Large-scale Data Visualization Research Articles

Graph Signal Processing for Machine Learning: A Review and New Perspectives

The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing (GSP), a vibrant branch of signal processing models and algorithms that aims at handling data supported on graphs, opens new paths of research to address this challenge. In this article, we review a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms. In particular, our discussion focuses on the following three aspects: exploiting data structure and relational priors, improving data and computational efficiency, and enhancing model interpretability. Furthermore, we provide new perspectives on future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing on one side, and machine learning and network science on the other. Cross-fertilization across these different disciplines may help unlock the numerous challenges of complex data analysis in the modern age.

Towards Efficient Front-End Visual Sensing for Digital Retina: A Model-Centric Paradigm

The digital retina excels at providing enhanced visual sensing and analysis capability for city brain in smart cities, and can feasibly convert the visual data from visual sensors into semantic features. With the deployment of deep learning or handcrafted models, these features are extracted on front-end devices, then delivered to back-end servers for advanced analysis. In this scenario, we propose a model generation, utilization and communication paradigm, aiming at strong front-end sensing capabilities for establishing better artificial visual systems in smart cities. In particular, we propose an integrated multiple deep learning models reuse and prediction strategy, which dramatically increases the feasibility of the digital retina in large-scale visual data analysis in smart cities. The proposed multi-model reuse scheme aims to reuse the knowledge from models cached and transmitted in digital retina to obtain more discriminative capability. To efficiently deliver these newly generated models, a model prediction scheme is further proposed by encoding and reconstructing model differences. Extensive experiments have been conducted to demonstrate the effectiveness of proposed model-centric paradigm.

What Makes Politicians’ Instagram Posts Popular? Analyzing Social Media Strategies of Candidates and Office Holders with Computer Vision

Previous research on the success of politicians’ messages on social media has so far focused on a limited number of platforms, especially Facebook and Twitter, and predominately studied the effects of textual content. This research reported here applies computer vision analysis to a total of 59,020 image posts published by 172 Instagram accounts of U.S. politicians, both candidates and office holders, and examines how visual attributes influence audience engagement such as likes and comments. In particular, this study introduces an unsupervised approach that combines transfer learning and clustering techniques to discover hidden categories from large-scale visual data. The results reveal that different self-personalization strategies in visual media, for example, images featuring politicians in private, nonpolitical settings, showing faces, and displaying emotions, generally increase audience engagement. Yet, a significant portion of politician’s Instagram posts still fell into the traditional, “politics-as-usual” type of political communication, showing professional settings and activities. The analysis explains how self-personalization is embodied in specific visual portrayals and how different self-presentation strategies affect audience engagement on a popular but less studied social media platform.

NeuralEE: A GPU-Accelerated Elastic Embedding Dimensionality Reduction Method for Visualizing Large-Scale scRNA-Seq Data.

The dramatic increase in amount and size of single-cell RNA sequencing data calls for more efficient and scalable dimensional reduction and visualization tools. Here, we design a GPU-accelerated method, NeuralEE, which aggregates the advantages of elastic embedding and neural network. We show that NeuralEE is both scalable and generalizable in dimensional reduction and visualization of large-scale scRNA-seq data. In addition, the GPU-based implementation of NeuralEE makes it applicable to limited computational resources while maintains high performance, as it takes only half an hour to visualize 1.3 million mice brain cells, and NeuralEE has generalizability for integrating newly generated data.

Portable interactive visualization of large-scale simulations in geotechnical engineering using Unity3D

Development in large-scale geotechnical engineering simulation places tremendous demand for efficient visualization of such simulation data. This study presents a lightweight software tool, i.e. Geotechnical Interactive Visualization (GIV), as a solution to this challenge, which achieves efficient interactive visualization of large-scale simulations data in geotechnical engineering. Visualization data flow and algorithms specifically optimized for common geotechnical engineering applications are implemented in GIV. GIV can visualize geotechnical structure models with time-varying attributes attached to mesh with fixed topology, and also models with time-varying mesh topologies but no attributes attached, the two most common visualization tasks in geotechnical engineering. Challenges for large-scale simulation data visualization, including parallel simulation data redundancy, massive data size, dynamic user interaction, and portability are overcome via specifically designed algorithms for simulation data preprocessing and optimized visualization modules using the powerful 3D rendering and interactive game engine Unity3D. Comparison of GIV with several widely used visualization tools for the visualization of large-scale idealized datasets and realistic geotechnical simulations highlights the visualization efficiency, smooth interactivity, and lightweight features of GIV.

A high-throughput platform for interpretation of metabolite profile data from pepper (Capsicum) fruits of 13 phenotypes associated with different fruit maturity states.

Nowadays, novel tools have been developed for efficient analysis and visualization of large-scale metabolite profile data associated with metabolic pathways. A high-throughput platform using PathVisio 3 combined with multivariate analysis is proposed for the first time. Additionally, this is the first analysis of the relationships among terpenoids monoterpene, sesquiterpene, triterpene, and tetraterpene during pepper fruit ripening, and their changes. This platform was successfully applied to interpret large-scale data related to 131 metabolites from mature and immature fruits of 13 pepper phenotypes. The carotenoid-derived volatiles, such as dihydroactinidiolide and β-ionone were closely correlated with carotenoids, indicating that the synthesis and degradation of carotenoids occurred in pepper fruit mature stage. Using PathVisio 3, the metabolic changes in pathway could be presented quickly, revealing the accumulation of stress-related metabolites, such as proline, capsaicin, and phenylalanine, in the mature stage. This approach could provide useful information about comprehensive biochemical regulation of fruit ripening.

Post-processing and visualization of large-scale DEM simulation data with the open-source VELaSSCo platform

Regardless of its origin, in the near future the challenge will not be how to generate data, but rather how to manage big and highly distributed data to make it more easily handled and more accessible by users on their personal devices. VELaSSCo (Visualization for Extremely Large-Scale Scientific Computing) is a platform developed to provide new visual analysis methods for large-scale simulations serving the petabyte era. The platform adopts Big Data tools/architectures to enable in-situ processing for analytics of engineering and scientific data and hardware-accelerated interactive visualization. In large-scale simulations, the domain is partitioned across several thousand nodes, and the data (mesh and results) are stored on those nodes in a distributed manner. The VELaSSCo platform accesses this distributed information, processes the raw data, and returns the results to the users for local visualization by their specific visualization clients and tools. The global goal of VELaSSCo is to provide Big Data tools for the engineering and scientific community, in order to better manipulate simulations with billions of distributed records. The ability to easily handle large amounts of data will also enable larger, higher resolution simulations, which will allow the scientific and engineering communities to garner new knowledge from simulations previously considered too large to handle. This paper shows, by means of selected Discrete Element Method (DEM) simulation use cases, that the VELaSSCo platform facilitates distributed post-processing and visualization of large engineering datasets.

Application of Big Data Visualization in Urban Planning

During the past ten years, with the process of urbanization accelerates, urban population has increased sharply. Traffic congestion, noise pollution, garbage treatment and other problems, which make the traditional urban planning methods difficult to shoulder this heavy responsibility. At the same time, intelligent city and the construction of informationize is also developing rapidly, the practice and research of big data in urban planning industry are widely used. Large scale data visualization is one of the most important steps in big data. Not only can the data be expressed in visible form to improve the efficiency of data analysis, but also human-computer interaction can be carried out. To overcome these problems, this paper studies the problem of urban planning based on large scale data visualization. Firstly, it extends the concept and model of big data visualization to the significance and framework of applying big data visualization to urban planning. Then through the case of visualization of traffic data and environmental sound data, we can see that both quality and efficiency have been significantly improved. Furthermore in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potential important research directions for urban planning.

An accelerated rendering scheme for massively large point cloud data

In the field of large-scale data visualization, the graphics rendering speed is one of the most important factors for its application development. Since the large-scale data visualization usually requires three-dimensional representations, the three-dimensional graphics libraries such as OpenGL and DirectX have been widely used. In this paper, we suggest a new way of accelerated rendering, through directly using the direct rendering manager packets. Current three-dimensional graphics features are focused on the efficiency of general purpose rendering pipelines. In contrast, we concentrated on the speed-up of the special-purpose rendering pipeline, for point cloud rendering. Our result shows that we achieved our purpose effectively.

Learning at the Intersection of Self and Society: The Family Geobiography as a Context for Data Science Education

Open large-scale datasets (LSDS) and data visualization technologies are new cultural tools that have potential to inform public dialogue and learning about important socioeconomic and scientific matters, particularly if the data is used to consider both personal and shared experiences. This paper reports on a design study in which diverse middle and high school youth in a free summer workshop at an urban public library were asked to model their family geobiographies, or their personal family migration stories, with socioeconomic LSDS. Youth represented family decision-making and social conditions that might have motivated family movements with online, dynamic data modeling and mapping tools (Gapminder.org; SocialExplorer.com). The qualitative video analysis examined participants’ experiences and learning in storytelling and modeling the family geobiography, focusing on multimodal talk-in-interaction to understand how the study design engaged learners’ capacities, histories, and imagined futures in relation to the LSDS and supported learning about oneself and society. Grounded examples from two family cases are used to illustrate how participants placed their families and selves into data ontologies and the role of family members in composing storylines.

Trajectory Object Detection using Deep Learning Algorithms

Video surveillance data in smart cities needs to analyze a large amount of video footage in order to locate the people who are violating the traffic rules. The fact is that it is very easy for the human being to recognize different objects in images and videos. For a computer program this is quite a difficult task. Hence there is a need for visual big data analytics which involves processing and analyzing large scale visual data such as images or videos. One major application of trajectory object detection is the Intelligent Transport Systems (ITS). Vehicle type detection, tracking and classification play an important role in ITS. In order to analyze huge amount of video footage deep learning algorithms have been deployed. The main phase of vehicle type detection includes annotating the data, training the model and validating the model. The problems and challenges in identifying or detecting type of vehicle are due to weather, shadows, blurring effect, light condition and quality of the data. In this paper deep learning algorithms such as Faster R CNN and Mask R CNN and Frameworks like YOLO were used for the object detection. Dataset (different types of vehicle pictures in video format) were collected both from in-house premises as well as from the Internet to detect and recognize the type of vehicles which are common in traffic systems. The experimental results show that among the three approaches used the Mask R CNN algorithm is found to be more efficient and accurate in vehicle type detection.

AGGREGATING CLOUD-FREE SENTINEL-2 IMAGES WITH GOOGLE EARTH ENGINE

Abstract. Cloud coverage is one of the biggest concerns in spaceborne optical remote sensing, because it hampers a continuous monitoring of the Earth’s surface. Based on Google Earth Engine, a web- and cloud-based platform for the analysis and visualization of large-scale geospatial data, we present a fully automatic workflow to aggregate cloud-free Sentinel-2 images for user-defined areas of interest and time periods, which can be significantly shorter than the one-year time frames that are commonly used in other multi-temporal image aggregation approaches. We demonstrate the feasibility of our workflow for several cities spread around the globe and affected by different amounts of average cloud cover. The experimental results confirm that our results are better than the results achieved by standard approaches for cloud-free image aggregation.

Parallel visualization of large-scale multifield scientific data

Following the recent rapid growth in supercomputer performance, many real-world problems in fields such as nuclear fusion energy and electromagnetic environments can be solved via multiphysics simulation, which outputs multifield datasets. However, current multifield visualization has difficulty handling multiphysics parallel simulation data. First, it is difficult to correctly visualize overlapping multifield data with semitransparent properties because of the complex distribution of partitioned data domains across multicore processors. Second, the interactive visualization performance of large-scale multifield data in serial processing mode on a personal computer is often slow because multiphysics simulations can produce large-scale datasets, i.e., of the order of gigabytes to terabytes. Considering the fidelity and efficiency of large-scale data visualization on supercomputer, a new parallel visualization method is required for multifield scientific data that do not change the original distribution of the mesh data generated by the multiphysics applications. This paper introduces a hybrid scheduling framework for the parallel visualization of large-scale multifield scientific data. This framework is used to overcome problems both in correct visual representation and in efficient visualization of large-scale multiphysics applications. We discuss the results of several typical multiphysics applications to verify the feasibility and reliability of our proposed framework. This framework currently supports scalable in situ visualization of up to 8.5 billion mesh cells on the 10 k cores of China’s Tianhe-2 supercomputer, which could help domain scientists understand multiphysics phenomena more clearly and accurately.

A new analytic framework for prediction of migration patterns and locations of historical manuscripts based on their script types

Abstract In this article, we utilized large-scale statistical analysis and data visualization techniques of the greatest collection in the world of Hebrew manuscript metadata records to develop a new methodology for quantitative investigation of the palaeographic, geographic, and temporal characteristics of historical manuscripts. The study aims to explore whether and to what extent the script type of the manuscript and its changes over time can be used to automatically predict and complete missing geospatial data of the manuscripts. To this end, various ontological entities were used as features to train supervised machine-learning algorithms to predict the places of writing of manuscripts which were often absent in the catalogue records. The obtained results show that while the script type as an only feature might not be sufficient for prediction of the location of the manuscript’s writing, its combination with temporal data of the manuscript yielded about 80% accuracy. Eventually, our system was able to complete the missing places of writing for over 60% of the manuscripts in the corpus. Moreover, we found that through typical and marginal script types in different regions and their changes over time, it is possible to draw the migration map of the Jewish communities over the centuries. This reinforces the findings of historical research on Jewish migration patterns and communal formation. For example, the waves of immigration from Western Europe can be seen clearly from the second half of the 13th century, which continued until the 17th century and greatly increased the Eastern European Jewish community.

Visualization of Large Scale Data by the CAVE VR System

没入型のバーチャルリアリティ装置であるCAVE装置で大規模なデータの可視化を実現した．興味領域の抽出と自動詳細度設定機能をVFIVEと呼ばれるCAVE装置用の対話的可視化ソフトウェアに実装した．興味領域の選択は，CAVE装置の中で対話的かつ直感的に行うことができる．この自動詳細度設定機能は，等値面表示などのスカラーデータの可視化手法では，興味領域の大きさにかかわらず常に一定の解像度のデータをもとに可視化を行うことを可能にする．流線表示などのベクトルデータの可視化手法では，興味領域の大きさに応じて流線の解像度を調節する．本機能を用いて大規模なデータを実際に可視化することで，大規模データの対話的な可視化が可能なことを確認した．

Interactive Visualization of Large-scale Design Data using Scatter Plot Matrix and Parallel Coordinate plot

Interactive Visualization of Large-scale Design Data using Scatter Plot Matrix and Parallel Coordinate plot

Construction of integrated particle rendering environment for large scale data visualization

Construction of integrated particle rendering environment for large scale data visualization

Shared-Memory Parallel Computation of Morse-Smale Complexes with Improved Accuracy.

Topological techniques have proven to be a powerful tool in the analysis and visualization of large-scale scientific data. In particular, the Morse-Smale complex and its various components provide a rich framework for robust feature definition and computation. Consequently, there now exist a number of approaches to compute Morse-Smale complexes for large-scale data in parallel. However, existing techniques are based on discrete concepts which produce the correct topological structure but are known to introduce grid artifacts in the resulting geometry. Here, we present a new approach that combines parallel streamline computation with combinatorial methods to construct a high-quality discrete Morse-Smale complex. In addition to being invariant to the orientation of the underlying grid, this algorithm allows users to selectively build a subset of features using high-quality geometry. In particular, a user may specifically select which ascending/descending manifolds are reconstructed with improved accuracy, focusing computational effort where it matters for subsequent analysis. This approach computes Morse-Smale complexes for larger data than previously feasible with significant speedups. We demonstrate and validate our approach using several examples from a variety of different scientific domains, and evaluate the performance of our method.

Visualizing virtual users through art: Usage statistics in outreach and marketing

Most modern academic libraries have physical and virtual spaces—and patrons. Physical users can be hard to miss, but virtual users often leave only traces behind. It’s all too easy, and misleading, to assess library use based on bodies in chairs. While online resources provide statistics documenting use, these numbers may seem unreal to administrators and funding agencies. Western New Mexico University’s Miller Library designed an art installation, “A Year of Virtual Research,” as a large-scale physical data visualization project to make virtual library use more present and real to the university community.

An alternative visualization pipeline for large-scale data sets by using early visibility test point rendering

The amount of produced data required to be visualized and analyzed has grown year by year, and the traditional approach of using larger computational resources or exploiting task and data parallelism seems to have reached its limit. Therefore, a new paradigm for large-scale data visualization becomes highly desired, and in this paper, we propose a new and optimized visualization pipeline which uses a point rendering-based early visibility testing for reducing the unnecessary rendering in the final stage of the visualization pipeline, and we named this technique “Early Visibility Test Point Rendering”. In a densely populated polygonal mesh scene, where multiple triangles may cover a single pixel, unnecessary and wasteful rendering will occur in the final stage of the traditional visualization pipeline, that is, during the rasterization process. Therefore, we propose an alternative visualization pipeline by introducing the “Early Visibility Test Point Rendering” for selecting only the visible polygonal elements for a given visualization scene. This visibility testing can be executed on the CPU side, and only the visible polygonal elements are needed to be sent to the GPU for an optimized rendering. We could verify the effectiveness of our proposed approach by using synthetic datasets, and also a real-world large-scale simulation result.