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International Joint Conference on Neural Networks (IJCNN)

作者： Tam Thanh Nguyen Song, Insu James Cook Univ Australia Sch Business IT Singapore Campus Singapore Singapore

ISBN: (纸本)9781509006199

Information visualization is essential for improving effectiveness and efficiency of data exploration and knowledge discovery. Therefore, visualization has been used in a wide range of fields from biology, medicine, criminal activity analysis to business and education. Information visualization has become more important than ever as the amount of data being generated has increased dramatically in recent years. One of the major difficulties of information visualization is performance, and this is even more critical when visualizing big data. One potential solution to this challenge is data sampling while maintaining fidelity of visual representation. In this paper, we propose two new centrality clustering-based sampling approaches that apply centrality measures on clusters of data points in order to make more informed sampling than random sampling approaches. We evaluate the new methods on graph data sets. The results show that the new methods significantly outperform existing data sampling methods in term of perceived differences and their ability to preserve essential visual information. Moreover computational complexity is comparable or even better than simple random sampling methods.

关键词： big data big data visualization data sampling clustering algorithm cluster sampling large data sets

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Towards big data Interactive visualization in Ambient Intelligence Environments 1

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4th International Conference on Distributed, Ambient and Pervasive Interactions (DAPI) held as part of 18th International Conference on Human-Computer Interaction (HCI International)

作者： Drossis, Giannis Margetis, George Stephanidis, Constantine Fdn Res & Technol Hellas FORTH Inst Comp Sci N Plastira 100 Iraklion 70013 Crete Greece Univ Crete Dept Comp Sci Iraklion Crete Greece

ISBN: (数字)9783319398624

ISBN: (纸本)9783319398624;9783319398617

big data visualization relies on an interdisciplinary research area that includes mass data storage and retrieval, operations, analytics, security, ethics as well as visualization and interaction with end users. This paper reports on the characteristics of big data systems, mainly focusing on information visualization and discusses a number of methods towards this direction, analyzing research issues and challenges that emerge. Additionally, this paper discusses new approaches for big data visualization in the context of Ambient Intelligence (AmI) environments, highlighting new aspects in the field in respect to information presentation and natural user interaction. Furthermore, a scenario of big data visualization in AmI environments is presented, aiming at bringing to surface the new potential of such approaches in terms of interaction simplification, and adaptation to the context of use.

关键词： big data big data visualization big data interaction Ambient Intelligence data centre infrastructure management

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A Progressive Real-time visualization Method for Earthquake big data

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电脑学刊 2022年第1期33卷 87-100页

作者： Weifeng Shan Jianqiao Li Yuntian Teng Huiling Chen Zhiyang Li Maofa Wang

As the volume of seismic observation time-series data grows larger, web-based visualization schemes suffer from longer system response times. Although big data visualization schemes based on sampling and filtering can greatly reduce the data scale and shorten transmission time, what it gains in speed it loses in information. Progressive visualization has become an increasingly popular scheme because it can quickly ＂see＂ some results without having to wait for all the data, thus enabling users to grasp a data-change trend quickly and perceive the rules behind it. In this paper, a Cloudberry-based progressive real-time visualization schema for earthquake big data (PVSEBD) is proposed for the first time. It greatly shortens the transmission time of each data slice, improves the user interaction experience, and meets the long-term, large-scale visualization needs of earthquake consultation business. Because the correctness of average aggregation function (AVG) in progressive visualization is often not guaranteed, this paper proposes an innovative AVG translation rule solution based on the accumulability of the COUNT and SUM aggregation functions. The experimental results showed that PVSEBD automatically adjusts the amount of data returned each time according to size and has a shorter response time for each interaction compared with the solution based on the web-based visualization toolkit Portable Progressive Parallel Processing Pipeline (P5).

关键词： earthquake big data big data visualization progressive visualization aggregate function Cloudberry

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A model-driven approach to automate data visualization in big data analytics

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INFORMATION visualization 2020年第1期19卷 24-47页

作者： Golfarelli, Matteo Rizzi, Stefano Univ Bologna DISI CINI Viale Risorgimento 2 I-40136 Bologna Italy

In big data analytics, advanced analytic techniques operate on big datasets aimed at complementing the role of traditional OLAP for decision making. To enable companies to take benefit of these techniques despite the lack of in-house technical skills, the H2020 TOREADOR Project adopts a model-driven architecture for streamlining analysis processes, from data preparation to their visualization. In this article, we propose a new approach named SkyViz focused on the visualization area, in particular on (1) how to specify the user's objectives and describe the dataset to be visualized, (2) how to translate this specification into a platform-independent visualization type, and (3) how to concretely implement this visualization type on the target execution platform. To support step (1), we define a visualization context based on seven prioritizable coordinates for assessing the user's objectives and conceptually describing the data to be visualized. To automate step (2), we propose a skyline-based technique that translates a visualization context into a set of most suitable visualization types. Finally, to automate step (3), we propose a skyline-based technique that, with reference to a specific platform, finds the best bindings between the columns of the dataset and the graphical coordinates used by the visualization type chosen by the user. SkyViz can be transparently extended to include more visualization types on one hand, more visualization coordinates on the other. The article is completed by an evaluation of SkyViz based on a case study excerpted from the pilot applications of the TOREADOR Project.

关键词： big data visualization skyline model-driven architecture

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Visual Analysis of Multidimensional big data: A Scalable Lightweight Bundling Method for Parallel Coordinates

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IEEE TRANSACTIONS ON big data 2023年第1期9卷 106-117页

作者： Cui, Wenqiang Strazdins, Girts Wang, Hao Norwegian Univ Sci & Technol Dept ICT & Nat Sci N-6009 Alesund Norway Norwegian Univ Sci & Technol Dept Comp Sci N-2815 Gjovik Norway

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.

关键词： data visualization big data Rendering (computer graphics) Image edge detection Clutter Visual analytics Scalability Parallel coordinates edge bundling visual analytics big data visualization anomaly detection

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An Immersive Web visualization Platform for a big data Context in Bosch's Industry 4.0 Movement 16th

An Immersive Web Visualization Platform for a Big Data Conte...

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16th European, Mediterranean, and Middle Eastern Conference (EMCIS)

作者： Rebelo, Joao Andrade, Carina Costa, Carlos Santos, Maribel Y. Univ Minho Dept Informat Syst Guimaraes Portugal

ISBN: (纸本)9783030443214;9783030443221

Motivated by challenges that emerged from Complex Event Processing (CEP) in big data contexts, an Intelligent Event Broker (IEB) was previously proposed as a CEP system built on flexible and scalable big data technologies and techniques, being already applied to Industry 4.0 scenarios. A key feature of the IEB lies in an effective visualization system for meta-monitorization and management. An analysis of previous scientific and technical literature has shown scarcity of proposals or development of such mechanisms. This paper proposes a Web visualization Platform for managing and monitoring an IEB system based on novel approaches that include interactive exploration techniques, tridimensional visualizations and mixed-reality environments. Being mainly targeted towards Industry 4.0, this paper presents a demonstration case at Bosch Car Multimedia Portugal. Results indicate that significant value can be obtained when the visualization system is applied to decision support scenarios within organizations foreseeing event processing, big data, and Industry 4.0 projects.

关键词： big data visualization Immersive Analytics Complex Event Processing

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In-Situ Process Monitoring and Defects Detection Based on Geometrical Topography With Streaming Point Cloud Processing in Directed Energy Deposition

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IEEE ACCESS 2023年 11卷 131319-131337页

作者： Imran, Muhammad Mu'az Kim, Young Jung, Gisun de Silva, Liyanage Chandratilak Suh, Jeong-Hun Abas, Pg Emeroylariffion Kim, Yun Bae Sungkyunkwan Univ Dept Ind Engn Suwon 16419 South Korea Univ Brunei Darussalam Fac Integrated Technol BE-1410 Gadong Brunei Hwacheon Machinery Co Ltd Digital Mfg DM Lab Gwangju 506733 South Korea

The 3D printing industry faces challenges in ensuring reliable and repeatable processes, as increasing slopes can lead to defects and faults forming layer by layer in fabricated parts. Detecting these defects through post-processing quality inspections is time-consuming and laborious. To address this, a new method is proposed that incorporates a scoring scheme to quantitatively evaluate process performance using clad height data during printing. This approach aims to save time and cost while preserving the structural integrity of the part. The study develops a layer-wise point cloud processing technique to convert the incoming unordered data streams into rasterized points. By transforming raw signals into spatially equidistant points representing clad height in a 2-D Cartesian plane, a heatmap tomography of each layer is generated. Subsequently, a novel Defects-Finder algorithm is developed to locate and cluster surface defects based on the assigned scores. The findings demonstrate the algorithm's ability to identify the root cause of propagated faults, which can result in severe defects. Additionally, the study employs various statistical measures in the layer-level analysis to evaluate miniature process faults or shifts, which may get overshadowed, including cases of under and over-deposition. Through comparison and validation with physical artifacts, the proposed method proves effective in identifying and assessing process faults. Ultimately, this method enhances big data visualization for cost-effective quality control and boosts the overall productivity of additive manufacturing processes. By streamlining defect detection and performance evaluation, it addresses the challenges faced by the industry in ensuring reliable 3D printing outcomes.

关键词： big data visualization directed energy deposition in-situ quality monitoring performance evaluation surface defects detection

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A New Tool Based on GIS Technology for Massive Public Transport data 11th

A New Tool Based on GIS Technology for Massive Public Transp...

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11th International Conference on Model and data Engineering (MEDI) / Workshop on Modeling, Verification and Testing of Dependable Critical Systems (DETECT)

作者： Brisaboa, Nieves R. de Bernardo, Guillermo Gutierrez-Asorey, Pablo Parama, Jose R. Rodeiro, Tirso V. Silva-Coira, Fernando Univ A Coruna Ctr Invest CITIC La Coruna Spain

ISBN: (纸本)9783031231186;9783031231193

In this work, we present the design of a novel geographic information tool for the analysis of public transportation data. The widespread integration of user traveler cards have enabled public transport operators to generate and store large amounts of data related to user movements within the transport network. However, these authorities are seldom equipped to efficiently exploit these data in order to produce a comprehensible analysis of the transport network usage. This is not only due to the sheer amount of data in need of processing, but also because most public transport operators only validate the travel card on boarding, whereas data referring to transfers and alightings are generally unavailable. Thus, the system we propose not only addresses efficient storage and exploitation of big datasets, but also the reconstruction of complete journeys by using a prediction algorithm to deduce the alighting stop for each boarding. Furthermore, we also provide the transport operators with easy-to-use means of visualizing and analyzing the data through a graphical interface.

关键词： Geographic information systems Compact data structures big data visualization

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A Digital Twin for Maritime Situational Awareness 10

A Digital Twin for Maritime Situational Awareness

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16th IEEE/ACM International Conference on Utility and Cloud Computing (UCC) / 10th IEEE/ACM International Conference on big data Computing, Applications and Technologies (BDCAT)

作者： Spiliopoulos, Giannis Grigoropoulos, Georgios Filippou, Evangelia Chamatidis, Ilias Vodas, Marios Kaliorakis, Manolis Troupiotis-Kapeliaris, Alexandros Zissis, Dimitris MarineTraff Kpler Res Labs Athens Greece Univ Aegean Dept Prod & Syst Design Engn Ermoupolis Syros Greece

ISBN: (纸本)9798400704734

Monitoring vessel traffic on a global scale is a complex and challenging task. The large number of moving vessels and the complexity of monitoring their position and forecasting their route in real-time require novel, advanced and highly scalable big-data mechanisms. In this work a digital twin for constant maritime situational awareness on a global scale is presented. The described multi-layered system is able to visualize maritime traffic in real-time, based on data from the Automatic Identification System (AIS), while also providing forecasts of future movement based on machine learning and deep learning techniques. The system is validated using real streaming AIS data from around the globe to demonstrate its performance, scalability and parallelization efficiency.

关键词： big data Digital Twins Machine Learning Applications Maritime Situational Awareness Real Time Systems big data visualization

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Interactive visualization of big data in the field of accounting A survey of current practice and potential barriers for adoption

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JOURNAL OF APPLIED ACCOUNTING RESEARCH 2019年第4期20卷 497-525页

作者： Perkhofer, Lisa Maria Hofer, Peter Walchshofer, Conny Plank, Thomas Jetter, Hans-Christian Univ Appl Sci Upper Austria Dept Controlling Accounting & Finance Wels Austria Univ Appl Sci Dept Accounting Controlling & Financial Managemen Neubrandenburg Germany Fachhsch Oberosterreich Dept Accounting Controlling & Financial Managemen Fak Management Steyr Austria Fachhsch Oberosterreich Fak Management Steyr Austria Fachhsch Oberosterreich Fak Informat Kommunikat Medien Human Ctr Comp Hagenberg Austria

Purpose big data introduces high amounts and new forms of structured, unstructured and semi-structured data into the field of accounting and this requires alternative data management and reporting methods. Generating insights from these new data sources highlight the need for different and interactive forms of visualization in the field of visual analytics. Nonetheless, a considerable gap between the recommendations in research and the current usage in practice is evident. In order to understand and overcome this gap, a detailed analysis of the status quo as well as the identification of potential barriers for adoption is vital. The paper aims to discuss this issue. Design/methodology/approach A survey with 145 business accountants from Austrian companies from a wide array of business sectors and all hierarchy levels has been conducted. The survey is targeted toward the purpose of this study: identifying barriers, clustered as human-related and technological-related, as well as investigating current practice with respect to interactive visualization use for big data. Findings The lack of knowledge and experience regarding new visualization types and interaction techniques and the sole focus on Microsoft Excel as a visualization tool can be identified as the main barriers, while the use of multiple data sources and the gradual implementation of further software tools determine the first drivers of adoption. Research limitations/implications - Due to the data collection with a standardized survey, there was no possibility of dealing with participants individually, which could lead to a misinterpretation of the given answers. Further, the sample population is Austrian, which might cause issues in terms of generalizing results to other geographical or cultural heritages. Practical implications - The study shows that those knowledgeable and familiar with interactive big data visualizations indicate high perceived ease of use. It is, therefore, necessary to offer sufficie

关键词： Financial reporting big data Visual analytics Interactive visualization big data visualization

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