Children increasingly encounter intelligent systems in educational and everyday contexts, yet most trust measures in human–AI interaction have been developed for adult users. This Work-in-Progress extends the validation of the child-centred K-AI Trust questionnaire to an older developmental cohort (10–14 years). Building on prior validation with 9–11 year-olds, we examine whether the instrument demonstrates acceptable internal consistency and coherent response patterns in early adolescence. Using survey data from 314 participants, we report preliminary reliability analyses and explore associations between dispositional trust (Propensity to Trust Technology; PTT) and post-interaction trust evaluations. Item refinement resulted in acceptable internal consistency for both scales (7-item K-AI; 5-item PTT). Correlational analyses revealed no significant association between dispositional and situational trust. This suggests that, in early adolescence, children’s inherent propensity to trust technology may not directly shape their immediate trust evaluation of a specific intelligent system. These findings provide preliminary evidence for the developmental applicability of a child-centred trust instrument and support ongoing age-sensitive psychometric refinement.

TRI-IVIS focuses on Trustable, Reproducible and Intelligent Information Visualization Systems, aiming to advance methods and systems for managing and analyzing large-scale complex data, integrating Artificial Intelligence, Machine Learning, Big Data analytics and advanced visual interfaces. While there are many domains that may benefit from such characteristics, preferred domains for the workshop are, but not limited to, genomic applications and Meetings, Incentives, Conferences and Events (MICE). Many topics revolve around such domains, ranging from heterogeneous large-scale data, distributed storage, multi-stakeholder scenarios, and regulatory requirements.

As generative AI systems increasingly act autonomously in digital environments, the ways in which these systems interact with children raise questions of both agency and permission. Existing frameworks offer limited guidance on how to design interfaces that respect children’s role as the final decision-makers [30]. This paper adopts a design-oriented perspective on child-AI interaction, drawing on mixed-methods data from 289 children aged 9-11. We reinterpret children’s trust-related responses as empirical signals for the ways in which agency and authority should be distributed within AI interfaces. Our findings indicate that children resist ‘proactive’ AI, instead preferring systems that seek permission, support non-judgmental error correction, and respect their right to refuse suggestions. Building on these insights, we propose three design principles: Staged Execution, Non-Destructive Reversibility, and Perceptible Uncertainty Signals, which translate children’s rights into concrete interface mechanisms, ensuring that generative systems support rather than override children’s agency.

Most trust metrics for intelligent systems are developed for adults, relying on complex reasoning and language that do not align with children’s developmental stages. As intelligent systems increasingly engage with young users, evaluating trust in child-AI interaction has become an urgent concern in HCI. In this paper, we present the iterative refinement and validation of the K-AI Trust Questionnaire, a child-centred instrument that integrates dispositional and situational trust components grounded in child-rights principles. Dispositional trust is captured through a child-adapted Propensity to Trust Technology (PTT), while situational trust is assessed through post-interaction items reflecting children’s experience with AI. Starting with a sample of 289 children, we conducted psychometric analyses and exploratory testing, culminating in a confirmatory factor analysis on a subsample of 85 children. Results supported a unidimensional structure consistent with the PTT, and highlighted the limitations of adult-oriented scales, underscoring the need for developmentally appropriate tools for trustworthy child-AI design.

Visualizing collaboration networks supports studies in both natural and social sciences as they reveal collaboration patterns among individuals and institutions. However, the complexity of data, involving heterogeneous timely and interconnected entities, often lead to visual clutter, making it challenging to create effective visualizations. This paper explores the use of an incremental approach to facilitate the exploration of co-authorship networks composed of multivariate entities distributed over time. We demonstrate how incremental visualization can assist users in focusing on relevant data while addressing scalability issues through a focus+context technique. We use a tool called Muvin, which implements this incremental approach to explore multi-sourced linked open data (LOD). Although Muvin can be applied to various types of collaboration networks, this study focuses specifically on co-authorship networks. A user study involving 19 participants offers insights into how the incremental approach supports domain-specific tasks using co-authorship networks.

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.

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.

Integrated Development Environments increasingly implement AI-powered code completion tools (CCTs), which promise to enhance developer efficiency, accuracy, and productivity. However, interaction challenges with CCTs persist, mainly due to mismatches between developers’ mental models and the unpredictable behavior of AI-generated suggestions, which is an aspect underexplored in the literature. We conducted an elicitation study with 56 developers using co-design workshops to elicit their mental models when interacting with CCTs. Different important findings that might drive the interaction design with CCTs emerged. For example, developers expressed diverse preferences on when and how code suggestions should be triggered (proactive, manual, hybrid), where and how they are displayed (inline, sidebar, popup, chatbot), as well as the level of detail. It also emerged that developers need to be supported by customization of activation timing, display modality, suggestion granularity, and explanation content, to better fit the CCT to their preferences. To demonstrate the feasibility of these and the other guidelines that emerged during the study, we developed ATHENA, a proof-of-concept CCT that dynamically adapts to developers’ coding preferences and environments, ensuring seamless integration into diverse workflows. • Users want flexible triggers: balance control with smart automation • Inline works for short code; sidebar/chatbot for longer suggestions • Start minimal, let users expand from single lines to full files • Keep explanations short, contextual, opened by click or shortcut • Let users tune timing, style, detail level, and coding format

Assessing human vulnerability in cybersecurity is critical to understanding the relationship between human factors and the security of digital systems. This issue is exacerbated in areas such as public administration due to the sensitive nature of the data and services handled by government agencies, including citizen records, financial data, and national security details. Robust cybersecurity measures and understanding the human factors contributing to incidents are essential to securing public administration systems and maintaining public trust in government institutions. This poster presents ongoing work to develop a new psychometric tool to assess the human factors that play a critical role in cybersecurity incidents.

The field of Sports Human-Computer Interaction (SportsHCI) investigates interaction design to support a physically active human being. Despite growing interest and dissemination of SportsHCI literature over the past years, many publications still focus on solving specific problems in a given sport. We believe in the benefit of generating fundamental knowledge for SportsHCI more broadly to advance the field as a whole. To achieve this, we aim to identify the grand challenges in SportsHCI, which can help researchers and practitioners in developing a future research agenda. Hence, this paper presents a set of grand challenges identified in a five-day workshop with 22 experts who have previously researched, designed, and deployed SportsHCI systems. Addressing these challenges will drive transformative advancements in SportsHCI, fostering better athlete performance, athlete-coach relationships, spectator engagement, but also immersive experiences for recreational sports or exercise motivation, and ultimately, improve human well-being.

Abstract This study developed a Human-Centered Technology Acceptance Model (HC-TAM) for recruitment chatbots, integrating aspects of the traditional Technology Acceptance Model (TAM)(Davis in 1989) with a focus on human-centered factors such as transparency, personalization, efficiency, and ethical concerns, alongside the fundamental TAM constructs of perceived ease of use and perceived usefulness. The study shows that the intention to use technology is influenced by their perceptions of its usefulness and ease of use. By extending TAM to include human-centered considerations, this research aimed to capture the diverse factors that significantly influence users’ acceptance of chatbots in the recruitment process. A three-phase study has been carried out, each serving a distinct purpose. (a) Phase 1 focuses on defining primary themes through qualitative interviews with 10 participants, laying the foundation for subsequent research. (b)Building upon this foundation, Phase 2 engages 28 participants in a refined exploration of these themes, ending in a comprehensive landscape of user perspectives. (c) Finally, Phase 3 employs rigorous Structural Equation Modeling for theoretical framework examination, yielding critical constructs and hypotheses. Moreover, Phase 3 encompasses the thorough development of measurement instruments and extensive data collection, involving 146 participants through questionnaires, the study found that the acceptance of recruitment chatbots is significantly enhanced when these systems are designed to be transparent, provide personalized interactions, efficiently fulfill user needs, and address ethical concerns. These findings contribute to the broader understanding of technology acceptance in the context of recruitment, offering valuable insights for developers and designers to create chatbots that are not only technically advanced but also ethically sound, user-friendly, and effectively aligned with human needs and expectations in recruitment settings.

The automatic monitoring and assessment of the engagement level of learners in distance education may help in understanding problems and providing personalized support during the learning process. This article presents a research aiming to investigate how student engagement level can be assessed from facial behavior and proposes a model based on Long Short-Term Memory (LSTM) networks to predict the level of engagement from facial action units, gaze, and head poses. The dataset used to learn the model is the one of the EmotiW 2019 challenge datasets. In order to test its performance in learning contexts, an experiment, involving students attending an online lecture, was performed. The aim of the study was to compare the self-evaluation of the engagement perceived by the students with the one assessed by the model. During the experiment we collected videos of students behavior and, at the end of each session, we asked students to answer a questionnaire for assessing their perceived engagement. Then, the collected videos were analyzed automatically with a software that implements the model and provides an interface for the visual analysis of the model outcome. Results show that, globally, engagement prediction from students' facial behavior was weakly correlated to their subjective answers. However, when considering only the emotional dimension of engagement, this correlation is stronger and the analysis of facial action units and head pose (facial movements) are positively correlated with it, while there is an inverse correlation with the gaze, meaning that the more the student's feels engaged the less are the gaze movements.

Remote usability testing is performed by evaluators who are in different physical locations from the participants (synchronous remote testing) and possibly operating at different times (asynchronous remote testing). The tools developed in recent years to support remote tests exploit web technology based on HTML5 and JavaScript ES6 and thus enable previously unexplored scenarios. However, studies providing evidence on the benefits or drawbacks of utilizing recent web-based tools have not yet been reported in the literature. This article sheds some light on the impact of such tools on asynchronous remote usability testing of websites by reporting an experimental study with 100 participants and 15 evaluators to compare real-time laboratory tests with asynchronous remote tests. The study investigates: 1) how the metrics results of asynchronous remote usability tests performed through a web-based tool differ from those of usability tests conducted in real-time laboratory settings; and 2) how the experience of participants differs in the two types of tests. The lessons learned in the study are instrumental in informing the design of future tools. Some results of particular interest indicate that the web technology used by the tool for asynchronous remote testing affects task execution times and participants’ satisfaction. Another indication is that slow internet connections must be managed in asynchronous remote testing; slow connections introduce delays when transferring large amounts of collected data, which, together with the lack of human support, make participants of asynchronous remote tests more prone to feel negative emotions.

We present a set of applications of dynamic hypergraph visualization. Dynamic hypergraphs can be used to represent connections between two or more entities that occur in time intervals. Visualizing dynamic hypergraphs can help analyzing the evolving connections in groups of entities. We report different domains where the data can be modeled as a hypergraph and some patterns that can be identified in the specific domains.

Abstract Hypergraphs are a generalization of graphs in which edges (hyperedges) can connect more than two vertices—as opposed to ordinary graphs where edges involve only two vertices. Hypergraphs are a fairly common data structure but there is little consensus on how to visualize them. To optimize a hypergraph drawing for readability, we need a layout algorithm. Common graph layout algorithms only consider ordinary graphs and do not take hyperedges into account. We focus on layered hypergraphs, a particular class of hypergraphs that, like layered graphs, assigns every vertex to a layer, and the vertices in a layer are drawn aligned on a linear axis with the axes arranged in parallel. In this paper, we propose a general method to apply layered graph layout algorithms to layered hypergraphs. We introduce six different transformations for layered hypergraphs. The choice of transformation affects the subsequent graph layout algorithm in terms of computational performance and readability of the results. Thus, we perform a comparative evaluation of these transformations in terms of number of crossings, edge length, and impact on performance. We also provide two case studies showing how our transformations can be applied to real‐life use cases. A copy of this paper with all appendices and supplemental material is available at osf.io/grvwu.

We propose a new approach-called PK-clustering-to help social scientists create meaningful clusters in social networks. Many clustering algorithms exist but most social scientists find them difficult to understand, and tools do not provide any guidance to choose algorithms, or to evaluate results taking into account the prior knowledge of the scientists. Our work introduces a new clustering approach and a visual analytics user interface that address this issue. It is based on a process that 1) captures the prior knowledge of the scientists as a set of incomplete clusters, 2) runs multiple clustering algorithms (similarly to clustering ensemble methods), 3) visualizes the results of all the algorithms ranked and summarized by how well each algorithm matches the prior knowledge, 4) evaluates the consensus between user-selected algorithms and 5) allows users to review details and iteratively update the acquired knowledge. We describe our approach using an initial functional prototype, then provide two examples of use and early feedback from social scientists. We believe our clustering approach offers a novel constructive method to iteratively build knowledge while avoiding being overly influenced by the results of often randomly selected black-box clustering algorithms.

Parallel Aggregated Ordered Hypergraph(PAOH) is a novel technique to visualize dynamic hypergraphs. Hypergraphs are a generalization of graphs where edges can connect several vertices. Hypergraphs can be used to model networks of business partners or co-authorship networks with multiple authors per article. A dynamic hypergraph evolves over discrete time slots. PAOH represents vertices as parallel horizontal bars and hyperedges as vertical lines, using dots to depict the connections to one or more vertices. We describe a prototype implementation of Parallel Aggregated Ordered Hypergraph, report on a usability study with 9 participants analyzing publication data, and summarize the improvements made. Two case studies and several examples are provided. We believe that PAOH is the first technique to provide a highly readable representation of dynamic hypergraphs. It is easy to learn and well suited for medium size dynamic hypergraphs (50-500 vertices) such as those commonly generated by digital humanities projects-our driving application domain.

This paper makes a point of current perspectives on Data Visualization research that were essentially conceived to provide guidelines for finding the best mapping between data and visual representations. Going back to foundational concepts of HCI that rely on manipulation of visual symbols, we propose a new perspective, with the aim to focus on a different configuration, that considers visual signs, professional contexts and user practices. We argue that, so far, user practices have been neglected or left behind in design, evaluation and recommendation scenarios, reducing them to the pure relational focus among kind of data, kind of charts and in lab tasks. This may underestimate the potential of the pragmatic side of this relation, where humans manipulate and interpret signs on the basis of their "practical knowledge, a factor that should be considered to improve human interactions with Data Visualization tools. The perspective discussed here would bring into light and help frame open problems such as interactions in routine tasks and the interpretation of data through visual interactive tools in daily professional practices. By proposing a light but formal model of investigation of these pragmatic interactions, we would like to contribute to the current debate around data visualization as the new strategic tool for dealing with the growing complexity of big data streams, digitization of life, sensor and hardware-embedded intelligence.

Abstract Automation in computing systems has always been considered a valuable solution to unburden the user. Internet of Things (IoT) technology best suits automation in different domains, such as home automation, retail, industry, and transportation, to name but a few. While these domains are strongly characterized by implicit user interaction, more recently, automation has been adopted also for the provision of interactive and immersive experiences that actively involve the users. IoT technology thus becomes the key for Smart Interactive Experiences (SIEs), i.e., immersive automated experiences created by orchestrating different devices to enable smart environments to fluidly react to the final users’ behavior. There are domains, e.g., cultural heritage, where these systems and the SIEs can support and provide several benefits. However, experts of such domains, while intrigued by the opportunity to induce SIEs, are facing tough challenges in their everyday work activities when they are required to automate and orchestrate IoT devices without the necessary coding skills. This paper presents a design approach that tries to overcome these difficulties thanks to the adoption of ontologies for defining Event-Condition-Action rules. More specifically, the approach enables domain experts to identify and specify properties of IoT devices through a user-defined semantics that, being closer to the domain experts’ background, facilitates them in automating the IoT devices behavior. We also present a study comparing three different interaction paradigms conceived to support the specification of user-defined semantics through a “transparent” use of ontologies. Based on the results of this study, we work out some lessons learned on how the proposed paradigms help domain experts express their semantics, which in turn facilitates the creation of interactive applications enabling SIEs.

Due to the diffusion of mobile devices, more and more people access e-learning platforms from mobile phones. Students learn from digital books and have access to information anytime and anywhere. However, with billions of mobile users worldwide, as well as billions of under-protected Internet of Things (IoT) devices, the risk of being the target of malware, cybercrime and sophisticated attacks is high. This paper proposes and discusses a set of visualization techniques applied to a dataset generated by DREBIN, a malware detection tool that performs a static analysis on apps installed to Android devices. On the base of dataset, we applied text, tree and graph visualization techniques to identify malware patterns. The visual findings can help the cybersecurity analyst in detecting malicious app behavior.

Due to its geographical position, the Apulia region, is used to house migrants from all over the world who arrived over the centuries. Apulia is also a transit land for migrants that want to reach other Italian regions or European countries. One of the main issues of migration flows is related to the health services network. In this context, the Apulia Region, in collaboration with other private and public organisations, proposed the Prevenzione 4.0 (Prevention) project that aims at creating an e-health environment to empower the services of the National Health Service for migrants. Technological solutions and learning paths will be implemented to reduce the number of users who daily ask for health care services. The actions will be available for both migrants and professional figures involved in the management of migrants' reception processes. This paper presents a mobile application designed to help the migrant centres to provide medical and psychological support to their guests. The app fosters the migrants' empowerment to make them able to take care of their health without involving the National Health Service when not strictly necessary.

The deep learning approach has increased the quality of automatic medical diagnoses at the cost of building qualified datasets to train and test such supervised machine learning methods. Image annotation is one of the main activity of dermatologists and the quality of annotation depends on the physician experience and on the number of studied cases: manual annotations are very useful to extract features like contours, intersections and shapes that can be used in the processes of lesion segmentation and classification made by automatic agents. This paper proposes the design of an interactive multimedia platform that enhance the annotation process of medical images, in the domain of dermatology, adopting gamification and "games with a purpose" (GWAP) strategies in order to improve the engagement and the production of qualified datasets also fostering their sharing and practical evaluation. A special attention is given to the design choices, theories and assumptions as well as the implementation and technological details.

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.

Polar coordinates have been widely used in various techniques of interactive data visualization. The spatial organization through circular and radial layouts is implemented in a wide range of statistical charts and plots and is applicable for space-filling techniques and for node-link-group diagrams. Different arrangements of dots, lines and areas in polar coordinates create grids for data distribution, aggregation and linking.

The analysis of air quality data may reveal the quality of life and can prevent dangers for the citizen health. This paper presents an approach for air quality data analysis, which exploits Data Mining and InfoVis techniques to support the analysts daily work. The proposed approach addresses data generated by the electronic nose, a device that detects chemical compounds perceived by humans through the smell. A working pipeline implements a workflow for data processing with clustering techniques; an enhanced powerful calendar visualization combined with more traditional line graph and geo-referenced visualizations shows data to the analyst allowing to detect temporal trends and making immediate comparisons.

The Italian National Police started a research on vehicular traffic to improve road safety and reduce the number of theft victims. In order to support the discovery of anomalous behavior, this paper proposes a method for data analysis to automatically detect relevant hypotheses, a data mining technique to extract relevant information and a visualization technique. Traffic flow analysis is a challenging and complex task, due to the huge size of the data involved, thus falling in the realm of Big Data. Visual Analytics tools reduce and improve the search by representing a large amount of data in a small space through smart visualizations.

Recent technologies are offering today many possibilities to end users, which ask for continuous support in a variety of situations. Internet of things (IoTs) and the proliferation of smart devices are offering many opportunities that raise the need to standardise protocols for their interoperability and interaction languages for their management. This study proposes EUDroid, a system composed of a mobile application and an IoT device used as a pill reminder to allow the patients to correctly take their prescribed drugs. A web server stores and manages the therapies that can be defined by the end users. The web server also manages the communication between the app and the device. In order to validate the management of the therapies, a formal language has been proposed. It describes the behaviour of different components of the IoT device, such as LEDs or buzzers, and defines when, with which delay, and for how long time a given event will last, to manage technical concepts related to smart devices for supporting them in following therapies more accurately.

People awareness in various contexts has been widely considered in the literature. A form of awareness is the quantification of self, which requires a number of conditions to be implemented. The most important are: producing, computing and making sense of data. Sensors produce data at very high rates. A lot of research, in the field of data bases, has focused on how to store and compute data efficiently. Data presentation is still challenging, because the possibilities of producing interactive visualizations on the Web and on different devices are increasing. The contribution of this demo paper is to propose a visualization technique and a web-based tool enabling the visualization of personal data produced during the 24 hours of the day. The aim of this tool is to help people to understand their own behavior. Such data can also be compared with other people's data to improve the analysis. This demo focuses on two main contexts: visualizing working data of a group of people living in different time zones in order to improve the awareness of the behavior of the group; visualizing energy consumption data in order to provide an idea of the behavior of people in the domestic context. The data for the first example are gathered from the activity people perform with their computer (e.g. email, chat, keyboard strokes) while the data of the second context are gathered from a low-cost Arduino device capable of providing instant electricity consumption information.

The evolution of communication technologies provides support to the collaboration of people that work in distributed teams. Group awareness is an important requirement for activity coordination, since understanding the activities of the others provides the context for the individual own activities and gives indications on how individual contributions are relevant to the team. This poster proposes a novel information visualization technique that aims at supporting awareness in distributed teams. Collaborative traces of team members are visualized in order to show which one is the most available and responsive.

The increasing adoption of video surveillance makes it possible to watch over sensitive areas and identify people responsible for damage, theft and violence. However, when such events are not detected immediately, the subsequent video analysis can be a long and tedious task. The aim of this paper is to present a technique that allows a human investigator to focus only on those parts of a video showing the event as it unfolds, and so helping to save on the time needed to identify and understand how it happened. The presented technique creates a single interactive image of the whole video that shows everything that happened m the scene. The human investigator can then select an area of interest and those parts of the video related to that specific area will start to play.

Time series analysis is a process whose goal is to understand phenomena. The analysis often involves the search for a specific pattern. Finding patterns is one of the fundamental steps for time series observation or forecasting. The way in which users are able to specify a pattern to use for querying the time series database is still a challenge. We hereby propose an enhancement of the SearchBox, a widget used in TimeSearcher, a well known tool developed at the University of Maryland that allows users to find patterns similar to the one of interest.

This paper reports the experimental studies we have performed to evaluate Explore!, an m-learning system that supports middle school students during a visit to an archaeological park. It exploits a learning technique called excursion-game, whose aim is to help students to acquire historical notions while playing and to make archaeological visits more effective and exciting. In order to understand the potentials and limitations of Explore!, our studies compare the experience of playing the excursion-game with and without technological support. The design and evaluation of Explore! have provided knowledge on the advantages and pitfalls of m-learning that may be instrumental in informing the current debate on e-learning.

Time-series forecasting has a large number of applications. Users with a partial time series for auctions, new stock offerings, or industrial processes desire estimates of the future behavior. We present a data driven forecasting method and interface called similarity-based forecasting (SBF). A pattern matching search in an historical time series dataset produces a subset of curves similar to the partial time series. The forecast is displayed graphically as a river plot showing statistical information about the SBF subset. A forecasting preview interface allows users to interactively explore alternative pattern matching parameters and see multiple forecasts simultaneously. User testing with 8 users demonstrated advantages and led to improvements.

This paper presents a system designed to support young students learning history at an archaeological site, by exploiting mobile technology. The approach uses game-play, since it stimulates in young students an understanding of history that would otherwise be difficult to engender, helping players to acquire historical notions and making archaeological visits more effective and exciting. A strength of the system is that, by running on the visitors own cellular phones, it requires minimal investments and small changes to the existing site exhibition.

SUIT (Systematic Usability Inspection Tool) is an Internet-based tool that supports the evaluators during the usability inspection of software applications. SUIT makes it possible to reach inspectors everywhere, guiding them in their activities. Differently from other tools that have been proposed in literature, SUIT not only supports the activities of a single evaluator, but permits to manage a team of evaluators who can perform peer reviews of their inspection works and merge their individual reports in a single document on which they agree.

PHiP (Patient History in Pocket) is a tool designed for a mobile device that displays patient histories and permits to visually query patient data stored in the hospital database. It exploits Information Visualization techniques and it is able to accommodate on the screen a good amount of information that physicians require in their analysis of clinical cases. Two different user interfaces for PHiP have been implemented and informal user testing has been performed to compare their impact on users.

DAE (data analysis engine) is a framework that provides various tools for data analysis, which can assist the users in their decision making process. The tools exploit visualizations techniques; various visualizations may be generated for a set of data in order to allow users to browse among data and get the information of interest as well as discover new and possibly unexpected insights. DAE is quite general and can manage data in different domains. It supports several activities involved in the data analysis process, starting from data selection, performing various data transformations and presenting the analysis results. DAE aims at giving decision makers the possibility to directly analyze data with different tools

The abundance of data available nowadays fosters the need of developing tools and methodologies to help users in extracting significant information. Visual data mining is going in this direction, exploiting data mining algorithms and methodologies together with information visualization techniques.The demand for visual and interactive analysis tools is particularly pressing in the Association Rules context where often the user has to analyze hundreds of rules in order to grasp valuable knowledge. This paper presents a visual strategy to face this drawback by exploiting graph-based technique and parallel coordinates to visualize the results of association rules mining algorithms. The combination of the two approaches allows both to get an overview on the association structure hidden in the data and to deeper investigate inside a specific set of rules selected by the user.

The enormous amount of data available on the web must be adequately exploited by company managers in order to improve their business. An important role is played by various techniques that are capable of extracting useful information. Our approach exploits visualization techniques. In this paper we present 2D and 3D visualizations that are used in a web-based system that supports the organization and management of trade fairs. We show how the main users of the system, namely fair organisers and companies that participate in the fair either as exhibitors or visitors, take advantage of the available visual tools in their business activities.