelerate and improve the analysis of microbiome data. The platform's primary goal is to provide a fully automated, reproducible, and compliant end-to-end solution, from data ingestion to clinical reporting, to support personalized medicine, with an initial focus on Inflammatory Bowel Disease (IBD). This paper presents an overview of GenDAI's vision, outlining its user-centered methodological approach and the conceptual architecture of its core components. The architecture integrates four key pillars: (1) a fully automated and auditable diagnostics workflow, (2) a secure and compliant cloud platform for long-term data management based on Open Archival Information System (OAIS) and Findable, Accessible, Interoperable, Reusable (FAIR) principles, (3) an advanced AI engine for biomarker discovery using GFMs, and (4) interactive, usercentered reporting tools designed to enhance explainability and clinical trust. By providing a holistic and ethically-grounded framework, GenDAI aims to bridge the gap between advanced genomic research and practical clinical application.

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.

Artificial intelligence has become integral to the life sciences, enabling large-scale data analysis and accelerating discovery. However, deploying these analytical capabilities depends on cloud infrastructures that are often complex to design, validate, and operate. Current approaches rely heavily on manual, time-consuming configuration by experts. This paper explores how artificial intelligence, specifically Large Language Models, can support not only genomic data analysis but also the automated creation of the underlying computational infrastructure. We propose a methodology that translates naturallanguage user stories and requirements into cloud-architectural specifications and containerized deployments, bridging the gap between user intent and executable infrastructure. Preliminary results indicate that LLM-driven synthesis can reduce human effort in defining container relationships and deployment logic, advancing automation in cloud-native bioinformatics.

This paper presents a cloud-native architecture for clinical metagenomic diagnostics developed as part of the Horizon Europe project GenDAI. The architecture integrates automated, reproducible, and auditable workflows with deterministic elasticity, compliant data stewardship, explainable Artificial Intelligence (AI), and verifiable reporting. Requirements derived from clinical practice inform a unified modeling, implementation, and evaluation strategy for a modular platform that combines workflow orchestration, data governance, AI-powered modeling, and cloud-native reporting. The system embeds provenance-bydesign, policy-as-code enforcement, and deterministic elasticity across all layers to enable reproducible, compliant, and trustworthy metagenomic diagnostics. This work provides a principled pathway for translating research-grade tools into regulator-ready diagnostic services while maintaining transparency, reproducibility, and long-term trust.

The increasing adoption of Large Language Models (LLMs) has significantly lowered technical barriers across various domains, enabling tasks such as code generation, database querying, and service composition. This work explores the potential of LLMs to facilitate human access to heterogeneous data in Cyber-Physical Systems (CPS). The proposed system interprets natural language queries and, by leveraging in-context learning, dynamically synthesizes data integration pipelines that compose data services, thus enabling access to information from multiple sources. The execution of a pipeline generates a table that fulfills the input query, effectively integrating and structuring the retrieved data. This paper presents the design and implementation of the proposed solution and its evaluation using a real-world case study and the BIRD benchmark dataset. The results demonstrate its effectiveness in improving data retrieval, data service design, and execution in CPS.

Balancing the utilization of model resources with the accuracy of the prediction is a significant challenge in energy consumption analysis. It is then essential to select models that combine computational efficiency with suitable predictive accuracy to support sustainable environments. We propose an approach to evaluate the sustainability level of popular machine learning (ML) and deep learning (DL) models, offering decision-makers the flexibility to select the most appropriate model, considering resource efficiency and prediction reliability. It is demonstrated using a novel dataset comprising detailed energy consumption data from four major social mass-housing buildings in Rome. It is collected over two years and is enriched with energy bills, resident surveys, and daily usage patterns.

ABSTRACT The demand for efficient functional size measurement (FSM) methods in the competitive software market today is undeniable. However, incomplete and imprecise system specifications pose significant challenges, particularly in scenarios that require fast, flexible, and accurate software size estimation, such as public tenders. Although the integration of conceptual models within FSMs offers a promising solution to these issues, a systematic exploration of such methods remains largely unexplored. This work evaluates FSM methods that integrate conceptual models by analyzing studies from the past 20 years. It highlights key contributions and advances in proposed conceptual model‐based FSM methods. In addition, the study examines their limitations and challenges, offering insights for future improvements. A systematic literature review (SLR) was conducted to guide the research process. The review was organized around three research questions, each targeting the study's key objectives: (1) to explore FSM methods utilizing conceptual models, (2) to summarize proposals for their improvement, and (3) to identify the limitations of the proposed enhancements. Primary studies span two decades (2004–2024), with peaks in 2008 and 2015, averaging one to two studies annually. Of the 1371 initial studies, 13 were selected using strict criteria. These studies are categorized into Measurement Techniques (30.77%), Automation (38.46%), and Application‐Specific topics (30.77%). The contributions of the primary studies are analyzed in terms of their approaches Repeatability and Validation. Repeatability is assessed by examining whether the primary studies proposed a formal model when using real datasets. In contrast, Validation focuses on whether the studies were tested in real‐world projects. A total of 46.15% of the primary studies utilize formal models, whereas 53.85% rely on nonformal models, although dataset size is often unspecified. Most studies validate their methods using 1 to 30 projects. Common Software Measurement International Consortium (COSMIC) is the most widely used FSM method (69.23%), followed by the Function Point Analysis (FPA) (15.38%) and custom Methods (15.38%), with conceptual UML models appearing in 84.61% of the studies. Key limitations, including Scalability and Generalizability, Complexity Robustness, and Flexibility, persist across all categories. Notably, Scalability and Generalizability was identified as a limitation in 75% of Measurement Techniques studies, 80% of Automation studies, and 75% of Application‐Specific studies, while Flexibility challenges were most pronounced, affecting 100% of Application‐Specific studies. The limited number of primary studies underscores a substantial research gap in conceptual model‐based FSM methods. Future research should focus on developing formal models to enhance theoretical rigor, leveraging real‐world datasets for validation, providing comprehensive methodological descriptions, and standardizing validation practices. Additionally, prioritizing advancements in FSM methods by improving scalability, generalizability, and flexibility is crucial. These enhancements will enable FSM methods to effectively manage complex systems, adapt across diverse software domains, and address application‐specific requirements, ensuring their continued relevance in dynamic and evolving software development environments.

Widespread use of IoT, like surveillance cameras, raises privacy concerns in citizens’ lives. However, limited studies explore AI-based automatic recognition of criminal incidents due to a lack of real data, constrained by legal and privacy regulations, preventing effective training and testing of deep learning models. To address dataset limitations, we propose using generative technology and virtual gaming data, such as the Grand Theft Auto (GTA-V) platform. However, it’s unclear if synthetic data accurately mirrors real-world videos for effective deep learning model performance. This research aims to explore the potential of identifying criminal scenarios using deep learning models based on gaming data. We propose a deep-learning violence detection framework using virtual gaming data. The 3-stage deep learning model focuses on person identification and violence activity recognition. We introduce a new dataset for supervised training and find virtual persons closely resembling real-world individuals. Our research demonstrates a 15% higher accuracy in identifying violent scenarios compared to three established real-world datasets, showcasing the effectiveness of a serious gaming approach.

Abstract The continuous evolution of digital technologies applied to the more traditional world of industrial automation led to Industry 4.0, which envisions production processes subject to continuous monitoring and able to dynamically respond to changes that can affect the production at any stage (resilient factory). The concept of agility, which is a core element of Industry 4.0, is defined as the ability to quickly react to breaks and quickly adapt to changes. Accurate approaches should be implemented aiming at managing, optimizing and improving production processes. In this vision paper, we show how process management (BPM) can benefit from the availability of raw data from the industrial internet of things to obtain agile processes by using a top-down approach based on automated synthesis and a bottom-up approach based on mining.

In recent decades, manufacturing practices have undergone a significant transformation, with the integration of computers and automation playing a central role. Concurrently, there has been a growing interest in utilizing intelligent techniques to effectively manage manufacturing processes. These processes entail the seamless integration of various activities across the supply chain. Given the diverse range of actors in a supply chain, each one with distinct characteristics such as cost, quality, and probability of failure, task assignment becomes a crucial challenge. In such a complex scenario, manual decision-making becomes impractical, necessitating the adoption of automated techniques to effectively address these challenges in a resilient and adaptive manner. This article proposes a service-oriented approach to model each manufacturing actor within the supply chain. Furthermore, it categorizes automated synthesis approaches for smart manufacturing on the basis ofi)the characteristics of each actor, which are retrieved by their Industrial API, andii)the goal(s) of the manufacturing process. Finally, the article evaluates three distinct approaches that implement automated synthesis techniques for composing services and generating operational plans.

Does it make sense to develop specialized writing assistants in the era of LLMs - Large Language Models? In this paper, we present CICERO, a specialized writing assistant we have developed for writing (pieces of) sentences in the Italian legal system. Our proposed solution involves fine-tuning a transformer on a pre-processed corpus of Italian civil judgments, resulting in a novel language model that can be deployed as a writing assistant for legal users to improve text writing efficiency. The model can also be further fine-tuned for use in other law-related natural language processing tasks. The experimental validation of CICERO allows us also to draw interesting insights on the meaningful, if any, of developing specialized tools for assisting specific classes of users and knowledge workers, in an era in which we witness the widespread adoption of LLMs.

Recent years witnessed a growing interest in the employment of intelligent techniques for the management of manufacturing processes in smart manufacturing. These processes may include tens of resources distributed among several different companies composing the supply chain. The status of the different resources evolves over time in terms of cost, quality, and probability of breakdunavailability, thus requiring the process to be adaptive and resilient to disruptions. Due to the high number of involved resources, making decisions manually soon become unfeasible, thus requiring automated techniques to solve the problem. In this paper, we discuss the potential and limitations of automated reasoning techniques for making modern supply chains adaptive and resilient by relying on the information provided by resources through their APIs.

Human activities represent a major source of information for smart home automation. While performing their daily activities, humans trigger sensors producing measurements that flow into a sensor log. Vast majority of techniques to recognize and exploit the occurrences of human activities are supervised, requiring the log to be manually labeled in correspondence of the onset and the end of each activity repetition. This task requires a considerable effort by the final user, resulting in imprecise labeling tampering the performance of algorithms. In this paper, we propose an unsupervised technique allowing to automatically segment smart home logs containing position sensor measurements. The proposed technique exploits information about the position of the human to automatically extract basic actions, which are then segmented on a temporal basis and clustered. The approach is evaluated against a state-of-the-art dataset.

Effort estimation poses a significant challenge in software development as it encompasses the determination of the necessary time and resources for the completion of a project. Several approaches have been proposed to estimate the effort in software development, but they often exhibit various limitations which can be grouped into three main categories. Firstly, these methods heavily rely on expert judgment, introducing subjectivity and variations in estimation. Secondly, they can be complex to comprehend and implement, requiring a deep understanding of software metrics and project attributes. Finally, they usually need manual effort in collecting and analyzing data, leading to potentially time-consuming tasks and the possibility of errors. This paper presents a solution that addresses these challenges by utilizing established conceptual models like Business Process Management Notation (BPMN) to develop a comprehensive system description through the extraction of entities and their relationships as semantic triples. This approach lays the foundation for identifying conceptual micro-services, enabling a precise breakdown of system functionalities, and incorporates prompt engineering with ChatGPT for suitable effort estimation of each micro-service.

The demand for protection and security of physical spaces and urban areas increased with the escalation of terroristic attacks in recent years. We envision with the proposed cyber–physical systems and spaces, a city that would indeed become a smarter urbanistic object, proactively providing alerts and being protective against any threat. This survey intend to provide a systematic multivocal literature survey comprised of an updated, comprehensive and timely overview of state of the art in counter-terrorism cyber–physical systems, hence aimed at the protection of cyber–physical spaces. Hence, provide guidelines to law enforcement agencies and practitioners providing a description of technologies and best practices for the protection of public spaces. We analyzed 112 papers collected from different online sources, both from the academic field and from websites and blogs ranging from 2004 till mid-2022. (a) There is no one single bullet-proof solution available for the protection of public spaces. (b) From our analysis we found three major active fields for the protection of public spaces: Information Technologies, Architectural approaches, Organizational field. (c) While the academic suggest best practices and methodologies for the protection of urban areas, the market did not provide any type of implementation of such suggested approaches, which shows a lack of fertilization between academia and industry. The overall analysis has led us to state that there is no one single solution available, conversely, multiple methods and techniques can be put in place to guarantee safety and security in public spaces. The techniques range from architectural design to rethink the design of public spaces keeping security into account in continuity, to emerging technologies such as AI and predictive surveillance.

The prevalence of video content consumption prompted book publishers to adopt this medium as a way to advertise books, through so called book trailers. Beyond advertising, a book’s video presentation may provide information to potential future readers so they can make an informed choice on whether consider reading it or not. StoryBook is a software tool that, through a process of knowledge extraction and media collection, supports the semi-automatic creation of multimedia trailers of books. A proof-of-concept of the tool and its user interface has been built and is available online.

Smart homes represent examples of cyber-physical environments realizing the paradigm known as ambient intelligence. An information system supporting ambient intelligence takes as input raw sensor measurements and analyzes them to eventually make decisions following final user preferences. Unfortunately, algorithms in this research area are mostly supervised, thus requiring a manual labeling of training instances usually involving final users in annoying and imprecise training sessions. In this paper, we propose a methodology allowing, given a sensor log, to automatically segment human habits by applying a bottom-up discretization strategy to the timestamp attribute of the sensor log. In particular, we show how classical quality measures, computed over Petri nets automatically mined from sensor logs filtered by timestamp, can be used as an heuristic to drive the discretization process, thus providing a likely subdivision of the day in human habits.

Abstract This paper proposes a general approach for using conversational interfaces such as chatbots to offer adaptive learning of business processes in an environment involving different actors. Adaptivity concerns both the content being proposed, the sequence of learning items, and the way the conversation is conducted. The original approach allows the development of sustainable chatbots and empowers various non-technical actors (authors, teachers, publishers, and learners) to control the chatbot features directly. The aCHAT-WF framework (adaptive CHATbot for WorkFlows), proposed in this paper for managing conversational interfaces, conceptually represents all the aspects related to a conversation about business processes, with different facets for the user, the conversation flow, and the conversation contents, combining them to obtain a flexible interaction with the user. The paper focuses on the different preparation phases for instructional material based on Business Process Modeling Notation (BPMN) models, separating the different roles involved in the construction of a chatbot for teaching business processes and with the possibility of defining different styles for the interaction with the users. The proposed method is configuration-driven, to facilitate the separation of the different aspects of the control of the interaction and the delivery of contents.

In this demo paper, we present ARCA, a visual-search based system that allows the semantic exploration of a bookstore. Navigating a domain-specific knowledge graph, students and researchers alike can start from any specific concept and reach any other related concept, discovering associated books and information. To achieve this paradigm of interaction we built a prototype system, flexible and adaptable to multiple contexts of use, that extracts semantic information from the contents of a books' corpus, building a dedicated knowledge graph that is linked to external knowledge bases.

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.

The technological foundation of smart manufacturing consists of cyber-physical systems and the Internet-of-Things (IoT). Each IoT device in a smart factory can be coupled with a digital twin, that is, a dynamic virtual representation of the physical system across its life-cycle using real-time sensor data. Currently, the manufacturing process itself, the involved devices, and how they interact, is designed by human experts in a traditional way. We envision an architecture where humans can instead specify a goal and take advantage of technologies such as digital twins to automatically compose the corresponding physical processes, sharing some analogies with the notion of Web service composition.

Data-provisioning services can provide high value. Since data can be used and misused in unpredictable ways, organizations deal with the dilemma "to expose or not to expose" their data through Web APIs and - depending on the nature of their business - ponder possible opportunities and pitfalls. Rate limiting strategies are widely employed to control access to data and to safeguard back-end computational resources. However, their effectiveness depends on a good choice of the maximum allowed rate at which information can be "safely" released to users. A relevant scenario is represented by public administrations and private companies providing services whose quality is regulated by formal business agreements on the service levels. These organizations may need to choose a rate limit for their Web APIs that prevent unauthorized users to compute service levels with an high confidence while still allowing the creation of useful value-added services. In this paper, we propose a general statistical model for this problem and a technique, based on uniform sampling tools, to select an appropriate rate limit, and we demonstrate its validity through a case study involving a large bus company.

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.

Summary Open‐Source INTelligence is intelligence based on publicly available sources such as news sites, blogs, forums, etc. The Web is the primary source of information, but once data are crawled, they need to be interpreted and structured. Ontologies may play a crucial role in this process, but because of the vast amount of documents available, automatic mechanisms for their population are needed, starting from the crawled text. This paper presents an approach for the automatic population of predefined ontologies with data extracted from text and discusses the design and realization of a pipeline based on the General Architecture for Text Engineering system, which is interesting for both researchers and practitioners in the field. Some experimental results that are encouraging in terms of extracted correct instances of the ontology are also reported. Furthermore, the paper also describes an alternative approach and provides additional experiments for one of the phases of our pipeline, which requires the use of predefined dictionaries for relevant entities. Through such a variant, the manual workload required in this phase was reduced, still obtaining promising results.

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.

Teaching STEM is a promising application domain for game-based instructional methods. In this paper we present a serious game organized as a role playing game: players learn how to inhabit the headspace of someone other than their primary ego identity, offering them the chance to develop a stronger sense of empathy. The same empathy is established between the player and her Intelligent Pedagogical Agent, which should guide the player into the Virtual Learning Environment and trough the game as well. We present the ongoing development of the game, and a preliminary validation of the Intelligent Pedagogical Agent to show its effectiveness with teenager students.

In this paper, we discuss the application of concept of data quality to big data by highlighting how much complex is to define it in a general way. Already data quality is a multidimensional concept, difficult to characterize in precise definitions even in the case of well-structured data. Big data add two further dimensions of complexity: (i) being “ very ” source specific , and for this we adopt the interesting UNECE classification, and (ii) being highly unstructured and schema-less , often without golden standards to refer to or very difficult to access. After providing a tutorial on data quality in traditional contexts, we analyze big data by providing insights into the UNECE classification, and then, for each type of data source, we choose a specific instance of such a type (notably deep Web data, sensor-generated data, and Twitters/short texts) and discuss how quality dimensions can be defined in these cases. The overall aim of the paper is therefore to identify further research directions in the area of big data quality, by providing at the same time an up-to-date state of the art on data quality.

Summary Current smart spaces require more and more sophisticated sensors able to acquire the state of the environment in order to provide advanced and customized services. Among the most important environmental variables, locations of users and their identities represent a primary concern for smart home applications. Despite some years of investigation in indoor positioning, the availability of systems designed as components pluggable into complex home automation platforms is limited. We presentPeopleLocalizationandTracking forHomE Automation (PLaTHEA), a vision‐based indoor localization system specifically tailored for Ambient Assisted Living applications. PLaTHEA features a novel technique to acquire a stereo video stream from a couple of independent (and not synchronized) network‐attached cameras, thus easing its physical deployment. The input stream is processed by integrating well‐known techniques with a novel tracking approach targeted to indoor spaces. The system has a modular architecture that offers clear interfaces exposed as Web services, and it can run on off‐the‐shelf and cheap hardware (both in terms of sensing devices and computing units). We evaluated PLaTHEA in real usage conditions and reported the measured performance in terms of precision and accuracy. Low light, crowded and large monitored environments might slightly decrease the performance of the system; nevertheless, the results here presented show that it is perfectly suitable to be employed in the typical domestic day‐to‐day life settings. Copyright © 2014 John Wiley & Sons, Ltd.

Declarative approaches are particularly suitable for modeling highly flexible processes. They especially apply to artful processes, i.e., rapid informal processes that are typically carried out by those people whose work is mental rather than physical (managers, professors, researchers, engineers, etc.), the so called “knowledge workers”. This paper describes MINERful++, a two-step algorithm for an efficient discovery of constraints that constitute declarative workflow models. As a first step, a knowledge base is built, with information about temporal statistics gathered from execution traces. Then, the statistical support of constraints is computed, by querying that knowledge base. MINERful++is fast, modular, independent of the specific formalism adopted for representing constraints, based on a probabilistic approach and capable of eliminating the redundancy of subsumed constraints.

Sensors have become increasingly deployed in many areas, and especially in industrial engineering settings. Sensor data is however temporal, massive, and multidimensional in nature. Such characteristics make it difficult to render sensor data for visual analysis. Although there exist potential visualizations for sensor data, there is very little specific guidance in literature on the visualization of sensor data in order to support industrial engineers in decision making. Existing visualizations are not always readily applicable to the domain of industrial engineering. This paper sheds more light on the area and proposes a visualization environment for supporting industrial engineers in their tasks.

Energy management, and in particular its efficient optimization, is one of the hot trends in the current days, both at the enterprise level (optimization of whole corporate/government buildings) and single-citizens' homes. Energy efficiency is generally function of out-door techniques -- renewable energy, smart energy production and distribution, etc. -- and in-door techniques; in particular, very few energy managers -- each of us can be an energy manager of his own home - can state "who, when and why is consuming", conversely this knowledge is fundamental in order to diminish wasting of energy. Recent studies show that the energy wasted in the overall consumption is about the 30% of the total amount; examples of potential energy wasting are printers and PCs on during the night, status LED of different devices (TV, set-top-box, etc.) and/or lights, lights during normal day-light time, etc.

Various visualization techniques for correlated multiple quantitative data sets, which allow a researcher to explore a character of content, are fairly common tools in the visual analysis. The available methods and techniques for a displaying of different diagram types, are suitable for a partial solution to this kind of a practical problem. This paper represents an overview for some existing tools and techniques, regarding the quantitative data visualization. As a key part of the paper, the design guidelines for a visual construction of correlated quantitative data values, which are mostly applicable for interactive techniques, are presented.

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.

SUMMARY Nowadays, process‐aware information systems (PAISs) are widely used for the management of ‘administrative’ processes characterized by clear and well‐defined structures. Besides those scenarios, PAISs can be used also in mobile and pervasive scenarios, where process participants can be only equipped with smart devices, such as personal digital assistants. None of existing PAISs can be entirely deployed on smart devices, making unfeasible its usage in highly mobile scenarios. This paper presents ROME4EU, a mobile PAIS developed for being applied to the coordination of emergency operators, and an extensive validation of the system, both in term of performances and usability/acceptability by the users. Copyright © 2011 John Wiley & Sons, Ltd.

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

With organizations increasingly depending on Web services to build complex applications, security and privacy concerns including the protection of access control policies are becoming a serious issue. Ideally, service providers would like to make sure that clients have knowledge of only portions of the access control policy relevant to their interactions to the extent to which they are entrusted by the Web service and without restricting the client’s choices in terms of which operations to execute. We propose ACConv , a novel model for access control in Web services that is suitable when interactions between the client and the Web service are conversational and long-running. The conversation-based access control model proposed in this article allows service providers to limit how much knowledge clients have about the credentials specified in their access policies. This is achieved while reducing the number of times credentials are asked from clients and minimizing the risk that clients drop out of a conversation with the Web service before reaching a final state due to the lack of necessary credentials. Clients are requested to provide credentials, and hence are entrusted with part of the Web service access control policies, only for some specific granted conversations which are decided based on: (1) a level of trust that the Web service provider has vis-à-vis the client, (2) the operation that the client is about to invoke, and (3) meaningful conversations which represent conversations that lead to a final state from the current one. We have implemented the proposed approach in a software prototype and conducted extensive experiments to show its effectiveness.

The brain computer interface (BCI) technology allows a direct connection between brain and computer without any muscular activity required, and thus it offers a unique opportunity to enhance and/or to restore communication and actions into external word in people with severe motor disability.

Introduction to the Special Issue with Best Papers from BPM 2008 WorkshopsThis special issue contains the best papers selected from the workshops that were held in conjunction with the 6th International Conference on Business Process Management (BPM 2008) that took place in Milan, Italy.The nine workshops included some important and challenging areas in business process management:• Business Process Management and Social Software (BPMS2) focuses on the interaction of social software and the underlying paradigm of social production with business processes.It explores how social software and social production interact with business process management, how business process management has to change to comply with social production, and how business processes may profit from social techniques.• Model-Driven Engineering for Business Process Management (MDE4BPM) is about the application of model-driven engineering to business process management.It focuses on research problems that arise when the model-driven engineering and development methodology is applied to automate the whole life cycle of business process modelling artifacts (e.g., automatically mapping high-level business process models to executable IT-level workflows).• Process Management for Highly Dynamic and Pervasive Scenarios (PM4HDPS) recognizes how nowadays process management systems are also being used in pervasive and highly dynamic situations, such as emergency management, pervasive healthcare and ambient intelligence.It requires novel approaches merging traditional BPM with artificial intelligence, agent programming and robotics.• QoS of Self-healing Web Services (QSWS) attacks the emerging area of Web service composition, QoS management and diagnosis.It focuses on distributed and centralized solutions for the diagnosis and repair of choreographed and/or orchestrated Web services, QoS management in choreographed and/or orchestrated Web services, ontologies for QoS.• Collaborative Business Processes (CBP) studies how the business processes of different organizations can be integrated seamlessly, while the constructed business process network dynamically evolves to adapt to the continuously changing business conditions.Nowadays organizations have been undergoing a thorough transformation towards highly flexible and agile collaborations.

The increasing of bandwidth capacity and the rising of more active and more demanding Internet users motivate the research of the best solution in P2P multimedia streaming. This paper presents STREAMCOMPLETE, a new architecture and prototype system for mesh-based P2P live video streaming; it realizes a new concept of overlay network's management as well as merges the best practices of tree- and mesh-based approaches. STREAMCOMPLETE creates a dynamic overlay network that optimizes itself basing on local conditions of peers. The two core operations are: (i) FastTop, that reorganizes peers in order to improve the general performance of the network; (ii) LoopCheck, that detects bounded loops of the multimedia streaming in the mesh overlay network. We have extensively evaluated the performance and the behavior of STREAMCOMPLETE over the PlanetLab. Our experiments demonstrate the ability of peers in managing the overlay network with autonomic behavior in case of network changes. Furthermore the system is scalable w.r.t. the cardinality of the overlay network, by requiring very few control traffic.

This paper introduces StreamComplete, a new architecture and prototype system for mesh-based P2P live video streaming; it realizes a new concept of overlay network's management as well as merges the best practices of treeand mesh-based approaches. StreamComplete creates a dynamic overlay network that optimizes itself on the basis of local conditions of peers. We have extensively evaluated the performance and the behavior of StreamComplete over the PlanetLab. Our experiments demonstrate the ability of peers in managing the overlay network with autonomic behavior in case of network changes. Furthermore the system is scalable w.r.t. the cardinality of the overlay network, by requiring very few control traffic.

Human robot interaction factors for controlling remote mobile robots is a recent field of research. In this paper we propose a novel system that dynamically adjusts the interaction between operators and robots according to the mission requirements. In particular we focus on the task allocation service. This service allocates the control of robots deployed in a mission between the artificial agents (autonomy) and the operators (tele-operation), being capable of detecting problems derived from autonomy, diagnose the causes, and alert the operator. The system is designed for a multi-robot-multi-user paradigm.

Pervasive computing environments are nowadays more and more used as a supporting tool for cooperative workflows, e.g., in emergency management. A typical problem in these scenarios is the synthesis of workflows in presence of sets of services (hosted on mobile devices) with constrained behaviors, just before the collaborating team is dropped off in the operation field. In this paper, we propose a technique able to automatically synthesize distributed orchestrators, each one coordinating a service and synchronizing with the other orchestrators, given a target generic workflow to be carried out and a set of behaviorally-constrained services.

In this paper, we present a recently funded European research project, namely WORKPAD, that aims at designing and developing an innovative software infrastructure (software, models, services, etc.) for supporting collaborative work of human operators in emergency/disaster scenarios. In such scenarios, different teams, belonging to different organizations, need to collaborate with one other to reach a common goal; each team member is equipped with handheld devices (PDAs) and communication technologies, and should carry on specific tasks. In such a case we can consider the whole team as carrying on a process, and the different teams (of the different organizations) collaborate through the "interleaving" of all the different processes (macro-process). Each team is supported by some back-end centre, and the different centres need to cooperate at an inter-organizational level to reach an effective coordination among teams. The project investigates a 2-level framework for such scenarios: a back-end peer-to-peer community, providing advanced services requiring high computational power, data & knowledge & content integration, and a set of front-end peer-to-peer communities, that provide services to human workers, mainly by adaptively enacting processes on mobile ad-hoc networks

The promise of Web services is to enable the composition of new distributed applications/solutions: when no available service can satisfy a client request, (parts of) available services can be composed and orchestrated in order to satisfy such a request. Service composition involves two different issues: the synthesis, in order to synthesize, either manually or automatically, a specification of how coordinating the component services to fulfill the client request, and the orchestration, i.e., how executing the previous obtained specification by suitably supervising and monitoring both the control flow and the data flow among the involved services. In this work, we address the automatic composition synthesis when the behavior of the available services is non-deterministic, and hence is not fully controllable by the orchestrator. The service behavior is modeled by the possible conversations the service can have with its clients. The presence of nondeterministic conversations stems naturally when modeling services in which the result of each interaction with its client on the state of the service can not be foreseen

Service Oriented Computing is emerging as the main approach to build distributed enterprise applications on the Web. The widespread use of Web services is hindered by the lack of adequate security and privacy support. In this paper, we present a novel framework for enforcing access control in conversation-based Web services. Our approach takes into account the conversational nature of Web services. This is in contrast with existing approaches to access control enforcement that assume a Web service as a set of independent operations. Furthermore, our approach achieves a tradeoff between the need to protect Web service's access control policies and the need to disclose to clients the portion of access control policies related to the conversations they are interested in. This is important to avoid situations where the client cannot progress in the conversation due to the lack of required security requirements. We introduce the concept of k-trustworthiness that defines the conversations for which a client can provide credentials maximizing the likelihood that it will eventually hit a final state.

Abstract Web service composition is emerging as an interesting approach to integrate business applications and create intra‐organizational business processes. Single Web services are combined to create a complex Web service that will realize the process business logic. Once the process is created, it is executed by an orchestration engine that invokes individual Web services in the correct order. However, Web services composing the workflow sometimes become unavailable during the run‐time phase, blocking process execution. This paper describes an architecture that allows the flexible orchestration of business processes. With this approach, Web services composing the process can be automatically substituted with other compatible Web services during process execution. A methodology is defined to evaluate Web service compatibility based on interface matching, in order to select substitutable Web services. Copyright © 2005 John Wiley & Sons, Ltd.

This paper addresses the issue of automatic service composition. We first develop a framework in which the exported behavior of a service is described in terms of a so-called execution tree, that is an abstraction for its possible executions. We then study the case in which such exported behavior (i.e. the execution tree of the service) can be represented by a finite state machine (i.e. finite state transition system). In this specific setting, we devise sound, complete and terminating techniques both to check for the existence of a composition, and to return a composition, if one exists. We also analyze the computational complexity of the proposed algorithms. Finally, we present an open source prototype tool, called [Formula: see text] (E-Service Composer), that implements our composition technique. To the best of our knowledge, our work is the first attempt to provide a provably correct technique for the automatic synthesis of service composition, in a framework where the behavior of services is explicitly specified.

In this paper we study automatic composition synthesis of e-Services, based on automated reasoning. We represent the behavior of an e-Service in terms of a deterministic transition syst (or a finite state machine), in which for each action the role of the e-Service, either as initiator or as servant, is highlighted. In this setting we present an algorithm based on satisfiability in a variant of Propositional Dynamic Logic that solves the automatic composition probl. Specifically, given (i) a possibly incomplete specification of the sequences of actions that a client would like to realize, and (ii) a set of available e-Services, our technique synthesizes a composite e-Service that (i) uses only the available e-Services and (ii) interacts with the client "in accordance" to the given specification. We also study the computational complexity of the proposed algorithm.

Cooperative information systems (CISs) are often characterized by a high degree of data replication; as an example, in an e-government scenario, the personal data of citizens are stored by almost all administrations. In such scenarios, organizations typically provide the same information with distinct quality levels and this enables providing users with data of the highest available quality. Furthermore, the comparison of data values might be used to enforce a general improvement of data quality in all organizations. In the DaQuinCIS project (Aguilera et al., 1999), we propose an architecture for the management of data quality in CISs; this architecture allows the diffusion of data and related quality and exploits data replication to improve the overall quality of cooperative data. In this paper, we present an overview of a component of the DaQuinCIS architecture, namely the quality notification service (QNS), which is used to inform interested users when changes in quality values occur within the CIS. QNS can be used to control the quality of critical data, e.g. to keep track of quality changes and to be always aware when quality degrades under a certain threshold. The interaction between the QNS and its users follows the publish/subscribe paradigm: a user willing to be notified for quality changes subscribes to the QNS by submitting the features of the events to be notified for, through a specific subscription language. When a change in quality occurs, an event is published by the QNS i.e., all the users which have a consistent subscription receive a notification. However, as shown in the paper by Marchetti et al. (2003), currently available pub/sub infrastructures do not allow to meet all the requirements that a QNS implementation should satisfy, in particular scaling to a large number of users and coping with platform heterogeneity. QNS addresses both these problems through a layered architecture that: (i) encapsulates the technological infrastructure specific of each organization; (ii) adopts the standard Web-service technology to implement inter-organization communications; and (iii) embeds solutions and algorithms (namely, merge subscriptions and diffusion trees) to reduce the use of physical and computational resources. The remainder of this paper is organized as follows: we first introduce some preliminary concepts and the QNS specification; then we motivate and describe the internal architecture of the service. Due to the lack of space, explanations are given at a very high abstraction level. Interested readers can find technical and formal details, as well as running examples, in the paper by Scannapieco et al. (2003).

Integrating autonomous enterprise systems allows the cooperation among applications belonging to distinct systems. As an example, this problem shows up when integrating software services of large departments and organizations in e-government initiatives. This paper studies, in the context of the Unitary Network of the Italian Public Administration, the problem of increasing the availability of services exported by an autonomous enterprise system towards others. In particular we show how a fault tolerant CORBA system, namely the Interoperable Replication Logic (IRL), can be used to increase such an availability by building a replicated cooperative gateway that wraps enterprise applications.

In many enterprises the presence of heterogenous legacy information systems is pervasive, nevertheless the different component organizations need to cooperate in order to offer new services. The Distributed Object Computing is a valid approach for the definition of a cooperative model among heterogeneous legacy information systems, but few methodologies and architectures explicitly address these issues. This paper describes a case study (or better, a set of case studies) in which these issues were addressed: it is the Cooperative Architecture over the Unitary Network of the Italian Public Administration. Then it proposes some methodological suggestions, stemming from the case studies, for the development of cooperative systems based on distributed object middleware.