Recent developments in sensor technology have enabled real-time data acquisition, high-frequency and multimodal data capturing thus underlying the need for monitoring physical or operational conditions in various aspe...
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Decision Trees (DTs) are one of the most widely used supervised Machine Learning algorithms. The algorithm constructs binary tree data structures that partition the data into smaller segments according to different ru...
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ISBN:
(纸本)9798350341614
Decision Trees (DTs) are one of the most widely used supervised Machine Learning algorithms. The algorithm constructs binary tree data structures that partition the data into smaller segments according to different rules. Hence, DTs can be used as a learning process of finding the optimal rules to separate and classify all items of a dataset. Since the algorithm relies on a decision process similar to rule-based decisions, they are easily interpretable. However, DTs can be difficult to analyse when dealing with large datasets and/or with multiple trees, i.e. ensembles. To ease the analysis and validation of these models, we developed a visual tool which includes a set of visualizations that overview and give details of a set of trees. Our tool aims to provide different perspectives over the same data and provide further insights on how decisions are being made. In this article, we overview our design process, present the different visualization models and their iterative validation. We present a use case in the telecommunications domain. In concrete, we use the visual tool to help understand how a model based on DTs decides which is the best channel (i.e., phonecall, e-mail, SMS) to contact a client.
With the vigorous development of big data technology, the application of data mining program to complete quantitative prediction and evaluation of mineral resources has become a new trend in the development of mineral...
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Studying spatio-temporal data is essential to understand the processes of the real world. However, the design of effective visualizations to explore spatio-temporal data is not a straightforward task due to the inhere...
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ISBN:
(纸本)9783031422928;9783031422935
Studying spatio-temporal data is essential to understand the processes of the real world. However, the design of effective visualizations to explore spatio-temporal data is not a straightforward task due to the inherent multidimensional aspects of the data. In this paper, we explore the usage of polymorphous glyphs (i.e. glyphs that change shapes according to the context) to support the exploration of multiple hierarchical levels and dimensions of the data. We implemented our approach in the form of a web-based visualization interface that we demonstrate through a case study of the ISSA KG, which describes scientific publications in the field of agriculture, thus supporting domain experts on investigating where, when and how different crops are cultivated.
The paper presents a recommender algorithm for visualanalysis based on data field Schema and Aggregation, and developed an automated dataanalysis solution recommendation system (AutoEDA) in conjunction with the Expl...
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data-centric NLP is a highly iterative process requiring careful exploration of text data throughout entire model development life-cycle. Unfortunately, existing dataexploration tools are not suitable to support data...
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ISBN:
(纸本)9781450394161
data-centric NLP is a highly iterative process requiring careful exploration of text data throughout entire model development life-cycle. Unfortunately, existing dataexploration tools are not suitable to support data-centric NLP because of workfow discontinuity and lack of support for unstructured text. In response, we propose Weedle, a seamless and customizable exploratory text analysis system for data-centric NLP. Weedle is equipped with built-in text transformation operations and a suite of visualanalysis features. With its widget, users can compose customizable dashboards interactively and programmatically in computational notebooks.
This paper investigates the application of mobile robotic platforms for visualdata capture in infrastructure inspection tasks. The captured data offer significant value for both manual and automated inspection proces...
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ISBN:
(纸本)9798331516246;9798331516239
This paper investigates the application of mobile robotic platforms for visualdata capture in infrastructure inspection tasks. The captured data offer significant value for both manual and automated inspection processes. It can produce detailed visual information for human inspectors and serve as input for automated systems to detect anomalies or assist inspectors through computer-aided analysis. Additionally, these data can be integrated into the robot navigation system for real-time path optimisation. A critical challenge in optimising data capture is highlighted: balancing the desired precision with the time invested in inspections. The study explores this tradeoff by analysing the impact of motion blur on measurement errors. Capturing high-quality images with minimal motion blur necessitates slower inspection speeds. The findings suggest that for extensive inspection areas, prioritising mid-range object distances can optimise data capture, as errors increase at a slower pace at these distances compared to closer or farther ranges. This research paves the way for further advancements. Future areas of exploration include evaluating noise reduction techniques, incorporating real-world complexities into testing environments, and investigating the impact of capture speed on machine learning algorithms.
Knowledge of emerging and declining trends and their potential future course is highly relevant in many application domains, particularly in corporate strategy and foresight. The early awareness of trends allows react...
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ISBN:
(纸本)9798350341614
Knowledge of emerging and declining trends and their potential future course is highly relevant in many application domains, particularly in corporate strategy and foresight. The early awareness of trends allows reacting to market, political, and societal changes and challenges at an appropriate time. In our previous works, we presented approaches for the early identification and analysis of emerging trends. Although our previous approaches are detecting emerging trends appropriately, they lack the ability to predict the potential future course of a trend or technology. We present in this work a novel visual Analytics approach for forecasting emerging trends that combines interactive visualizations with machine learning techniques and statistical approaches to detect, analyze, and predict trends from textual data. We extend our previous work on analyzing technological trends from text and propose an advanced approach that includes forecasting through hybrid techniques consisting of neural networks and established statistical methods. Our approach offers insights from enormous data sets and the potential future course of trends based on their occurrence in textual data. We contribute with a novel approach for identifying and forecasting trends, a hybrid forecasting method to predict trends from text, and interactive visualization techniques on macro level, micro level, and monitoring topics of interest.
Line charts are fundamental to dataanalysis and exploration, offering concise visual representations of trends. However, gaining access to the underlying data used to construct these charts is often challenging. In t...
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Seabirds have varied movement patterns depending on their group, nesting site, or season. Therefore, seabird experts need to compare movement data under various conditions for conservation and elucidation of ecology. ...
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ISBN:
(纸本)9781665490078
Seabirds have varied movement patterns depending on their group, nesting site, or season. Therefore, seabird experts need to compare movement data under various conditions for conservation and elucidation of ecology. visualization of movement data helps intuitive analysis. However, it is not easy for seabird experts to design suitable visual representations for comparison. The purpose of our study is to develop a tool to support the visualization process for comparison of seabird movement. There are three components to visualization techniques for comparison: Juxtaposition, Superposition, and Explicit Encodings. In our study, we aim to support comparative analysis under complex conditions using flexible Small Multiples based on juxtaposition and superposition. We designed visual representations for the comparative analysis of seabird movement data. We implemented such visual representations into a tool and confirmed its effectiveness for comparative analysis of seabird movement data.
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