Visualising Business Data: A Survey

A rapidly increasing number of businesses rely on visualisation solutions for their data management challenges. This demand stems from an industry-wide shift towards data-driven approaches to decision making and problem-solving. However, there is an overwhelming mass of heterogeneous data collected as a result. The analysis of these data become a critical and challenging part of the business process. Employing visual analysis increases data comprehension thus enabling a wider range of users to interpret the underlying behaviour, as opposed to skilled but expensive data analysts. Widening the reach to an audience with a broader range of backgrounds creates new opportunities for decision making, problem-solving, trend identification, and creative thinking. In this survey, we identify trends in business visualisation and visual analytic literature where visualisation is used to address data challenges and identify areas in which industries use visual design to develop their understanding of the business environment. Our novel classification of literature includes the topics of businesses intelligence, business ecosystem, customer-centric. This survey provides a valuable overview and insight into the business visualisation literature with a novel classification that highlights both mature and less developed research directions

Visual analysis design to support research into movement and use of space in Tallinn: A case study

We designed and applied interactive visualisation to help an urban study group investigate how suburban residents in the Tallinn Metropolitan Area (Estonia) use space in the city. We used mobile phone positioning data collected from suburban residents together with their socio-economic characteristics. Land-use data provided geo-context that helped characterise visited locations by suburban residents. Our interactive visualisation design was informed by a set of research questions framed as identification, localisation and comparison tasks. The resulting prototype offers five linked and coordinated views of spatial, temporal, socio-economic characteristics and land-use aspects of data. Brushing, sorting and filtering provide visual means to identify similarities between individuals and facilitate the identification, localisation and comparison of patterns of use of urban space. The urban study group was able to use the prototype to explore their data and address their research questions in a more flexible way than previously possible. Initial feedback was positive. The prototype was found to support the research and facilitate the discovery of patterns and relations among groups of participants and their movements

Digital 3D Technologies for Humanities Research and Education: An Overview

Digital 3D modelling and visualization technologies have been widely applied to support research in the humanities since the 1980s. Since technological backgrounds, project opportunities, and methodological considerations for application are widely discussed in the literature, one of the next tasks is to validate these techniques within a wider scientific community and establish them in the culture of academic disciplines. This article resulted from a postdoctoral thesis and is intended to provide a comprehensive overview on the use of digital 3D technologies in the humanities with regards to (1) scenarios, user communities, and epistemic challenges; (2) technologies, UX design, and workflows; and (3) framework conditions as legislation, infrastructures, and teaching programs. Although the results are of relevance for 3D modelling in all humanities disciplines, the focus of our studies is on modelling of past architectural and cultural landscape objects via interpretative 3D reconstruction methods

Accelerating in-transit co-processing for scientific simulations using region-based data-driven analysis

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly

Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey

The emergence of natural language processing has revolutionized the way users interact with tabular data, enabling a shift from traditional query languages and manual plotting to more intuitive, language-based interfaces. The rise of large language models (LLMs) such as ChatGPT and its successors has further advanced this field, opening new avenues for natural language processing techniques. This survey presents a comprehensive overview of natural language interfaces for tabular data querying and visualization, which allow users to interact with data using natural language queries. We introduce the fundamental concepts and techniques underlying these interfaces with a particular emphasis on semantic parsing, the key technology facilitating the translation from natural language to SQL queries or data visualization commands. We then delve into the recent advancements in Text-to-SQL and Text-to-Vis problems from the perspectives of datasets, methodologies, metrics, and system designs. This includes a deep dive into the influence of LLMs, highlighting their strengths, limitations, and potential for future improvements. Through this survey, we aim to provide a roadmap for researchers and practitioners interested in developing and applying natural language interfaces for data interaction in the era of large language models.Comment: 20 pages, 4 figures, 5 tables. Submitted to IEEE TKD

Creating sparks: comparing search results using discriminatory search term word co-occurrence to facilitate serendipity in the enterprise.

Categories or tags that appear in faceted search interfaces which are representative of an information item, rarely convey unexpected or non-obvious associated concepts buried within search results. No prior research has been identified which assesses the usefulness of discriminative search term word co-occurrence to generate facets to act as catalysts to facilitate insightful and serendipitous encounters during exploratory search. In this study, 53 scientists from two organisations interacted with semi-interactive stimuli, 74% expressing a large/moderate desire to use such techniques within their workplace. Preferences were shown for certain algorithms and colour coding. Insightful and serendipitous encounters were identified. These techniques appear to offer a significant improvement over existing approaches used within the study organisations, providing further evidence that insightful and serendipitous encounters can be facilitated in the search user interface. This research has implications for organisational learning, knowledge discovery and exploratory search interface design

Deep-Learning-Based Computer- Aided Systems for Breast Cancer Imaging: A Critical Review

[EN] This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010-January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. 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Neural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review

Hyperspectral imaging (HSI) has become widely used in cultural heritage (CH). This very efficient method for artwork analysis is connected with the generation of large amounts of spectral data. The effective processing of such heavy spectral datasets remains an active research area. Along with the firmly established statistical and multivariate analysis methods, neural networks (NNs) represent a promising alternative in the field of CH. Over the last five years, the application of NNs for pigment identification and classification based on HSI datasets has drastically expanded due to the flexibility of the types of data they can process, and their superior ability to extract structures contained in the raw spectral data. This review provides an exhaustive analysis of the literature related to NNs applied for HSI data in the CH field. We outline the existing data processing workflows and propose a comprehensive comparison of the applications and limitations of the various input dataset preparation methods and NN architectures. By leveraging NN strategies in CH, the paper contributes to a wider and more systematic application of this novel data analysis method

Evaluation of storytelling in information visualization (MPhil to PhD Transfer Report)

Story telling has been used throughout the ages as a means of communication between people and to convey and transmit knowledge from one person to another, and from one generation to the next. In various domains, formulating of messages, ideas, or findings into a story has proven its efficiency in making them understandable, comprehensible, memorable, interesting, and engaging. Information Visualization as an academic field has also utilised the power of storytelling to make visualizations more understandable and interesting for a variety of audiences, including experts. However, although storytelling has been a hot topic in information visualization for some time, little or no empirical evaluations exist to compare different approaches of storytelling through information visualization. There is also a need for work that addresses in depth some particular criteria and techniques of storytelling such as transitions types in visual stories in general and data-driven stories in particular. A within subject experiment with 13 participants has been conducted to explore empirically how two different models of story delivery with information visualization influence narratives/stories constructed by audiences. Specifically, the first model involves direct narrative by a speaker using a visualization design to tell a story, while the second model involves constructing a story by interactively exploring visualization software. An openended questionnaire in controlled laboratory settings has been used in which the primary goal was to collect a number of stories derived from the two models. All the stories written by the participants were transcribed, analysed, and coded, using data-driven and preset themes. Themes included initial perception of the main story pattern/topic, insight types derived, narrative structures, and unexpected type of insights gained. This experiment was followed by a semi-structured interview where each participant answered two Likert-scale questions on each delivery model, and commented on the overall experiment. It is found that although most participants found telling a story easier with the first model (narrative) they did not perform better in other aspects. The second model (software) was advantegeous in the variety of insight types gained and participants accepted the message and information more neutrally. In contrast, participants were more critical about the data in software model than in the narrative model. The role of time in structuring story events was more apparent in the software model. These findings have some significant practical implications on storytelling through information visualization. A statement of the work done and a work plan for the remaining period of the PhD is also included explaining the proposed enhancement to the experiment conducted and further research work planned to address the issue of transitions in storytelling visualization