K-means clustering is widely used, but its iterative and initialization-sensitive nature makes results hard to interpret, compare, and debug, especially when multiple runs are needed to obtain a reliable solution. This paper presents VISPEK, a visual interactive system for progressive ensemble k-means clustering that helps users understand how clustering results evolve and how agreement emerges across runs. By combining Progressive Visual Analytics with an ensemble-based strategy, VISPEK exposes intermediate results, stability and quality metrics, and similarities among runs, enabling users to inspect, explain, and steer the clustering process before convergence. In this way, VISPEK supports the interpretation of consensus formation, highlights uncertainty, and helps users identify promising results early. We validate the approach through two usage scenarios and an expert study with data science and machine learning experts, showing that VISPEK improves analysis transparency while reducing time and computational effort.

Abstract Differential gene expression (DGE) analysis is one of the most widely used techniques for investigating RNA‐seq data and supports numerous medical and biological applications, including biomarker identification for diagnosis and prognosis, as well as the evaluation of medical treatments. However, performing DGE analysis typically requires navigating a complex multistep pipeline and proficiency in programming languages such as R. This poses a barrier for researchers ‐‐‐ including biologists and clinicians ‐‐‐ who may lack coding expertise, and adds overhead for experienced bioinformaticians. To address these challenges, we propose a workflow‐driven visual analytics approach for DGE analysis that integrates state‐of‐the‐art methodologies and supports interactive exploration of gene expression data through a guided step‐by‐step process. Building on this workflow, we developed GEVIS, a visual analytics system that enables users to conduct DGE analysis without writing code, thereby reducing analytical overhead and making the process more accessible to a broader audience. Both the workflow and the GEVIS system have been validated by experts in bioinformatics and demonstrated through a use case.

Conversational Information Access systems have experienced widespread diffusion thanks to the natural and effortless interactions they enable with the user. In particular, they represent an effective interaction interface for conversational search (CS) and conversational recommendation (CR) scenarios. Despite their commonalities, CR and CS systems are often devised, developed, and evaluated as isolated components. Integrating these two elements would allow for handling complex information access scenarios, such as exploring unfamiliar recommended product aspects, enabling richer dialogues, and improving user satisfaction. As of today, the scarce availability of integrated datasets - focused exclusively on either of the tasks - limits the possibilities for evaluating by-design integrated CS and CR systems. To address this gap, we propose CoSRec, the first dataset for joint Conversational Search and Recommendation (CSR) evaluation. The CoSRec test set includes 20 high-quality conversations, with human-made annotations for the quality of conversations, and manually crafted relevance judgments for products and documents. Additionally, we provide supplementary training data comprising partially annotated dialogues and raw conversations to support diverse learning paradigms. CoSRec is the first resource to model CR and CS tasks in a unified framework, enabling the training and evaluation of systems that must shift between answering queries and making suggestions dynamically.

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.

As cyber threats continue to evolve, the need for highly skilled cyber defence operators becomes increasingly critical. In this work, we aim to provide a multidisciplinary exploration into the current educational landscape, focusing on the following pivotal areas: cyber defence educational initiatives, digital skills, technological enablers, and ethical considerations. First, we present the current landscape of cyber defence educational initiatives. Then, we examine the required digital skills, virtual reality and augmented reality initiatives for immersive learning experiences and delve into the advantages of game-based learning for skill acquisition in order to finally provide a holistic evaluation of the complexities involved in cyber defence training. We underscore the importance of standardising training modules tailored to diverse roles within cyber defence. The discussion emphasises the need for ethical and legal guidelines, especially concerning privacy and bias in AI-driven educational tools. Finally, we highlight the importance of dynamic curricula that include technical, legal, and soft skills, along with hands-on training through simulations to prepare operators for real-world cyber threats. This work will serve as a foundation for academics, industry professionals, and policy-makers interested in elevating the standards and effectiveness of cyber defence training suggesting ideas for more specialised, adaptable, and ethically responsible programs.

Machine learning techniques are a driving force for research in various fields, from credit card fraud detection to stock analysis. Recently, a growing interest in increasing human involvement has emerged, with the primary goal of improving the interpretability of machine learning models. Among different techniques, Partial Dependence Plots (PDP) represent one of the main model-agnostic approaches for interpreting how the features influence the prediction of a machine learning model. However, its limitations (i.e., visual interpretation, aggregation of heterogeneous effects, inaccuracy, and computability) could complicate or misdirect the analysis. Moreover, the resulting combinatorial space can be challenging to explore both computationally and cognitively when analyzing the effects of more features at the same time. This paper proposes a conceptual framework that enables effective analysis workflows, mitigating state-of-the-art limitations. The proposed framework allows for exploring and refining computed partial dependences, observing incrementally accurate results, and steering the computation of new partial dependences on user-selected subspaces of the combinatorial and intractable space. With this approach, the user can save both computational and cognitive costs, in contrast with the standard monolithic approach that computes all the possible combinations of features on all their domains in batch. The framework is the result of a careful design process involving experts' knowledge during its validation and informed the development of a prototype, W4SP (available athttps://aware-diag-sapienza.github.io/W4SP/), that demonstrates its applicability traversing its different paths. A case study shows the advantages of the proposed approach.

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.

Abstract The use and creation of machine‐learning‐based solutions to solve problems or reduce their computational costs are becoming increasingly widespread in many domains. Deep Learning plays a large part in this growth. However, it has drawbacks such as a lack of explainability and behaving as a black‐box model. During the last few years, Visual Analytics has provided several proposals to cope with these drawbacks, supporting the emerging eXplainable Deep Learning field. This survey aims to (i) systematically report the contributions of Visual Analytics for eXplainable Deep Learning; (ii) spot gaps and challenges; (iii) serve as an anthology of visual analytical solutions ready to be exploited and put into operation by the Deep Learning community (architects, trainers and end users) and (iv) prove the degree of maturity, ease of integration and results for specific domains. The survey concludes by identifying future research challenges and bridging activities that are helpful to strengthen the role of Visual Analytics as effective support for eXplainable Deep Learning and to foster the adoption of Visual Analytics solutions in the eXplainable Deep Learning community. An interactive explorable version of this survey is available online at https://aware‐diag‐sapienza.github.io/VA4XDL .

Progressive visual analytics allows users to interact with early, partial results of long-running computations on large datasets. In this context, computational steering is often brought up as a means to prioritize the progressive computation. This is meant to focus computational resources on data subspaces of interest so as to ensure their computation is completed before all others. Yet, current approaches to select a region of the view space and then to prioritize its corresponding data subspace either require a one-to-one mapping between view and data space, or they need to establish and maintain computationally costly index structures to trace complex mappings between view and data space. We present steering-by-example, a novel interactive steering approach for progressive visual analytics, which allows prioritizing data subspaces for the progression by generating a relaxed query from a set of selected data items. Our approach works independently of the particular visualization technique and without additional index structures. First benchmark results show that steering-by-example considerably improves Precision and Recall for prioritizing unprocessed data for a selected view region, clearly outperforming random uniform sampling.

RadViz contributes to multidimensional analysis by using 2D points for encoding data elements and interpreting them along the original data dimensions. For these characteristics it is used in different application domains, like clustering, anomaly detection, and software visualization. However, it is likely that using the dimension arrangement that comes with the data will produce a plot that leads users to make inaccurate conclusions about points values and data distribution. This article attacks this problem without altering the original RadViz design: It defines, for both a single point and a set of points, the metric of effectiveness error, and uses it to define the objective function of a dimension arrangement strategy, arguing that minimizing it increases the overall RadViz visual quality. This article investigated the intuition that reducing the effectiveness error is beneficial for other well-known RadViz problems, like points clumping toward the center, many-to-one plotting of non-proportional points, and cluster separation. It presents an algorithm that reduces to zero the effectiveness error for a single point and a heuristic that approximates the dimension arrangement minimizing the effectiveness error for an arbitrary set of points. A set of experiments based on 21 real datasets has been performed, with the goals of analyzing the advantages of reducing the effectiveness error, comparing the proposed dimension arrangement strategy with other related proposals, and investigating the heuristic accuracy. The Effectiveness Error metric, the algorithm, and the heuristic presented in this article have been made available in a d3.js plugin at https://aware-diag-sapienza.github.io/d3-radviz.

Analyzing and mitigating the threats that cyber-attacks pose on the services of a critical infrastructure is not a trivial activity. Research solutions have been developed using data about the devices used for implementing the services, services dependencies, network topology, and the vulnerabilities that can be exploited to attack the network. However, most of the proposed solutions fail to consider these aspects in an integrated fashion, allowing the user to understand global dependencies and weaknesses. This paper contributes this issue with BUCEPHALUS, a Visual Analytics solution providing a) a visual overview of the existing relationships among business functions, devices, and vulnerabilities, and b) a what-if analysis scenario, in which the user is supported on making decisions on which vulnerabilities are more appropriate to fix. BUCEPHALUS has been developed and validated within a user-centered design process involving security professionals.

Filtering is one of the basic interaction techniques in Information Visualization, with the main objective of limiting the amount of displayed information using constraints on attribute values. Research focused on direct manipulation selection means or on simple interactors like sliders or check-boxes: while the interaction with a single attribute is, in principle, straightforward, getting an understanding of the relationship between multiple attribute constraints and the actual selection might be a complex task. To cope with this problem, usually referred as cross-filtering, the paper provides a general definition of the structure of a filter, based on domain values and data distribution, the identification of visual feedbacks on the relationship between filters status and the current selection, and guidance means to help in fulfilling the requested selection. Then, leveraging on the definition of these design elements, the paper proposes CrossWidgets, modular attribute selectors that provide the user with feedback and guidance during complex interaction with multiple attributes. An initial controlled experiment demonstrates the benefits that CrossWidgets provide to cross-filtering activities.

Data-driven analysis, AI, machine learning and modern data science pipelines of analysis are becoming more and more important possibilities when coping with problem solving. In this respect, the capability to explore data, understands how algorithmic approaches work and steer them toward the desired goals make Visualization and Visual Analytics strong research fields in which to invest efforts. While this importance has been understood by several countries (e.g., USA, Germany, France) that created strong national communities around these research fields, in Italy the research efforts in these fields are still disjointed. With the second edition of ITAVIS we want to consolidate and expand on the encouraging results obtained from the first edition (ITA.WA.- Italian Visualization & Visual Analytics workshop). The goal is to make an additional step toward the creation of an Italian research community on these topics, allowing identification of research directions, joining forces in achieving them, linking researchers and practitioners and developing common guidelines and programs for teaching activities on the fields of Visualization and Visual Analytics.

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.

Management and maintenance of mobile networks is a challenging activity: key point indicators related to different dimensions need to be evaluated in order to make business decisions and/or to manage problems arising from the network and the relative impact on business. This paper presents a visual analytics solution able to geo-reference up to three key point indicators, aiming at supporting operators in charge of analyzing the mobile network, detecting possible mobile network failures and managing resulting business oriented decisions. The proposed system has been developed collaboratively by University of Rome “La Sapienza” and TIM.

Modern software systems require the support of automatic program analyses to answer questions about their correctness, reliability, and safety. In recent years, symbolic execution techniques have played a pivotal role in this field, backing research in different domains such as software testing and software security. Like other powerful machine analyses, symbolic execution is often affected by efficiency and scalability issues that can be mitigated when a domain expert interacts with its working, steering the computation to achieve the desired goals faster. In this paper we explore how visual analytics techniques can help the user to grasp properties of the ongoing analysis and use such insights to refine the symbolic exploration process. To this end, we discuss two real-world usage scenarios from the malware analysis and the vulnerability detection domains, showing how our prototype system can help users make a wiser use of symbolic exploration techniques in the analysis of binary code.

Abstract Visualizing contextual information to a map often comes at the expense of overplotting issues. Especially for use cases with relevant map features in the immediate vicinity of an information to add, occlusion of the relevant map context should be avoided. We present SurgeryCuts, a map manipulation technique for the creation of additional canvas area for contextual visualizations on maps. SurgeryCuts is occlusion‐free and does not shift, zoom or alter the map viewport. Instead, relevant parts of the map can be cut apart. The affected area is controlledly distorted using a parameterizable warping function fading out the map distortion depending on the distance to the cut. We define extended metrics for our approach and compare to related approaches. As well, we demonstrate the applicability of our approach at the example of tangible use cases and a comparative user study.

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.

In the last years, several standards and frameworks have been developed to help organizations to increase the security of their Information Technology (IT) systems. In order to deal with the continuous evolution of the cyber-attacks complexity, such solutions have to cope with an overwhelming set of concepts, and are perceived as complex and hard to implement. The exploration of the cyber-security state of an organization can be made more effective and proficient if supported by the right level of automation. This paper presents the implementation of a visual analytics solution, called CybeR secUrity fraMework BrowSer (CRUMBS) [2], targeted at dealing with the Italian Adaptation of the Cyber Security Framework (IACSF), derived by the National Institute of Standards and Technology (NIST) proposal [1], adaptation that, in its full complexity, presents the security managers with hundreds of scattered concepts, like functions, categories, subcategories, priorities, maturity levels, current and target profiles, and controls, making its adoption a complex activity. The prototype is available at: http://awareserver.dis.uniroma1.it:11768/crumbs/.

Data-driven approaches to problem solving and data analysis are becoming more and more important problems to consider and on which apply research ideas. In this respect, the capability to explore data, understands how algorithmic approaches work and steer them toward the desired goals make Visualization and Visual Analytics strong research fields in which to invest efforts. While this importance has been understood by several countries (e.g., USA, Germany, France) in Italy the research efforts in these fields are still disjointed. With the first edition of the ITA.WA. (Italian Visualization & Visual Analytics) workshop the goal is to make a step toward the creation of an Italian research community on these topics, allowing identification of research direction, joining forces in achieving them and developing common guidelines and programs for teaching activities.

This paper presents a system that prioritizes user actions according to the domain context and user preferences, in order to provide the adaptation process with a partial order of functionalities, useful to optimize the user interface with the main goal of minimizing screen usage and user interaction. The solution is instantiated in a smart home environment in the form of a smart user interface exploiting visual means to convey information. An user evaluation based on this use case is provided, confirming the benefits of the solution in terms of utility and easiness of usage.

Exploits based on ROP (Return-Oriented Programming) are increasingly present in advanced attack scenarios. Testing systems for ROP-based attacks can be valuable for improving the security and reliability of software. In this paper, we propose ROPMATE, the first Visual Analytics system specifically designed to assist human red team ROP exploit builders. In contrast, previous ROP tools typically require users to inspect a puzzle of hundreds or thousands of lines of textual information, making it a daunting task. ROPMATE presents builders with a clear interface of well-defined and semantically meaningful gadgets, i.e., fragments of code already present in the binary application that can be chained to form fully-functional exploits. The system supports incrementally building exploits by suggesting gadget candidates filtered according to constraints on preserved registers and accessed memory. Several visual aids are offered to identify suitable gadgets and assemble them into semantically correct chains. We report on a preliminary user study that shows how ROPMATE can assist users in building ROP chains.

Malware associated with Web downloads is responsible for many attacks trying to execute malicious code on a remote machine. Web browsers are protected by anti-malware utilities that try to distinguish between good downloads and bad downloads, blocking the bad ones and alerting the user. In order to cope with the uncertainty of such a process, very often the final decision is made using suitable thresholds, giving rise to a 3 categories classification: good downloads, bad downloads, and “in the middle” downloads (i.e., the uglies). In this situation, it is possible to involve the user (e.g., the security manager) in the decision loop, presenting him with the details of the decision process in a way he can either be more confident about the system decisions or he can refine what has been done automatically, e.g., promoting an ugly download to a good one. The paper addresses this problem presenting a visual analytics solution supporting the analysis of the classification system presented in AMICO [24], providing the user with a better understanding of the classification decisions and the possibility of changing the classification results. A prototype is available at: http://awareserver.dis.uniroma1.it:11768/malvis/.

In the last years, several standards and frameworks have been developed to help organizations to increase the security of their Information Technology (IT) systems. In order to deal with the continuous evolution of the cyberattacks complexity, such solutions have to cope with an overwhelming set of concepts, and are perceived as complex and hard to implement. This paper presents a visual analytics solution targeted at dealing with the Italian Adaptation of the Cyber Security Framework (IACSF), derived by the National Institute of Standards and Technology (NIST) proposal, adaptation that, in its full complexity, presents the security managers with hundreds of scattered concepts, like functions, categories, subcategories, priorities, maturity levels, current and target profiles, and controls, making its adoption a complex activity. The system has been designed together with the security experts of one of the largest Italian public organization and has the goal of providing a continuous overview of the adoption process, providing a prioritizing view that helps in effectively planning the required activities. A prototype is available at: http://awareserver.dis.uniroma1.it:11768/crumbs/.

We present the innovative visual analytics approach of the VATE system, which eases and makes more effective the experimental evaluation process by introducing the what-if analysis. The what-if analysis is aimed at estimating the possible effects of a modification to an IR system to select the most promising fixes before implementing them, thus saving a considerable amount of effort. VATE builds on an analytical framework which models the behavior of the systems in order to make estimations, and integrates this analytical framework into a visual part which, via proper interaction and animations, receives input and provides feedback to the user.

With today's technical possibilities, a stable visualization scenario can no longer be assumed as a matter of course, as underlying data and targeted display setup are much more in flux than in traditional scenarios. Incremental visualization approaches are a means to address this challenge, as they permit the user to interact with, steer, and change the visualization at intermediate time points and not just after it has been completed. In this paper, we put forward a model for incremental visualizations that is based on the established Data State Reference Model, but extends it in ways to also represent partitioned data and visualization operators to facilitate intermediate visualization updates. In combination, partitioned data and operators can be used independently and in combination to strike tailored compromises between output quality, shown data quantity, and responsiveness-i.e., frame rates. We showcase the new expressive power of this model by discussing the opportunities and challenges of incremental visualization in general and its usage in a real world scenario in particular.

The increasing number of security accidents, due to failures or cyber attacks, that critical infrastructures/smart grids are experimenting is pushing researchers to investigate on the different issues associated with such a problem. Among them, an increasing attention has been devoted to the development of monitoring systems that exploit geographical data about the network under control. This papers focuses on situational awareness and describes a Visual Analytics solution for monitoring and put in tight relation data from network level (computers, routers, etc.) with organization's mission. The goal of the proposed solution is to make security officers aware of both the actual network endangerment level and the impact it actually has on the infrastructure mission, making clear the relationships and the responsibilities that exist between the network level and the business level. The solution proposed is then instantiated on ACEA infrastructure, a company that provides power and water purification services to cities in central Italy (millions of end users).

Situational awareness is a key concept in cyber-defence. Its goal is to make the user aware of different and complex aspects of the network he or she is monitoring. This paper proposes PERCIVAL, a novel visual analytics environment that contributes to situational awareness by allowing the user to understand the network security status and to monitor security events that are happening on the system. The proposed visualization allows for comparing the proactive security analysis with the actual attack progress, providing insights on the effectiveness of the mitigation actions the system has triggered against the attack and giving an overview of the possible attack's evolution. Moreover, the same visualization can be fruitfully used in the proactive analysis since it allows for getting details on computed attack paths and evaluating the mitigation actions that have been proactively computed by the system. A preliminary user study provided a positive feedback on the prototype implementation of the system. A video of the system is available at: https://youtu.be/uMpYCJCX95k.

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.

Density maps allow for visually rendering density differences, usually mapping density values to a grey or color scale. The paper analyzes the drawbacks arising from the commonly used strategies and introduces a novel technique able to improve the overall mapping process. The technique is driven by statistical knowledge about the density distribution and a set of quality metrics allows for validating, in an objective way, its effectiveness.

In this paper we present a novel, hybrid, and automatic strategy whose goal is to reduce the 2D scatter plot cluttering. The presented technique relies on a combination of nonuniform sampling and pixel displacement and it is driven by perceptual results coming from a suitable user study. The same results are used to define precise quality metrics that allow for validating our approach.

The wide spread of mobile devices in the consumer market has posed a number of new issues in the design of internet applications and their user interfaces. In particular, applications need to adapt their interaction modalities to different portable devices. In this paper we address the problem of defining models and techniques for designing internet based applications that automatically adapt to different mobile devices. First, we define a formal model that allows for specifying the interaction in a way that is abstract enough to be decoupled from the presentation layer, which is to be adapted to different contexts. The model is mainly based on the idea of describing the user interaction in terms of elementary actions. Then, we provide a formal device characterization showing how to effectively implements the AIUs in a multidevice context.

Dealing with visualizations containing large data set is a challenging issue and, in the field of information visualization, almost every visual technique reveals its drawback when visualizing large number of items. To deal with this problem we introduce a formal environment, modeling in a virtual space the image features we are interested in (e.g, absolute and relative density, clusters, etc.) and we define some metrics able to characterize the image decay. Such metrics drive our automatic techniques (i.e., not uniform sampling) rescuing the image features and making them visible to the user. In This work we focus on 2D scatter-plots, devising a novel non uniform data sampling strategy able to preserve in an effective way relative densities.

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.

Producing high quality software is a very hard task. In the last years a big effort has been spent in devising techniques for estimating and/or measuring software properties. This ranges from forecasting, in a very early stage, the cost of software production to measuring several subcharacterics in order to assess the internal and external software quality. The role of predicting vs. measuring is gaining an increas ing relevance. As an example, the recently revised ISO 9126 standard [1] introduces the concept of Estimated (Predicted) Product Quality. It is clear that the sooner estimated figures are available, the better is possible to modify some design choices. Among all the aspects involved in software developing, a central role is played by the chosen software architecture. Estimating the quality characteristics of such architecture is a strategic activity that can drive several following design decision. In this paper we report the experience of an architectural assessment performed in Ericsson Lab Italy. The assessment was performed according to the framework presented by Jan Bosch in [2].

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.

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.

No abstract available.

Abstract In recent years, several attempts have been made to define query languages characterized by both high expressive power and easy query formulation. Several issues concern graphical applications, based on the diagrammatic representation of a semantic model and visual interaction. This paper describes the architecture and the implementation of a graphical query system, based on the diagrammatic representation of entity relationship schemata. The query language underlying the system allows the formulation of recursive queries; moreover, user interaction in both managing diagrams and expressing queries is simplified by the presence of a fully visual environment and a rich set of interaction strategies.

The data dictionary contains the description of all types of data produced, managed, exchanged, and maintained in an organization. Data descriptions (very often hundreds of schemas) should be organized in such a way to allow all the users of the information system to understand the meaning of data and their relationships. To this end, a set of structuring primitives for a dictionary of entity relationship data schemas is presented. The formal properties of such structuring primitives are investigated, and the feasibility of their usage is shown by providing a methodology for dictionary design.>

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.>