Artificial intelligence offers powerful methods for audio processing and analysis. Still, complex workflows and the required programming skills often limit access for students and domain experts, such as marine bioacousticians and soundscape ecologists. We present "AI EcoSound Tutor", a code-free and interactive tool that lowers these barriers by allowing users to construct and explore a complete AI pipeline for audio data analysis. Starting from raw recordings, users can choose from various feature extraction techniques (MFCC, OpenL3), apply dimensionality reduction methods (PCA, t-SNE, UMAP), and optionally perform unsupervised clustering (K-Means, GMM,HDBSCAN). The results are displayed with an interactive 2D visualisation where the user can compare multiple plots by employing various techniques, including PCA and t-SNE. Interactive plots enable the selection of points or clusters of interest, allowing exploration of spectrograms within the desired frequency range, and playing an audio clip corresponding to the selected points. An integrated "Help" feature provides explanations of each method (i.e., what it is, how it works, and its practical use in different domains, such as bioacoustics), fostering both conceptual understanding and useful skill acquisition as learning outcomes. For precomputed features or embeddings, this tool also supports training and evaluating a variety of machine learning models, providing visual feedback on the results. By merging accessibility, interactivity, pedagogy, and domain relevance, our application demystifies AI methods for interdisciplinary education and supporting research in audio analysis.

This paper describes a qualitative study concerning the experiences of blind and visually impaired students learning mathematics in primary schools in Ireland, along with a thematic analysis of the findings. It investigates the instructional challenges and teaching techniques that facilitate an inclusive learning environment through flexible assessment plans. Key focus areas include adapting class collaboration activities, using digital and non-digital materials, and deploying specialized strategies to boost learning skills. This study identified significant barriers, including technology accessibility, classroom mobility, and the need for supportive educational aids such as tactile materials and Braille resources. This paper also discusses the importance of support and assistance, including teacher training, parental involvement, and peer support processes. Based on expert opinions, the study identifies gaps in current solutions and suggests future research and development directions. These include the advancement of User-Centred Design (UCD), the integration of multimodal techniques, and the improvement of collaborative and interactive learning solutions. Furthermore, this study recommends reforming educational policy and comprehensive training programs for educators to ensure that visually impaired students receive an equitable mathematics education. This study lays the groundwork for future advancements in teaching methodologies and assistive technologies, aiming to remove educational barriers and foster academic excellence within blind and visually impaired students.

This paper presents MOD-kt, a modular framework designed to bridge the gap between modern language models and key-term-based document clustering. One of the main challenges of using neural language models for key-term-based clustering is the mismatch between the interpretability of the underlying document representation (i.e. document embeddings) and the more intuitive semantic elements that allow the user to guide the clustering process (i.e. key-terms). Our framework acts as a communication layer between word and document models, enabling key-term-based clustering in the context of document and word models with a flexible and adaptable architecture. We report a comparison of the performance of multiple neural language models on clustering, considering a selected range of relevance metrics. Additionally, a qualitative user study was conducted to illustrate the framework's potential for intuitive user-guided quality clustering of document collections.

Abstract The abundance of digital documents offers a valuable chance to gain insights into public opinion, social structure, and dynamics. However, the scale and volume of these digital collections makes manual analysis approaches extremely costly and not scalable. In this paper, we study the potential of using automated methods from natural language processing and machine learning, in particular weak supervision strategies, to understand how news influence decision making in society. Besides proposing a weak supervision solution for the task, which replaces manual labeling to a certain extent, we propose an improvement of a recently published economic index. This index is known as economic policy uncertainty (EPU) index and has been shown to correlate to indicators such as firm investment, employment, and excess market returns. In summary, in this paper, we present an automated data efficient approach based on weak supervision and deep learning (BERT + WS) for identification of news articles about economical uncertainty and adapt the calculation of EPU to the proposed strategy. Experimental results reveal that our approach (BERT + WS) improves over the baseline method centered in keyword search, which is currently used to construct the EPU index. The improvement is over 20 points in precision, reducing the false positive rate typical to the use of keywords.

Interactive clustering techniques play a key role by putting the user in the clustering loop, allowing her to interact with document group abstractions instead of full-length documents. It allows users to focus on corpus exploration as an incremental task. To explore Information Discovery's incremental aspect, this article proposes a visual component to depict clustering membership changes throughout a clustering iteration loop in both static and dynamic data sets. The visual component is evaluated with an expert user and with an experiment with data streams.

Document clustering is a necessary step in various analytical and automated activities. When guided by the user, algorithms are tailored to imprint a perspective on the clustering process that reflects the user’s understanding of the dataset. More than just allow for customized adjustment of the clusters, a visual analytics approach will provide tools for the user to draw new insights on the collection. While contributing his or her perspective, the user will also acquire a deeper understanding of the data set. To that effect, we propose a novel visual analytics system for interactive document clustering. We built our system on top of clustering algorithms that can adapt to user’s feedback. In the proposed system, initial clustering is created based on the user-defined number of clusters and the selected clustering algorithm. A set of coordinated visualizations allow the examination of the dataset and the results of the clustering. The visualization provides the user with the highlights of individual documents and understanding of the evolution of documents over the time period to which they relate. The users then interact with the process by means of changing key-terms that drive the process according to their knowledge of the documents domain. In key-term-based interaction, the user assigns a set of key-terms to each target cluster to guide the clustering algorithm. We have improved that process with a novel algorithm for choosing proper seeds for the clustering. Results demonstrate that not only the system has improved considerably its precision, but also its effectiveness in the document-based decision making. A set of quantitative experiments and a user study have been conducted to show the advantages of the approach for document analytics based on clustering. We performed and reported on the use of the framework in a real decision-making scenario that relates users discussion by email to decision making in improving patient care. Results show that the framework is useful even for more complex data sets such as email conversations.

Document clustering is an efficient way to get insight into large text collections. Due to the personalized nature of document clustering, even the best fully automatic algorithms cannot create clusters that accurately reflect the user»s perspectives. To incorporate the user»s perspective in the clustering process and, at the same time, effectively visualize document collections to enhance user's sense-making of data, we propose a novel visual analytics system for interactive document clustering. We built our system on top of clustering algorithms that can adapt to user's feedback. First, the initial clustering is created based on the user-defined number of clusters and the selected clustering algorithm. Second, the clustering result is visualized to the user. A collection of coordinated visualization modules and document projection is designed to guide the user towards a better insight into the document collection and clusters. The user changes clusters and key-terms iteratively as a feedback to the clustering algorithm until the result is satisfactory. In key-term based interaction, the user assigns a set of key-terms to each target cluster to guide the clustering algorithm. A set of quantitative experiments, a use case, and a user study have been conducted to show the advantages of the approach for document analytics based on clustering.

The keyterm-based approach is arguably intuitive for users to direct text-clustering processes and adapt results to various applications in text analysis. Its way of markedly influencing the results, for instance, by expressing important terms in relevance order, requires little knowledge of the algorithm and has predictable effect, speeding up the task. This article first presents a text-clustering algorithm that can easily be extended into an interactive algorithm. We evaluate its performance against state-of-the-art clustering algorithms in unsupervised mode. Next, we propose three interactive versions of the algorithm based on keyterm labeling, document labeling, and hybrid labeling. We then demonstrate that keyterm labeling is more effective than document labeling in text clustering. Finally, we propose a visual approach to support the keyterm-based version of the algorithm. Visualizations are provided for the whole collection as well as for detailed views of document and cluster relationships. We show the effectiveness and flexibility of our framework, Vis-Kt , by presenting typical clustering cases on real text document collections. A user study is also reported that reveals overwhelmingly positive acceptance toward keyterm-based clustering.

The worldwide adoption of Twitter turned it into one of the most popular platforms for content analysis as it serves as a gauge of the public’s feeling and opinion on a variety of topics. This is particularly true of political discussions and lawmakers’ actions and initiatives. Yet, one common but unrealistic assumption is that the data of interest for analysis is readily available in a comprehensive and accurate form. Data need to be retrieved, but due to the brevity and noisy nature of Twitter content, it is difficult to formulate user queries that match relevant posts that use different terminology without introducing a considerable volume of unwanted content. This problem is aggravated when the analysis must contemplate multiple and related topics of interest, for which comments are being concurrently posted. This article presents Active Tweet Retrieval Visualization (ATR-Vis), a user-driven visual approach for the retrieval of Twitter content applicable to this scenario. The method proposes a set of active retrieval strategies to involve an analyst in such a way that a major improvement in retrieval coverage and precision is attained with minimal user effort. ATR-Vis enables non-technical users to benefit from the aforementioned active learning strategies by providing visual aids to facilitate the requested supervision. This supports the exploration of the space of potentially relevant tweets, and affords a better understanding of the retrieval results. We evaluate our approach in scenarios in which the task is to retrieve tweets related to multiple parliamentary debates within a specific time span. We collected two Twitter datasets, one associated with debates in the Canadian House of Commons during a particular week in May 2014, and another associated with debates in the Brazilian Federal Senate during a selected week in May 2015. The two use cases illustrate the effectiveness of ATR-Vis for the retrieval of relevant tweets, while quantitative results show that our approach achieves high retrieval quality with a modest amount of supervision. Finally, we evaluated our tool with three external users who perform searching in social media as part of their professional work.

BACKGROUND: The advent of "omics" science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. proteins, metabolites etc.) are represented by nodes and their interactions are represented by edges. An appropriate visualization of data is crucial for understanding such networks, since pathways are related to functions that occur in specific regions of the cell. The force-directed layout is an important and widely used technique to draw networks according to their topologies. Placing the networks into cellular compartments helps to quickly identify where network elements are located and, more specifically, concentrated. Currently, only a few tools provide the capability of visually organizing networks by cellular compartments. Most of them cannot handle large and dense networks. Even for small networks with hundreds of nodes the available tools are not able to reposition the network while the user is interacting, limiting the visual exploration capability. RESULTS: Here we propose CellNetVis, a web tool to easily display biological networks in a cell diagram employing a constrained force-directed layout algorithm. The tool is freely available and open-source. It was originally designed for networks generated by the Integrated Interactome System and can be used with networks from others databases, like InnateDB. CONCLUSIONS: CellNetVis has demonstrated to be applicable for dynamic investigation of complex networks over a consistent representation of a cell on the Web, with capabilities not matched elsewhere.

As the spread of rumours has been increasing every day in online social networks (OSNs), it is important to analyze and understand this phenomenon. Damage caused by the spread of rumours is difficult to handle without a full understanding of the dynamics behind it. One of the central steps of understanding rumour spread is to analyze who spread rumours online, why, and how. In this research, we focus on the steps who and why by describing, implementing, and evaluating an approach that studies whether or not a group of users is actively involved in rumour discussions, and assesses rumour-spreading personality types in OSNs. We implement this general approach using Reddit data, and demonstrate its use by determining which users engage with a recurring rumour, and analyzing their comments using qualitative methods. We find that we can reliably classify users into one of three categories: (1) “Generally support a false rumour”, (2) “Generally refute a false rumour”, or (3) “Generally joke about a false rumour”. Combining text mining techniques, such as text classification, sentiment analysis, and social network analysis, we aim to identify and classify those rumour-spreading user categories automatically and provide a more holistic view of rumour spread in OSNs.

This paper introduces a new large-scale n-gram corpus that is created specifically from social media text. Two distinguishing characteristics of this corpus are its monthly temporal attribute and that it is created from 1.65 billion comments of user-generated text in Reddit. The usefulness of this corpus is exemplified and evaluated by a novel Topic-based Latent Semantic Analysis (TLSA) algorithm. The experimental results show that unsupervised TLSA outperforms all the state-of-the-art unsupervised and semi-supervised methods in SEMEVAL 2015: paraphrase and semantic similarity in Twitter tasks.

The spread of "rumours" in Online Social Networks (OSNs) has grown at an alarming rate. Consequently, there is an increasing need to improve understanding of the social and technological processes behind this trend. The first step in detecting rumours is to identify and extract memes, a unit of information that can be spread from person to person in OSNs. This paper proposes four similarity scores and two novel strategies to combine those similarity scores for detecting the spread of memes in OSNs, with the end goal of helping researchers as well as members of various OSNs to study the phenomenon. The two proposed strategies include: (1) automatically computing the similarity score weighting factors for four elements of a submission and (2) allowing users to engage in the clustering process and filter out outlier submissions, modify submission class labels, or assign different similarity score weight factors for various elements of a submission using a visualization prototype. To validate our approach, we collect submissions on Reddit about five controversial topics and demonstrate that the proposed strategies outperform the baseline.

Technical writing in professional environments, such as user manual authoring for new products, is a task that relies heavily on reuse of content. Therefore, technical content is typically created following a strategy where modular units of text have references to each other. One of the main challenges faced by technical authors is to avoid duplicating existing content, as this adds unnecessary effort, generates undesirable inconsistencies, and dramatically increases maintenance and translation costs. However, there are few computational tools available to support this activity. This paper investigates the use of different similarity methods for the task of identification of reuse opportunities in technical writing. We evaluated our results using existing ground truth as well as feedback from technical authors. Finally, we also propose a tool that combines text similarity algorithms with interactive visualizations to aid authors in understanding differences in a collection of topics and identifying reuse opportunities.

BACKGROUND: Set comparisons permeate a large number of data analysis workflows, in particular workflows in biological sciences. Venn diagrams are frequently employed for such analysis but current tools are limited. RESULTS: We have developed InteractiVenn, a more flexible tool for interacting with Venn diagrams including up to six sets. It offers a clean interface for Venn diagram construction and enables analysis of set unions while preserving the shape of the diagram. Set unions are useful to reveal differences and similarities among sets and may be guided in our tool by a tree or by a list of set unions. The tool also allows obtaining subsets' elements, saving and loading sets for further analyses, and exporting the diagram in vector and image formats. InteractiVenn has been used to analyze two biological datasets, but it may serve set analysis in a broad range of domains. CONCLUSIONS: InteractiVenn allows set unions in Venn diagrams to be explored thoroughly, by consequence extending the ability to analyze combinations of sets with additional observations, yielded by novel interactions between joined sets. InteractiVenn is freely available online at: www.interactivenn.net .

Similarity-based layouts generated by multidimensional projections or other dimension reduction techniques are commonly used to visualize high-dimensional data. Many projection techniques have been recently proposed addressing different objectives and application domains. Nonetheless, very little is known about the effectiveness of the generated layouts from a user's perspective, how distinct layouts from the same data compare regarding the typical visualization tasks they support, or how domain-specific issues affect the outcome of the techniques. Learning more about projection usage is an important step towards both consolidating their role in high-dimensional data analysis and taking informed decisions when choosing techniques. This work provides a contribution towards this goal. We describe the results of an investigation on the performance of layouts generated by projection techniques as perceived by their users. We conducted a controlled user study to test against the following hypotheses: (1) projection performance is task-dependent; (2) certain projections perform better on certain types of tasks; (3) projection performance depends on the nature of the data; and (4) subjects prefer projections with good segregation capability. We generated layouts of high-dimensional data with five techniques representative of different projection approaches. As application domains we investigated image and document data. We identified eight typical tasks, three of them related to segregation capability of the projection, three related to projection precision, and two related to incurred visual cluttering. Answers to questions were compared for correctness against `ground truth' computed directly from the data. We also looked at subject confidence and task completion times. Statistical analysis of the collected data resulted in Hypotheses 1 and 3 being confirmed, Hypothesis 2 being confirmed partially and Hypotheses 4 could not be confirmed. We discuss our findings in comparison with some numerical measures of projection layout quality. Our results offer interesting insight on the use of projection layouts in data visualization tasks and provide a departing point for further systematic investigations.

Automatic data classification is a computationally intensive task that presents variable precision and is considerably sensitive to the classifier configuration and to data representation, particularly for evolving data sets. Some of these issues can best be handled by methods that support users' control over the classification steps. In this paper, we propose a visual data classification methodology that supports users in tasks related to categorization such as training set selection; model creation, application and verification; and classifier tuning. The approach is then well suited for incremental classification, present in many applications with evolving data sets. Data set visualization is accomplished by means of point placement strategies, and we exemplify the method through multidimensional projections and Neighbor Joining trees. The same methodology can be employed by a user who wishes to create his or her own ground truth (or perspective) from a previously unlabeled data set. We validate the methodology through its application to categorization scenarios of image and text data sets, involving the creation, application, verification, and adjustment of classification models.

The number of images available has grown over the years, as well as the number of techniques to aid to organizing and retrieving from image collections. Techniques and systems have been proposed to recover images based on query, in which an image (or words) is used as input parameter and a list of similar images (or images with related text content) is recovered. However, understanding how the retrieved images are related to each other remains as a problem. This paper proposes an approach based on multidimensional visualization and coordination techniques to show the relationship from retrieved images. In this approach, coordination techniques are employed to perform image retrieval methods and highlight the results in visual representations, showing how retrieved images are relate. To evaluate our proposal image collections with and without textual annotations related to each image were used, and also image retrieval mechanisms based on distance, topic and semantic to retrieve images from distinct and multimodal datasets.

Exploratory tasks supported by visualization are usually improved by Coordinated and Multiple Views (CMV) of the data under study. Several coordination techniques have been proposed in the literature, resulting in a diversity of tools to generate mappings among the multiple views. These mappings can be highly dynamic, and their history reveals the settings employed in the multiple exploratory tasks conducted in a discovery process. Several solutions have been proposed to help users to recover the steps performed in exploratory tasks, but little support is found for registering the multiple coordination mappings employed. This paper provides a contribution in this direction, proposing a model for storing and recovering such mappings. We believe such a facility is an important feature of CMV systems, so that users can recover and rerun the coordinations performed when exploring their data. We present details of the proposed model and show some potential applications.

Abstract Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces for high dimensional data or to mapping data directly into 2D or 3D spaces. Although techniques have evolved to improve data segregation on reduced or visual spaces, they have limited capabilities for adjusting the results according to user's knowledge. In this paper, we propose a novel approach to handling both dimensionality reduction and visualization of high dimensional data, taking into account user's input. It employs Partial Least Squares (PLS), a statistical tool to perform retrieval of latent spaces focusing on the discriminability of the data. The method employs a training set for building a highly precise model that can then be applied to a much larger data set very effectively. The reduced data set can be exhibited using various existing visualization techniques. The training data is important to code user's knowledge into the loop. However, this work also devises a strategy for calculating PLS reduced spaces when no training data is available. The approach produces increasingly precise visual mappings as the user feeds back his or her knowledge and is capable of working with small and unbalanced training sets.

Abstract Word clouds have become one of the most widely accepted visual resources for document analysis and visualization, motivating the development of several methods for building layouts of keywords extracted from textual data. Existing methods are effective to demonstrate content, but are not capable of preserving semantic relationships among keywords while still linking the word cloud to the underlying document groups that generated them. Such representation is highly desirable for exploratory analysis of document collections. In this paper we present a novel approach to build document clouds, named ProjCloud that aim at solving both semantical layouts and linking with document sets. ProjCloud generates a semantically consistent layout from a set of documents. Through a multidimensional projection, it is possible to visualize the neighborhood relationship between highly related documents and their corresponding word clouds simultaneously. Additionally, we propose a new algorithm for building word clouds inside polygons, which employs spectral sorting to maintain the semantic relationship among words. The effectiveness and flexibility of our methodology is confirmed when comparisons are made to existing methods. The technique automatically constructs projection based layouts the user may choose to examine in the form of the point clouds or corresponding word clouds, allowing a high degree of control over the exploratory process.

Abstract Fiber tracts detection is an increasingly common technology for diagnosis and also understanding of brain function. Although tools for tracing and presenting brain fibers are advanced, it is still difficult for physicians or students to explore the dataset in 3D due to their intricate topology. In this work we present a visual exploration approach for fiber tracts data aimed at supporting exploration of such data. The work employs a local, precise and fast 2D multidimensional projection technique that allows a large number of fibers to be handled simultaneously and to select groups of bundled fibers for further exploration. In this approach, a DTI feature dataset, including curvature as well as spatial features, is projected on a 2D or 3D view. By handling groups formed in this view, exploration is linked to corresponding brain fibers in object space. The link exists in both directions and fibers selected in object space are also mapped to feature space. Our approach also allows users to modify the projection, controlling and improving, if necessary, the definition of groups of fibers for small and large datasets, due to the local nature of the projection. Compared to other related work, the method presented here is faster for creating visual representations, making it possible to explore complete sets of fibers tracts up to 250K fibers, which was not possible previously. Additionally, the ability to change configuration of the feature space representation adds a high degree of flexibility to the process.

Abstract Multidimensional projection has emerged as an important visualization tool in applications involving the visual analysis of high‐dimensional data. However, high precision projection methods are either computationally expensive or not flexible enough to enable feedback from user interaction into the projection process. A built‐in mechanism that dynamically adapts the projection based on direct user intervention would make the technique more useful for a larger range of applications and data sets. In this paper we propose the Piecewise Laplacian‐based Projection (PLP), a novel multidimensional projection technique, that, due to the local nature of its formulation, enables a versatile mechanism to interact with projected data and to allow interactive changes to alter the projection map dynamically, a capability unique of this technique. We exploit the flexibility provided by PLP in two interactive projection‐based applications, one designed to organize pictures visually and another to build music playlists. These applications illustrate the usefulness of PLP in handling high‐dimensional data in a flexible and highly visual way. We also compare PLP with the currently most promising projections in terms of precision and speed, showing that it performs very well also according to these quality criteria.

Abstract Visualization of high‐dimensional data requires a mapping to a visual space. Whenever the goal is to preserve similarity relations a frequent strategy is to use 2D projections, which afford intuitive interactive exploration, e.g., by users locating and selecting groups and gradually drilling down to individual objects. In this paper, we propose a framework for projecting high‐dimensional data to 3D visual spaces, based on a generalization of the Least‐Square Projection (LSP). We compare projections to 2D and 3D visual spaces both quantitatively and through a user study considering certain exploration tasks. The quantitative analysis confirms that 3D projections outperform 2D projections in terms of precision. The user study indicates that certain tasks can be more reliably and confidently answered with 3D projections. Nonetheless, as 3D projections are displayed on 2D screens, interaction is more difficult. Therefore, we incorporate suitable interaction functionalities into a framework that supports 3D transformations, predefined optimal 2D views, coordinated 2D and 3D views, and hierarchical 3D cluster definition and exploration. For visually encoding data clusters in a 3D setup, we employ color coding of projected data points as well as four types of surface renderings. A second user study evaluates the suitability of these visual encodings. Several examples illustrate the framework's applicability for both visual exploration of multidimensional abstract (non‐spatial) data as well as the feature space of multi‐variate spatial data.

An alternative form to multidimensional projections for the visual analysis of data represented in multidimensional spaces is the deployment of similarity trees, such as Neighbor Joining trees. They organize data objects on the visual plane emphasizing their levels of similarity with high capability of detecting and separating groups and subgroups of objects. Besides this similarity-based hierarchical data organization, some of their advantages include the ability to decrease point clutter; high precision; and a consistent view of the data set during focusing, offering a very intuitive way to view the general structure of the data set as well as to drill down to groups and subgroups of interest. Disadvantages of similarity trees based on neighbor joining strategies include their computational cost and the presence of virtual nodes that utilize too much of the visual space. This paper presents a highly improved version of the similarity tree technique. The improvements in the technique are given by two procedures. The first is a strategy that replaces virtual nodes by promoting real leaf nodes to their place, saving large portions of space in the display and maintaining the expressiveness and precision of the technique. The second improvement is an implementation that significantly accelerates the algorithm, impacting its use for larger data sets. We also illustrate the applicability of the technique in visual data mining, showing its advantages to support visual classification of data sets, with special attention to the case of image classification. We demonstrate the capabilities of the tree for analysis and iterative manipulation and employ those capabilities to support evolving to a satisfactory data organization and classification.

Document interpretation is a crucial task in many visual analytics applications, made harder by the widespread availability of freely available textual files. In this paper we propose an approach based on topic detection coupled with multiple coordinated views to assist analysis of time varying document collections. Given multiple document maps built from a set of text files, we define a strategy to support users locating the evolution of topics addressed by the documents, along various time steps. The approach is supported by a new algorithm for topic extraction from texts, also introduced. Finally, we show several examples illustrating how the proposed strategy may be applied in the analysis of document collections.

Time series analysis poses many challenges to professionals in a wide range of domains. Several visualization solutions have been proposed for exploratory tasks on time series collections. For large data sets, however, current techniques fail to provide a global view that supports a good association between groups of similar time series. We employ fast multidimensional projection techniques to create concise visual representations of a collection of time series. The whole collection can be viewed in a two-dimensional graph-based representation that provides a starting point for further exploration and detailed analysis. The projections employ distance metrics to compare the series and generate a layout that attempts to group those with similar behavior. We illustrate the approach on a real data set containing streamflows describing the behavior of hydroelectric power plants in Brazil.

Multiple Views have been put forward as an alternative to assist exploration of evolving phenomena or associations between distinct data sets or distinct presentations of a single data set. Coordinating between views is a challenge that must be met to improve visualization support for exploratory tasks. This is particularly true for high-dimensional data, such as document collections. We introduce a coordination framework for multiple views of document collections created using projections and point placement visualizations. Coordination can occur between different views of a single data set or between views of multiple data sets. Multiple coordinations are also admitted. Three new types of coordination are presented to illustrate the framework; these have been implemented in a multipurpose multi-dimensional visualization system called PEx (Projection Explorer).

The efficacy of search engines has expanded the uses for the information available on the Web. An increasing number of applications make use of the WWW as a primary source of information. The usefulness of such applications is, however, impaired by the current styles of display of the Web search results. This paper presents a system that adapts two techniques to map and explore Web results visually in order to find relevant patterns and relationships amongst the resulting documents. The first technique creates a visual map of the search results using a content-based multidimensional projection. The second techniques is capable of identifying, labeling and displaying topics within sub-groups of documents on the map. The system (The Projection explorer for the WWW, or PEx-Web) implements these techniques and various additional tools as means to make better use of Web search results for exploratory applications.

Point placement strategies aim at mapping data points represented in higher dimensions to bi-dimensional spaces and are frequently used to visualize relationships amongst data instances.They have been valuable tools for analysis and exploration of datasets of various kinds. Many conventional techniques, however, do not behave well when the number of dimensions is high, such as in the case of documents collections. Later approaches handle that shortcoming, but may cause too much clutter to allow flexible exploration to take place. In this work we present a novel hierarchical point placement technique that is capable of dealing with these problems. While good grouping and separation of data with high similarity is maintained without increasing computation cost,its hierarchical structure lends itself both to exploration in various levels of detail and to handling data in subsets, improving analysis capability and also allowing manipulation of larger data sets.

The problem of projecting multidimensional data into lower dimensions has been pursued by many researchers due to its potential application to data analysis of various kinds. This paper presents a novel multidimensional projection technique based on least square approximations. The approximations compute the coordinates of a set of projected points based on the coordinates of a reduced number of control points with defined geometry. We name the technique Least Square Projections (LSP). From an initial projection of the control points, LSP defines the positioning of their neighboring points through a numerical solution that aims at preserving a similarity relationship between the points given by a metric in mD. In order to perform the projection, a small number of distance calculations is necessary and no repositioning of the points is required to obtain a final solution with satisfactory precision. The results show the capability of the technique to form groups of points by degree of similarity in 2D. We illustrate that capability through its application to mapping collections of textual documents from varied sources, a strategic yet difficult application. LSP is faster and more accurate than other existing high quality methods, particularly where it was mostly tested, that is, for mapping text sets.

This paper presents a tool, called text map explorer, which can be used to create and explore document maps (visual representations of document collections). This tool is capable of grouping (and separating) documents by their contents, revealing to the user relationships amongst them. This paper also presents a novel multi-dimensional projection technique for text that reduces the quadratic time complexity of our previous approach to O(N3/2), keeping the same quality of maps. The technique creates a surface that reveals intrinsic patterns and supports various kinds of exploration of a text collection

This paper introduces Least Square Projection (LSP), a fast technique for projection of multi-dimensional data onto lower dimensions developed and tested successfully in the context of creation of text maps based on their content. Current solutions are either based on computationally expensive dimension reduction with no proper guarantee of the outcome or on faster techniques that need some sort of post-processing for recovering information lost during the process. LSP is based on least square approximation, a technique originally employed for surface modeling and reconstruction. Least square approximations are capable of computing the coordinates of a set of projected points based on a reduced number of control points with defined geometry. We extend the concept for general data sets. In order to perform the projection, a small number of distance calculations is necessary and no repositioning of the final points is required to obtain a satisfactory precision of the final solution. Textual information is a typically difficult data type to handle, due to its intrinsic dimensionality. We employ document corpora as a benchmark to demonstrate the capabilities of the LSP to group and separate documents by their content with high precision.

A map of multi-dimensional data is a graphical representation - defined in 2D or 3D space - of a set of data points that reflects similarity relationships amongst them. Triangulations of those points can be produced to generate surface meshes on which additional information can be mapped to visual attributes such as color or height. Such surfaces may then be used to explore the data set and the similarities between the different data points. In this paper we introduce Voromap, an exploration tool that is based on Voronoi diagrams of the projected data, and compare that representation with a standard representation that uses triangulations. Voronoi diagrams represent each element in the data set by the area of a polygon in which every point within the polygon is closer to its generating point than to any other point from the data set. A user experiment was conducted to compare Voronoi maps with equivalent maps represented by surface triangulations explored with a similar triangulation-based tool.

A set of user evaluation studies were performed to test hypotheses about the efficacy and value of the use of sonification by itself and together with visualization to improve the process of interactive data exploration. Our results show that, in some cases, sonification yielded more accurate analysis results than visualization, and when combined with visualization, yield much better results than either one (visualization or sonification) yielded separately. That supports sonification as a valuable and promising tool that should be further studied as a complement to visualization systems.

Lowering computational cost of data analysis and visualization techniques is an essential step towards including the user in the visualization. In this paper we present an improved algorithm for visual clustering of large multi-dimensional data sets. The original algorithm is an approach that deals efficiently with multi-dimensionality using various projections of the data in order to perform multi-space clustering, pruning outliers through direct user interaction. The algorithm presented here, named HC-Enhanced (for human-computer enhanced), adds a scalability level to the approach without reducing clustering quality. Additionally, an algorithm to improve clusters is added to the approach. A number of test cases is presented with good results.

Despite the significant evolution of techniques for 3D-reconstruction from planar cross sections, establishing the correspondence of regions in adjacent slices remains an important issue. In this article, we propose a novel approach for solving the correspondence problem in a flexible manner. We show that from the 3D Delaunay triangulation, it is possible to derive a distance measure among regions lying in adjacent slices. Such distance is used to define a positive integer parameter, called β, responsible for establishing the connections. Varying β thus allows the construction of different models from a given set of cross-sectional regions: small values of β causes closer regions to be connected into a single component, and as β increases, more distant regions are connected together. The algorithm, named β-connection, is described, and examples are provided that illustrate its applicability in solid modeling and model reconstruction from real data. The underlying reconstruction method is effective, which jointly with the β-connection correspondence strategy, improve the usability of volumetric reconstruction techniques considerably.

This paper introduces the concept of digital planar surfaces and corresponding Morse operators. These operators offer a novel and powerful method for construction and de-construction of such surfaces in a way that global topological control of the resulting object is always maintained. In that respect, this paper offers a complete pixel characterization tool. Image handling is a natural application for such approach. We present a novel fast algorithm for image segmentation using Morse operators for digital planar surfaces. It classifies as a region growing technique with added topological control and is extremely useful for applications that need proper object description. Results from real data are stimulating, and show that the segmentation algorithm compares very well with other methods. The topological approach also forms a base for future expansion to applications such as volume segmentation.

This paper presents a novel algorithm for volumetric reconstruction of objects from planar sections using Delaunay triangulation, which solves the main problems posed to models defined by reconstruction, particularly from the viewpoint of producing meshes that are suitable for interaction and simulation tasks. The requirements for these applications are discussed here and the results of the method are presented. Additionally, it is compared to another commonly used reconstruction algorithm based on Delaunay triangulation, showing the advantages of the reconstructions obtained by our technique.

Visualisation in the field of dentistry has not, thus far, reached the same development as other medical fields. Potential applications of visualisation techniques in this area, however, are many, ranging from educational displays to training for delicate procedures. This paper reports on the investigation of techniques for handling 3D models of teeth, aiming at the investigation of dental structures. An algorithm was implemented for this purpose, which reconstructs 3D teeth models from 2D contour slices. Results employing various data sets are presented, including the output of VRML models for exploration.

This paper presents an analysis of progress in the use of sound as a tool in support of visualisation and gives an insight into its development and future needs. Special emphasis is given to the use of sound in scientific and engineering applications. A system developed to support surface data presentation and interaction by using sound is presented and discussed.