Recommender systems are central to contemporary music listening, yet their problematic behaviors remain underexplored from the perspective of everyday listeners. While prior research has addressed issues such as bias and diversity, less is known about how users themselves perceive and interpret these dynamics in relation to music discoverability. This paper reports on think-aloud interviews with 20 Italian digital-native listeners, who completed discovery-oriented tasks while reflecting on algorithmic recommendations. Thematic analysis revealed three recurring concerns: reinforcement of societal biases, commercial imperatives driving exposure, and confinement within narrow niches. These findings show how listeners actively develop folk theories of recommender behavior, highlighting a tension between algorithmic efficiency and cultural effects. We contribute empirical insights into user sensemaking of algorithmic harms, consolidate the use of the Think-Aloud Protocol as a user-driven auditing method, and outline design implications for more participatory and equitable music recommender systems.

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

Visual Analytics (VA) has become a paramount discipline in supporting data analysis in many scientific domains, empowering the human user with automatic capabilities while keeping the lead in the analysis. At the same time, designing an effective VA solution is not a simple task, requiring its adaptation to the problem at hand and the intended user of the system. In this scenario, the User-Centered Design (UCD) methodology provides the framework to incorporate user needs into the design of a VA solution. On the other hand, its implementation mainly relies on qualitative feedback, with the designer missing tools supporting her in quantitatively reporting the user feedback and using it to hypothesize and test the successive changes to the VA solution. To overcome this limitation, we propose TraVIS, a Visual Analytics solution allowing the loading of a web-based VA system, collecting user traces, and analyzing them with respect to the system at hand. In this process, the designer can leverage the collected traces and relate them to the tasks the VA solution supports and how those can be achieved. Using TraVIS, the designer can identify ineffective interaction paths, analyze the user traces support to task completion, hypothesize corrections to the design, and evaluate the effect of changes. We evaluated TraVIS through experimentation with 11 VA systems from literature, a use case, and user evaluation with five experts. Results show the benefits that TraVIS provides in terms of identifying design problems and efficient support for UCD.

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.

In this paper, we present a new benchmark to validate the suitability of database systems for interactive visualization workloads. While there exist proposals for evaluating database systems on interactive data exploration workloads, none rely on real user traces for database benchmarking. To this end, our long term goal is to collect user traces that represent workloads with different exploration characteristics. In this paper, we present an initial benchmark that focuses on "crossfilter"-style applications, which are a popular interaction type for data exploration and a particularly demanding scenario for testing database system performance. We make our benchmark materials, including input datasets, interaction sequences, corresponding SQL queries, and analysis code, freely available as a community resource, to foster further research in this area: https://osf.io/9xerb/?view_only=81de1a3f99d04529b6b173a3bd5b4d23.

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

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.

Vulnerabilities represent one of the main weaknesses of IT systems and the availability of consolidated official data, like CVE (Common Vulnerabilities and Exposures), allows for using them to compute the paths an attacker is likely to follow. However, even if patches are available, business constraints or lack of resources create obstacles to their straightforward application. As a consequence, the security manager of a network needs to deal with a large number of vulnerabilities, making decisions on how to cope with them. This paper presents VULNUS (VULNerabilities visUal aSsessment), a visual analytics solution for dynamically inspecting the vulnerabilities spread on networks, allowing for a quick understanding of the network status and visually classifying nodes according to their vulnerabilities. Moreover, VULNUS computes the approximated optimal sequence of patches able to eliminate all the attack paths and allows for exploring sub-optimal patching strategies, simulating the effect of removing one or more vulnerabilities. VULNUS has been evaluated by domain experts using a lab-test experiment, investigating the effectiveness and efficiency of the proposed solution.

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

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

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

Policy making has the strict requirement to rely on quantitative and high quality information. This paper will address the data quality issue for policy making by showing how to deal with Big Data quality in the different steps of a processing pipeline, with a focus on the integration of Big Data sources with traditional sources. In this respect, a relevant role is played by metadata and in particular by ontologies. Integration systems relying on ontologies enable indeed a formal quality evaluation of inaccuracy, inconsistency and incompleteness of integrated data. The paper will finally describe data confidentiality as a Big Data quality dimension, showing the main issues to be faced for its assurance.

Handling the complexity of relevant data requires new techniques about data access, visualization, perception, and interaction for innovative and successful strategies. In order to address human-computer interaction, cognitive eficiency, and interoperability problems, a generic information visualization, user empowerment, as well as service integration and mediation approach based on the existing state-of-the-art in the relevant areas of computer science has to be achieved.

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.

In this work we present some findings on how gesture-based interfaces can stimulate and motivate children into learning the basics of music notation. We report on a serious game we developed that relies on a hand detection interface using motion-sensing camera technology. Our initial experiments indicate the accuracy, effectiveness, and user acceptance of the gesture-based interface among children, thereby validating the idea that such interfaces can be used to facilitate the teaching of music concepts.

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.

Evaluating interface usability and system functionality is time-consuming and effort-intense. The short time-span involved in development iterations, such as those in agile development, makes it challenging for software teams to perform ongoing interface usability evaluation and system functionality testing. We propose a way to perform product ongoing evaluation, thus enabling software teams to perform interface usability evaluation alongside automated system functionality testing. We use formal task models, created in our defined TaMoGolog task modeling language, to conduct product evaluation experiments through TaMUlator. TaMUlator is a tool we developed for use at the Integrated Development Environment (IDE) level. Our case study revealed that software teams can easily engage in system evaluation by using TaMUlator on an iterative basis.

Object Matching (OM) is the problem of identifying pairs of data-objects coming from different sources and representing the same real world object. Several methods have been proposed to solve OM problems, but none of them seems to be at the same time fully automated and very effective. In this paper we present a fundamentally new suite of methods that instead possesses both these abilities. We adopt a statistical approach based on mixture models, which structures an OM process into two consecutive tasks. First, mixture parameters are estimated by fitting the model to observed distance measures between pairs. Then, a probabilistic clustering of the pairs into Matches and Unmatches is obtained by exploiting the fitted model. In particular, we use a mixture model with component densities belonging to the Beta parametric family and we fit it by means of an original perturbation-like technique. Moreover, we solve the clustering problem according to both Maximum Likelihood and Minimum Cost objectives. To accomplish this task, optimal decision rules fulfilling one-to-one matching constraints are searched by a purposefully designed evolutionary algorithm. Notably, our suite of methods is distance-independent in the sense that it does not rely on any restrictive assumption on the function to be used when comparing data-objects. Even more interestingly, our approach is not confined to record linkage applications but can be applied to match also other kinds of dataobjects. We present several experiments on real data that validate the proposed methods and show their excellent effectiveness.

One of the challenges in software development is to collect and analyze the end users' feedback in an effective and efficient manner. In this paper we present a tool to manage user evaluation alongside the process of software development. The tool is based on the idea that user evaluation should be managed iteratively from within the integrated development environment (IDE) in order to provide high quality user interface. The main capabilities include creating the experiment object as part of the software project; deriving development tasks from the analysis of evaluation data; and tracing these tasks to and from the code. Further, we provide a library to enable development of Java aspects for creation of automatic measures to increase the body of the evaluation data. Using this tool, development teams can manage user-centered design (UCD) activities at the IDE level, hence developing software products with an adequate level of usability.

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

Although expert-based evaluation techniques such as heuristic evaluation and cognitive walkthrough are often inexpensive and quick to apply, they have not proved to be effective in capturing contextual factors that arise in real-world settings. It is no trivial issue to understand how such evaluation techniques could be modified or differently applied so as to better take into account context, without loosing the advantages inherent in those techniques. This paper explores a possible way of addressing the trade-off between application of cognitive walkthrough and low cost improvements of its contextual validity. In particular, we propose and investigate the benefits of supporting cognitive walkthrough with video data about user interaction with an eLearning course on mobile device. Initial results from this study indicated that video data provided evaluators with a more detailed understanding of user characteristics and interaction contexts, leading to an improvement of their assessments in terms of the total number of system's flaws detected. Video data was regarded by experts as both relevant and useful, especially for tuning the evaluation focus on types of difficulties they would normally not have experienced because of differences in terms of abilities, knowledge and background with those of the target user group. Copyright 2005 ACM.

There is a need for an overall framework that can support the entire knowledge discovery process. Of special interest, is the role of visualization in such a framework. This paper focuses on the exploitation of various visual strategies with a view to discovering knowledge through metarules and association rules.

We deal with the problem of automatic visualization of a large set of medical data and propose a framework that addresses the involved issues. Our approach does not depend on the particular data under analysis and its aim is to produce a highly effective visual representation. Such objective is made possible by the consistency of a set of logic rules contained in a knowledge base. These rules express facts and relationships about the visual domain, the data domain, the mapping between both domains, and the perceptual domain. Our approach has been tested against a challenging set of data, i.e., the biomedical data coming from the Nefrology Center of the Civil hospital of Vigevano.

Among the wide range of digital libraries, an interesting, yet quite neglected, subclass is constituted by those exclusively dealing with newspaper clippings. Compared with book-oriented digital libraries, clipping libraries are more difficult to seize, since they are wide and unstructured, and the subjects and content of a clipping are completely heterogeneous. LAURIN is an EU-funded project involving seventeen participants from several countries, including two software companies and a large group of libraries, whose main purpose is to set up a network of digitalized newspaper clipping archives that can be easily accessed through the Internet, for searching and retrieving clippings. The project also provides the libraries with models and methodologies to be used for scanning, digitalizing, storing, indexing, and making accessible newspaper clippings. The core of the LAURIN system is an integrated multilingual Thesaurus. In this paper we illustrate how to express the thesaurus in terms of a knowledge base, and how to exploit such a knowledge base to improve the fundamental task of clipping retrieval.

No abstract available.

The management and retrieval of Web data has received significant attention. Among the various approaches, systems have been proposed whose main goal is to provide a framework to integrate different and heterogeneous information sources into a common domain model. The Web-At-A-Glance (WAG) system falls into this category, its key characteristic being that instead of requiring an explicit description of the sources, it attempts to semi-automatically classify the information gathered from various sites based on the conceptual model of the domain of interest. The initial WAG prototype dealt exclusively with "standard" Web pages, which typically are HTML or XML documents and present non- or semi-structured information, and they vary widely in their means of presenting information to the user. We present the extension of the WAG system to deal with form pages. In particular, we describe how the system semiautomatically first extracts a conceptual schema for the form page, and then fills and submits the form in consequence of a user query expressed on the domain conceptual schema.

During the last few years, databases have been growing in size, varieties of data, numbers of users and diversity of applications. Many such applications deal with data characterized by the temporal dimension (e.g. medical records, biographical data, financial data, etc.). Typically, end-users of these data are competent in the field of the application but are not computer experts. They need easy-to-use systems that are able to support them in the task of accessing and manipulating the data contained in the databases. It is well-known that visual techniques are suitable in supporting users interacting with large data sets. However, not much research has been carried on specifically on the relationship between visual techniques and time-dependent data. This paper aims at filling this gap by presenting an overview of the main visual techniques for interactive exploration of time-oriented (historical) information.

The need for friendly environments for effective information access is further enforced by the growth of the global Internet, which is causing a dramatic change in both the kind of people who access the information and the types of information itself (ranging from unstructured multimedia data to traditional record-oriented data). To cope with these new demands, the interaction techniques traditionally offered to the users have to evolve and eventually integrate in a powerful interface to the global information infrastructure. The new interaction mechanisms must be especially friendly and easy-to-use, since, given the enormous quantity of information sources available on the Internet, most of the users remain "permanent novices" with respect to each one of the sources they have access to. The article offers a survey of the main approaches adopted for letting the users effectively interact with the Web. Thus, it covers topics related with both extracting the information of interest spread over existing Web sites and building new, more usable sites. Being mainly "user-centered", the article analyzes proposals coming from different areas, namely DB, AI, and HCI, which share the final goal of making the Web a huge, easy-to-access information repository.

Recent database applications are typically oriented towards a large set of non-expert users and therefore they need to be equipped with suitable interfaces facilitating the interaction with the system. It has been pointed out that temporal data constitute a growing part of such recent applications. However, there are not many proposals of systems providing the user with a friendly and effective environment to interact with temporal data. Our work concentrates on a user-oriented visual query system (called TVQE) for temporal databases. The system adopts a diagrammatic representation of the database schema and a "graphical notebook" as metaphor to allow the user to easily formulate conventional and temporal queries. The visual interaction mechanisms of TVQE are provided with precise syntax and semantics founded on formally defined graph based data model and minimal set of temporal graphical primitives.

In this paper we present a novel approach to accessing the Web, that enables automatically acquiring data from Web sites and making them accessible to the user through a database query paradigm. The basic idea is to build, once the user has specified a generic domain of interest, the domain conceptual representation, to instantiate it with data extracted from Web sites (so to build a materialized view over the Web), and to query such a conceptual representation through an easy-to-use visual interface. Knowledge representation techniques are used for both the internal modeling of the conceptual representation and for supporting the automatic extraction of data from Web sites to feed the materialized view. We describe a prototype implementation, focusing on the internal representation of information and on the process for analysing Web sites and acquiring data from them. Our preliminary results support our intuition that, at least for certain kinds of queries, the proposed approach can effectively provide the desired information.

The Internet revolution has made an enormous quantity of information available to a disparate variety of people. The amount of information, the typical access modality (that is, browsing), and the rapid growth of the Net, force the user, while searching for the information of interest, to dip into multiple sources, in a labyrinth of millions of links. Web-at-A-Glance (WAG) is a system allowing the user to query (instead of browsing) the Web. WAG performs this ambitious task by constructing a personalized database, pertinent to the user's interests. The system semi-automatically gleans the most relevant information from a Web site or several Web sites, stores them into a database cooperatively designed with the user, and allows her/him to query such a database through a visual interface equipped with a powerful multimedia query language. This paper presents the design philosophy, the architecture and the core of the WAG system. A prototype WAG is being implemented to test the feasibility of the proposed approach.

Recent years have seen the publication of several methodologies for query processing in a Multidatabase (MDB) environment. Whilst different approaches are pertinent to specific CIS architectures, the issue of efficiency of processing has received relatively little attention. This is somewhat surprising considering the potentially large costs associated with MDB queries. In this paper, we consider the efficiency of processing queries in the VENUS (Esprit 6398) environment. The goal of this system is to allow a user to query multiple data sources by interacting with a conceptually single database, and by providing a query interface based on the use of visual formalisms. This system takes advantage of existing multidatabase query processing strategies, and deals with optimization issues by considering the exploitation of inter- and intra-schema semantics. We present a methodology which harnesses knowledge of the set-theoretic relationships between classes participating in a query, and demonstrate that significant saving can be made in MDB query processing. The methodology presented here is based on a powerful data model, the Graph Model, which we argue is suitable as the canonical model of a MDB. We also introduce a logic language to formally express interdependencies between classes belonging to different schemas. These assertions form the "backbone" of the MDB and, we may benefit in several ways from the possibility of reasoning about them, specifically by applying them in MDB query processing.

Describes an approach for multiparadigmatic visual access integration of different interaction paradigms. The user is provided with an adaptive interface augmented by a user model, supporting different visual representations of both data and queries. The visual representations are characterized on the basis of the chosen visual formalisms, namely forms, diagrams and icons. To access different databases, a unified data model called the "graph model" is used as a common underlying formalism to which databases, expressed in the most popular data models, can be mapped. Graph model databases are queried through the adaptive interface. The semantics of the query operations is formally defined in terms of graphical primitives. Such a formal approach permits us to define the concept of an "atomic query", which is the minimal portion of a query that can be transferred from one interaction paradigm to another and processed by the system. Since certain interaction modalities and visual representations are more suitable for certain user classes, the system can suggest to the user the most appropriate interaction modality as well as the visual representation, according to the user model. Some results on user model construction are presented.

Several applications use algorithms for drawing k-layered networks and, in particular, 2-layered networks (i.e. bipartite graphs). Bipartite graphs are commonly drawn in the plane so that all vertices lie on two parallel vertical lines, and an important requirement in drawing such graphs is to minimize edge crossings. Such a problem is NP-complete even when the position of the vertices on one layer is held fixed. This paper presents a heuristic, called the assignment heuristic, for edge crossing minimization in bipartite graphs, which works by reducing the problem to an assignment problem. The main idea of the assignment heuristic is to position simultaneously all the vertices of one layer, so that the mutual interaction of the position of all the vertices can be taken into account. We also show that the idea underlying the assignment heuristic can be effectively applied in other cases requiring edge crossing minimization.>

No abstract available.

Managing interschema knowledge is an essential task when dealing with cooperative information systems. We propose a logical approach to the problem of both expressing interschema knowledge, and reasoning about it. In particular, we set up a structured representation language for expressing semantic interdependencies between classes belonging to different database schemas, and present a method for reasoning over such. interdependencies. The language and the associated reasoning technique makes it possible to build a logic-based module that can draw useful inferences whenever the need arises of both comparing and combining the knowledge represented in the various schemas. Notable examples of such inferences include checking the coherence of interschema knowledge, and providing integrated access to a cooperative information system.

A system to query databases using diagrams as a standard user interface is proposed. The system, called Query by Diagram* (QBD*), makes use of a conceptual data model, a query language on this model, and a graphical user interface. The conceptual model is the entity-relationship model. The query language, whose expressive power allows recursive queries, supports visual interaction. The main characteristics of the interface are ease of use and the availability of a rich set of primitives for schema selection and query formulation. The expressive power of QBD* and G/sup +/, which are the only languages allowing recursive queries to be expressed graphically are compared.>