Web-based Visualization System Research Articles

3D imaging application to the study of the early Neolithic ceramic complex: the decorated pottery of Rio Tana (Abruzzi, Italy)

This study aims to demonstrate the potential of 3D analysis in the study of prehistoric ceramic complexes. Due to the production systems used, prehistoric pottery decorations can vary significantly. Examining the frequency of various decorative methods used at different sites may help identify areas potentially connected to specific traditional elements. However, this is usually a cumbersome and time-consuming task. 3D digital methodologies can bring several advantages in the interpretation of ceramic decoration, providing new tools that can improve analytical skills, speed up the decision process, and support the researcher’s conclusions. This work focuses on the application of these methodologies to the study of decorated ceramics from the Neolithic site of Rio Tana (Abruzzo, central Italy): firstly, a pottery complex from this site has been 3D digitised; then, an interactive and web-based 3D visualisation system has been developed to enable access and study of these digital data. By comparing archaeological data with experimental data, in parallel with the analysis of 3D models’ surfaces, it has been possible to better define the technique employed and quickly identify the type of tool used to create the ceramic decorations. Furthermore, the web-based system made it possible to share and disseminate the digital dataset with the community, creating the conditions to make it easier to compare the frequency of different decoration methods and tools used at Rio Tana with other sites.

Visualization system to identify structurally vulnerable links in OHT railway network in semiconductor FAB using betweenness centrality.

In semiconductor fabrication (FAB), wafers are placed into carriers known as Front Opening Unified Pods (FOUPs), transported by the Overhead Hoist Transport (OHT). The OHT, a type of Automated Guided Vehicle (AGV), moves along a fixed railway network in the FAB. The routes of OHTs on the railway network are typically determined by a Single Source Shortest Path (SSSP) algorithm such as Dijkstra's. However, the presence of hundreds of operating OHTs often leads to path interruptions, causing congestion or deadlocks that ultimately diminish the overall productivity of the FAB. This research focused on identifying structurally vulnerable links within the OHT railway network in semiconductor FAB and developing a visualization system for enhanced on-site decision-making. We employed betweenness centrality as a quantitative index to evaluate the structural vulnerability of the OHT railway network. Also, to accommodate the unique hierarchical node-port structure of this network, we modified the traditional Brandes algorithm, a widely-used method for calculating betweenness centrality. Our modification of the Brandes algorithm integrated node-port characteristics without increasing computation time while incorporating parallelization to reduce computation time further and improve usability. Ultimately, we developed an end-to-end web-based visualization system that enables users to perform betweenness centrality calculations on specific OHT railway layouts using our algorithm and view the results through a web interface. We validated our approach by comparing our results with historically vulnerable links provided by Samsung Electronics. The study had two main outcomes: the development of a new betweenness centrality calculation algorithm considering the node-port structure and the creation of a visualization system. The study demonstrated that the node-port structure betweenness centrality effectively identified vulnerable links in the OHT railway network. Presenting these findings through a visualization system greatly enhanced their practical applicability and relevance.

RANCANG BANGUN SISTEM INFORMASI PENGELOLAAN WISATA PULAU RANOH BERBASIS WEBSITE

The purpose of making the Design and Development of a Website-Based Ranoh Island Tourism Management Information System is to promote and facilitate booking of tour packages on Ranoh Island. Web-based visual information system is a system consisting of text and graphic images. It is these factors that make these tours interesting and make bookings easy. In addition, the Website-Based Ranoh Island Tourism Management Information System is a system that contains complete information for tourists and can be found at any time. This support cannot be separated from the research process design and technology. This study aims to design a Website-Based Ranoh Island Tourism Management Information System as a source of information for viewing and ordering tourist attractions and other facilities on Ranoh Island. The Website-Based Ranoh Island Tourism Management Information System was developed using the PHP programming language and data stored in a database using MySQL to optimize system and database performance. The Google Maps API is one of the features of this information system that can be used to add maps to websites using JavaScript. The result of the design is a web tourism information system which should be a way to promote tourism in the city of Batam, so that through this web design it is hoped that there will be a similar system to create better services. and more complete than existing services.

LegalVis: Exploring and Inferring Precedent Citations in Legal Documents.

To reduce the number of pending cases and conflicting rulings in the Brazilian Judiciary, the National Congress amended the Constitution, allowing the Brazilian Supreme Court (STF) to create binding precedents (BPs), i.e., a set of understandings that both Executive and lower Judiciary branches must follow. The STF's justices frequently cite the 58 existing BPs in their decisions, and it is of primary relevance that judicial experts could identify and analyze such citations. To assist in this problem, we propose LegalVis, a web-based visual analytics system designed to support the analysis of legal documents that cite or could potentially cite a BP. We model the problem of identifying potential citations (i.e., non-explicit) as a classification problem. However, a simple score is not enough to explain the results; that is why we use an interpretability machine learning method to explain the reason behind each identified citation. For a compelling visual exploration of documents and BPs, LegalVis comprises three interactive visual components: the first presents an overview of the data showing temporal patterns, the second allows filtering and grouping relevant documents by topic, and the last one shows a document's text aiming to interpret the model's output by pointing out which paragraphs are likely to mention the BP, even if not explicitly specified. We evaluated our identification model and obtained an accuracy of 96%; we also made a quantitative and qualitative analysis of the results. The usefulness and effectiveness of LegalVis were evaluated through two usage scenarios and feedback from six domain experts.

Study and Development of IOT based Smart Irrigation System using Soil Moisture and Weather Prediction

Abstract: The smart irrigation system was developed to optimize water use of crops. The system has a distributed wireless network of soil-moisture temperature sensors placed in the root zone of the plants. Also, a gateway unit handles sensor information, triggers, actuators, and transmits data to the web application. The intelligence of The proposed system is based on a smart algorithm, which considers sensed data along with the weather forecast parameters like precipitation, air temperature, humidity, and UV for the near future. The complete system has been developed and deployed on a pilot scale, where the sensor node data is wirelessly collected over the cloud using web- services and a web-based information visualization and decision support system provides real-time information insights based on the analysis of sensor dataand weather forecast data. The paper describes the system and discusses in detail the information processing results of three weeks data based on the proposed algorithm. The system is fully functional and the prediction results are very encouraging

Smart Irrigation and Crop Health Prediction

The scarcity of clean water resources around the globe has generated a need for their optimum utilization Internet of Things (IoT) solutions, based on the application specific sensors’ data acquisition and intelligent processing are bridging the gaps between the cyber and physical world. IoT based smart irrigation systems can help in achieving optimum water-resource utilization in the precision farming landscape. This paper presents an open-source technology based smart system to predict the irrigation requirements of a field using the sensing of ground parameter like soil moisture soil temperature, and environmental conditions along with the weather forecast data from the Internet. The intelligence of the proposed system is based on a smart algorithm, which considers sensed data along with the weather forecast parameters like precipitation, air temperature, humidity, and UV for the near future. The complete system has been developed and deployed on a pilot scale, where the sensor node data is wirelessly collected over the cloud using web-services and a web-based information visualization and decision support system provides the real-time information-insights based on the analysis of sensors data and weather forecast data. The paper describes the system and discusses in detail the information processing results of three weeks data based on the proposed algorithm. The system is fully functional and the prediction results are very encouraging.

A comprehensive and up-to-date web-based interactive 3D emergency response and visualization system using Cesium Digital Earth: taking landslide disaster as an example

ABSTRACT As with the fast advances in the technologies of big Earth data and information communication, Web-based 3D GIS system has come a long way from a few years ago. These advances reflect in many aspects of 3D GIS such as higher real-time performance, enhanced interactivity, more realistic 3D visualization effect and improved user interface. This paper aims to present a comprehensive and up-to-date 3D Web GIS for Emergency Response using the current vue.js web application framework and the well-known Cesium API, taking landslide disaster as an example. Building upon recent advances in WebGL technology, we developed a suite of enhanced 3D spatial analysis functions, including interactive route planning, instant text/image/video messaging being incorporated into both 3D WebGL page and mobile GIS applications, and progressive 3D construction and AR visualization using LiDAR and camera over local emergency network or internet. Moreover, professional functions such as landslide susceptibility mapping, landslide monitoring, spatial temporal contingency plan management, landslide information management, personnel and equipment management, and communication are all implemented and integrated in the 3D GIS system. Most of the functions of the system are implemented using open-source projects, which is beneficial to the development of the 3D GIS research community.

Visual Analysis of Multidimensional Big Data: A Scalable Lightweight Bundling Method for Parallel Coordinates

Varied edge bundling methods have been used to reduce visual clutter in parallel coordinates plots (PCP). However, existing edge-bundled PCP do not scale well for visual analysis of multidimensional big data and often overplot the bundles in the area near the axes. In this study, we propose a scalable lightweight bundling method to support visual analysis of multidimensional big data in PCP. It helps the users discover trends and detect outliers in the data by bundling the edges between each two adjacent axes independently. We integrate human judgments into the two-dimensional data binning by novel interactions to accelerate the clustering process of the data. We use the frequency-based representation to render the clusters as histogram-like bundles to reveal the distribution of the data and eliminate the overplotting of the bundles. Based on our method, we build a lightweight web-based visual analytics system for exploring multidimensional big data in PCP. The scalability analysis of our method shows that its clustering time increases linearly with the size of the data. Its rendering time is independent of the size of the data. It can cluster and visualize 1 million data records with 6 dimensions in about 1 second in web-based visualization without pre-computation of the data or hardware-accelerated rendering. We conduct two case studies and a user study to compare our method with classic PCP and two state-of-the-art edge-bundled PCP. The results show that our method is more efficient and effective for visually analyzing multidimensional big data.

Interactive Web-Based Visual Analysis on Network Traffic Data

Network traffic data analysis is important for securing our computing environment and data. However, analyzing network traffic data requires tremendous effort because of the complexity of continuously changing network traffic patterns. To assist the user in better understanding and analyzing the network traffic data, an interactive web-based visualization system is designed using multiple coordinated views, supporting a rich set of user interactions. For advancing the capability of analyzing network traffic data, feature extraction is considered along with uncertainty quantification to help the user make precise analyses. The system allows the user to perform a continuous visual analysis by requesting incrementally new subsets of data with updated visual representation. Case studies have been performed to determine the effectiveness of the system. The results from the case studies support that the system is well designed to understand network traffic data by identifying abnormal network traffic patterns.

MonaGO: a novel gene ontology enrichment analysis visualisation system

BackgroundGene ontology (GO) enrichment analysis is frequently undertaken during exploration of various -omics data sets. Despite the wide array of tools available to biologists to perform this analysis, meaningful visualisation of the overrepresented GO in a manner which is easy to interpret is still lacking.ResultsMonash Gene Ontology (MonaGO) is a novel web-based visualisation system that provides an intuitive, interactive and responsive interface for performing GO enrichment analysis and visualising the results. MonaGO supports gene lists as well as GO terms as inputs. Visualisation results can be exported as high-resolution images or restored in new sessions, allowing reproducibility of the analysis. An extensive comparison between MonaGO and 11 state-of-the-art GO enrichment visualisation tools based on 9 features revealed that MonaGO is a unique platform that simultaneously allows interactive visualisation within one single output page, directly accessible through a web browser with customisable display options.ConclusionMonaGO combines dynamic clustering and interactive visualisation as well as customisation options to assist biologists in obtaining meaningful representation of overrepresented GO terms, producing simplified outputs in an unbiased manner. MonaGO will facilitate the interpretation of GO analysis and will assist the biologists into the representation of the results.

LargeNetVis: Visual Exploration of Large Temporal Networks Based on Community Taxonomies.

Temporal (or time-evolving) networks are commonly used to model complex systems and the evolution of their components throughout time. Although these networks can be analyzed by different means, visual analytics stands out as an effective way for a pre-analysis before doing quantitative/statistical analyses to identify patterns, anomalies, and other behaviors in the data, thus leading to new insights and better decision-making. However, the large number of nodes, edges, and/or timestamps in many real-world networks may lead to polluted layouts that make the analysis inefficient or even infeasible. In this paper, we propose LargeNetVis, a web-based visual analytics system designed to assist in analyzing small and large temporal networks. It successfully achieves this goal by leveraging three taxonomies focused on network communities to guide the visual exploration process. The system is composed of four interactive visual components: the first (Taxonomy Matrix) presents a summary of the network characteristics, the second (Global View) gives an overview of the network evolution, the third (a node-link diagram) enables community- and node-level structural analysis, and the fourth (a Temporal Activity Map - TAM) shows the community- and node-level activity under a temporal perspective. We demonstrate the usefulness and effectiveness of LargeNetVis through two usage scenarios and a user study with 14 participants.

A Visual Analytics Approach for Structural Differences Among Graphs via Deep Learning.

Representing and analyzing structural differences among graphs help gain insight into the difference related patterns such as dynamic evolutions of graphs. Conventional solutions leverage representation learning techniques to encode structural information, but lack an intuitive way of studying structural semantics of graphs. In this article, we propose a representation-and-analysis scheme for structural differences among graphs. We propose a deep-learning-based embedding technique to encode multiple graphs while preserving semantics of structural differences. We design and implement a web-based visual analytics system to support comparative study of features learned from the embeddings. One distinctive feature of our approach is that it supports semantics-aware construction, quantification, and investigation of latent relations encoded in graphs. We validate the usability and effectiveness of our approach through case studies with three datasets.

Data visualization for distribution of people with disabilities

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, more and more data which contains the information of disabled population has been collected. However, researchers have pointed out some problems with visualizing data especially using traditional methods. Therefore, this study aimed to develop a web-based system for data visualization of disabled people distribution in Kuala Selangor, Selangor, Malaysia. Prototyping methodology was chosen in order to develop the web-based system. The results show two main interfaces of the web system which are the admin module and user module. Users may see a specific area, type of disabled people categories and the number of disabled people in the located area from the Google Map. This web system is hope to help public in identifying disabled people location using data visualization technique. In addition, in future it could help the authorize institutions/bodies in doing better planning for disabled people education, facilities and other needs based on its location distribution.

Artificial intelligence-based automatic visual inspection system for built heritage

PurposeHeritage is the latent part of a sustainable built environment. Conservation and preservation of heritage is one of the United Nations' (UN) sustainable development goals. Many social and natural factors seriously threaten heritage structures by deteriorating and damaging the original. Therefore, regular visual inspection of heritage structures is necessary for their conservation and preservation. Conventional inspection practice relies on manual inspection, which takes more time and human resources. The inspection system seeks an innovative approach that should be cheaper, faster, safer and less prone to human error than manual inspection. Therefore, this study aims to develop an automatic system of visual inspection for the built heritage.Design/methodology/approachThe artificial intelligence-based automatic defect detection system is developed using the faster R-CNN (faster region-based convolutional neural network) model of object detection to build an automatic visual inspection system. From the English and Dutch cemeteries of Surat (India), images of heritage structures were captured by digital camera to prepare the image data set. This image data set was used for training, validation and testing to develop the automatic defect detection model. While validating this model, its optimum detection accuracy is recorded as 91.58% to detect three types of defects: “spalling,” “exposed bricks” and “cracks.”FindingsThis study develops the model of automatic web-based visual inspection systems for the heritage structures using the faster R-CNN. Then it demonstrates detection of defects of spalling, exposed bricks and cracks existing in the heritage structures. Comparison of conventional (manual) and developed automatic inspection systems reveals that the developed automatic system requires less time and staff. Therefore, the routine inspection can be faster, cheaper, safer and more accurate than the conventional inspection method.Practical implicationsThe study presented here can improve inspecting the built heritages by reducing inspection time and cost, eliminating chances of human errors and accidents and having accurate and consistent information. This study attempts to ensure the sustainability of the built heritage.Originality/valueFor ensuring the sustainability of built heritage, this study presents the artificial intelligence-based methodology for the development of an automatic visual inspection system. The automatic web-based visual inspection system for the built heritage has not been reported in previous studies so far.

Visualization and analysis of local and distant population flows on the Qinghai-Tibet Plateau using crowd-sourced data

Human migration between cities is one important aspect of spatial interaction that not only reflects urban attractiveness but also denotes interactions amongst agglomerations. We therefore implemented a web-based visualization system to analyze and interactively explore local and distant population flow patterns between cities on the Qinghai-Tibet Plateau (QTP). We utilized 2017 Tencent population flow data from which we initially constructed inbound and outbound vectors for cities on the QTP. We then used multidimensional scaling to examine and visualize migration patterns and similarities between cities. Results reveal the presence of six local and three distant human mobility patterns on the QTP as well as average summer monthly migrations more than twice the level of those in the winter.

HyperTendril: Visual Analytics for User-Driven Hyperparameter Optimization of Deep Neural Networks.

To mitigate the pain of manually tuning hyperparameters of deep neural networks, automated machine learning (AutoML) methods have been developed to search for an optimal set of hyperparameters in large combinatorial search spaces. However, the search results of AutoML methods significantly depend on initial configurations, making it a non-trivial task to find a proper configuration. Therefore, human intervention via a visual analytic approach bears huge potential in this task. In response, we propose HyperTendril, a web-based visual analytics system that supports user-driven hyperparameter tuning processes in a model-agnostic environment. HyperTendril takes a novel approach to effectively steering hyperparameter optimization through an iterative, interactive tuning procedure that allows users to refine the search spaces and the configuration of the AutoML method based on their own insights from given results. Using HyperTendril, users can obtain insights into the complex behaviors of various hyperparameter search algorithms and diagnose their configurations. In addition, HyperTendril supports variable importance analysis to help the users refine their search spaces based on the analysis of relative importance of different hyperparameters and their interaction effects. We present the evaluation demonstrating how HyperTendril helps users steer their tuning processes via a longitudinal user study based on the analysis of interaction logs and in-depth interviews while we deploy our system in a professional industrial environment.

A web-based visual analytics system for traffic intersection datasets

A web-based visual analytics system for traffic intersection datasets

A Web-Based Visual Analytics System for Traffic Intersection Datasets

The efficiency of a road network may be improved by making the traffic intersections more efficient. A smart traffic intersection is equipped with different sensors from which it is possible today ...

Visual exploration of Internet news via sentiment score and topic models

Analyzing and understanding Internet news are important for many applications, such as market sentiment investigation and crisis management. However, it is challenging for users to interpret a massive amount of unstructured text, to dig out its accurate meaning, and to spot noteworthy news events. To overcome these challenges, we propose a novel visualization-driven approach for analyzing news text. We first collect Internet news from different sources and encode sentences into a vector representation suitable for input to a neural network, which calculates a sentiment score, to help detect news event patterns. A subsequent interactive visualization framework allows the user to explore the development of and relationships between Internet news topics. In addition, a method for detecting news events enables users and domain experts to interactively explore the correlations between market sentiment, topic distribution, and event patterns. We use this framework to provide a web-based interactive visualization system. We demonstrate the applicability and effectiveness of our proposed system using case studies involving blockchain news.

Prostate cancer trajectory-map: clinical decision support system for prognosis management of radical prostatectomy.

PurposeProstate cancer has a low mortality rate and requires persistent treatment; however, treatment decisions are challenging. Because prostate cancer is complex, the outcomes warrant thorough follow-up evaluation for appropriate treatment. Electronic health records (EHRs) do not present intuitive information. This study aimed to develop a Clinical Decision Support System (CDSS) for prognosis management of radical prostatectomy.MethodsWe used data from 5,199 prostate cancer patients from three hospitals’ EHRs in South Korea, comprising laboratory results, surgery, medication, and radiation therapy. We used open source R for data preprocessing and development of web-based visualization system. We also used R for automatic calculation functionalities of two factors to visualize the data, e.g., Prostate-Specific Antigen Doubling Time (PSADT), and four Biochemical Recurrence (BCR) definitions: American Society of Therapeutic Radiology and Oncology (ASTRO), Phoenix, consecutive PSA > 0.2 ng/mL, and PSA > 0.2 ng/mL.ResultsWe developed the Prostate Cancer Trajectory Map (PCT-Map) as a CDSS for intuitive visualization of serial data of PSA, testosterone, surgery, medication, radiation therapy, BCR, and PSADT.ConclusionsThe PCT-Map comprises functionalities for BCR and PSADT and calculates and visualizes the newly added patient data automatically in a PCT-Map data format, thus optimizing the visualization of patient data and allowing clinicians to promptly access patient data to decide the appropriate treatment.