data visualizations make data easier for the human brain to understand, and visualization also makes it easier to detect patterns, trends, and styles in groups of data. With the development of the data visualization, ...
详细信息
The growth of smart city applications is increasingly around the world, many cities invest in the development of these systems intending to improve the management and life of their residents. This increase is mainly d...
详细信息
The growth of smart city applications is increasingly around the world, many cities invest in the development of these systems intending to improve the management and life of their residents. This increase is mainly due to the emergence of new technologies such as Big data and Internet of Things (IoT). Some of the biggest challenges in applying these systems, relate to the processing, visualization, and analysis of the generated data, since most systems tend to work connected, thus generating a large mass of data that deviates from the standard of previously used systems. For data visualization, one of the main devices used is the reduction of dimensionality, in an attempt to bring data from one dimension N to two or three dimensions and thus be noticeable to human eyes. There are several algorithms used for dimensionality reduction, the linear ones that as the name implies, solve linearly separable problems and so these are very limited and the nonlinear ones, that solve more complex problems, but usually have an excessive runtime, making them or often inappropriate to apply. This article presents the parallel implementation of the nonlinear dimension reduction algorithm t-Distributed Stochastic Neighbor Embedding (t-SNE), showing better results than its conventional version in terms of runtime, thus showing that parallelism can make the problem of dimensionality reduction treatable, bringing greater scalability and delivering results within an acceptable time frame.
This study contributes to the research on Internet of Things data visualization for business intelligence processes, an area of growing interest to scholars, by conducting a systematic review of the literature. A tota...
详细信息
This study contributes to the research on Internet of Things data visualization for business intelligence processes, an area of growing interest to scholars, by conducting a systematic review of the literature. A total of 237 articles published over the past 11 years were obtained and compared. This made it possible to identify the top contributing and most influential authors, countries, publishers, institutions, papers, and research findings, together with the challenges facing current research. Based on these results, this work provides a thorough insight into the field by proposing four research categories (Technology infrastructure, Case examples, Final-user experience, and Big data tools), together with the development of these research streams over time and their future research directions.
visualization is the representation of information in the form of various charts or images. data visualization is used to identify useful patterns, to understand trends, and to find out outliers in dataset. data visua...
详细信息
In order to improve the user experience in the process of analyzing data information, and improve the intuitiveness of data information, this paper proposes a construction strategy of data visualization system based o...
详细信息
News articles containing data visualizations play an important role in informing the public on issues ranging from public health to politics. Recent research on the persuasive appeal of data visualizations suggests th...
详细信息
News articles containing data visualizations play an important role in informing the public on issues ranging from public health to politics. Recent research on the persuasive appeal of data visualizations suggests that prior attitudes can be notoriously difficult to change. Inspired by an NYT article, we designed two experiments to evaluate the impact of elicitation and contrasting narratives on attitude change, recall, and engagement. We hypothesized that eliciting prior beliefs leads to more elaborative thinking that ultimately results in higher attitude change, better recall, and engagement. Our findings revealed that visual elicitation leads to higher engagement in terms of feelings of surprise. While there is an overall attitude change across all experiment conditions, we did not observe a significant effect of belief elicitation on attitude change. With regard to recall error, while participants in the draw trend elicitation exhibited significantly lower recall error than participants in the categorize trend condition, we found no significant difference in recall error when comparing elicitation conditions to no elicitation. In a follow-up study, we added contrasting narratives with the purpose of making the main visualization (communicating data on the focal issue) appear strikingly different. Compared to the results of Study 1, we found that contrasting narratives improved engagement in terms of surprise and interest but interestingly resulted in higher recall error and no significant change in attitude. We discuss the effects of elicitation and contrasting narratives in the context of topic involvement and the strengths of temporal trends encoded in the data visualization.
作者：
Kaneko, HiromasaMeiji Univ
Sch Sci & Technol Dept Appl Chem Tama Ku 1-1-1 Higashi Mita Kawasaki Kanagawa 2148571 Japan
It can be very difficult to automatically determine the hyperparameter values of nonlinear data visualization methods. In this study, a new measure called the k-nearest neighbor normalized error for visualization and ...
详细信息
It can be very difficult to automatically determine the hyperparameter values of nonlinear data visualization methods. In this study, a new measure called the k-nearest neighbor normalized error for visualization and reconstruction (k3n-error) is developed to compare the visualization performance and automatically optimize the hyperparameters of nonlinear visualization methods using only unsupervised data. For a given sample, the k3n-error approach is based on the standardized errors between the Euclidean distances to neighboring samples before and after projection onto the latent space. Case studies are conducted using two numerical simulation datasets and four quantitative structure-activity/property relationship datasets. The results confirm that, for each nonlinear visualization method, samples can be mapped to the two-dimensional space while maintaining their proximity relationship from the original space by selecting the hyperparameters using the proposed k3n-error.
Leaving the context of visualizations invisible can have negative impacts on understanding and transparency. While common wisdom suggests that recontextualizing visualizations with metadata (e.g., disclosing the data ...
详细信息
Leaving the context of visualizations invisible can have negative impacts on understanding and transparency. While common wisdom suggests that recontextualizing visualizations with metadata (e.g., disclosing the data source or instructions for decoding the visualizations' encoding) may counter these effects, the impact remains largely unknown. To fill this gap, we conducted two experiments. In Experiment 1, we explored how chart type, topic, and user goal impacted which categories of metadata participants deemed most relevant. We presented 64 participants with four real-world visualizations. For each visualization, participants were given four goals and selected the type of metadata they most wanted from a set of 18 types. Our results indicated that participants were most interested in metadata which explained the visualization's encoding for goals related to understanding and metadata about the source of the data for assessing trustworthiness. In Experiment 2, we explored how these two types of metadata impact transparency, trustworthiness and persuasiveness, information relevance, and understanding. We asked 144 participants to explain the main message of two pairs of visualizations (one with metadata and one without);rate them on scales of transparency and relevance;and then predict the likelihood that they were selected for a presentation to policymakers. Our results suggested that visualizations with metadata were perceived as more thorough than those without metadata, but similarly relevant, accurate, clear, and complete. Additionally, we found that metadata did not impact the accuracy of the information extracted from visualizations, but may have influenced which information participants remembered as important or interesting.
Sensors can capture very sensitive and valuable information without human intervention and send it to remote location. However, capturing sensory data from a Body Sensor Network (BSN) and sending it to social networks...
详细信息
This article elaborates on the use of data visualization to promote a more informed and engaged participation in civic and democratic life. First, it outlines the main constraints and challenges in electronic particip...
详细信息
暂无评论