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21st International conference on Information visualisation (iv)

作者： Guchev, Vladimir Gena, Cristina Univ Turin Dept Comp Sci Turin Italy

ISBN: (纸本)9781538608319

The exploration of moderately dense networks is used in various challenges of visual data analysis. Frequently, the solutions lay in graph drawing, based on automatic force-directed layout, which results in a spontaneous and irreproducible node-link diagram. Currently available approaches to improve its readability are generally oriented to finite rendering without providing to the analyst handy tools for post-layout manipulations. Enabling indirect manual control on visualizations through multi-step menus may appear difficult to learn and use. Thus, this problem requires a more intuitive way of solving. This paper presents an original toolset for userguided refinement of the force-directed graph layout, with a bias on pen-centric sketching techniques.

关键词： data visualization human-centered computing user interface design graph exploration user-generated layout pen-centric and sketch-based interaction

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A Transfer Entropy Based visual Analytics System for Identifying Causality of Critical Hardware Failures Case Study: CPU Failures in the K Computer 18th

A Transfer Entropy Based Visual Analytics System for Identif...

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18th Annual Asia Simulation conference (AsiaSim)

作者： Koiso, Kazuki Sakamoto, Naohisa Nonaka, Jorji Shoji, Fumiyoshi Kobe Univ Grad Sch Syst Informat Kobe Hyogo Japan RIKEN Ctr Computat Sci Kobe Hyogo Japan

ISBN: (纸本)9789811328534;9789811328527

Large-scale scientific computing facilities usually operate expensive HPC (High Performance Computing) systems, which have their computational and storage resources shared with the authorized users. On such shared resource systems, a continuous and stable operation is fundamental for providing the necessary hardware resources for the different user needs, including large-scale numerical simulations, which are the main targets of such large-scale facilities. For instance, the K computer installed at the R-CCS (RIKEN Center for Computational Science), in Kobe, Japan, enables the users to continuously run large jobs with tens of thousands of nodes (a maximum of 36,864 computational nodes) for up to 24 h, and a huge job by using the entire K computer system (82,944 computational nodes) for up to 8 h. Critical hardware failures can directly impact the affected job, and may also indirectly impact the scheduled subsequent jobs. To monitor the health condition of the K computer and its supporting facility, a large number of sensors has been providing a vast amount of measured data. Since it is almost impossible to analyze the entire data in real-time, these information has been stored as log data files for post-hoc analysis. In this work, we propose a visual analytics system which uses these big log data files to identify the possible causes of the critical hardware failures. We focused on the transfer entropy technique for quantifying the "causality" between the possible cause and the critical hardware failure. As a case study, we focused on the critical CPU failures, which required subsequent substitution, and utilized the log files corresponding to the measured temperatures of the cooling system such as air and water. We evaluated the usability of our proposed system, by conducting practical evaluations via a group of experts who directly works on the K computer system operation. The positive and negative feedbacks obtained from this evaluation will be considered for the

关键词： data exploration Failure analysis High performance computing system Log data Causality Transfer entropy

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Exploring the Role of Sound in Augmenting visualization to Enhance User Engagement 11

Exploring the Role of Sound in Augmenting Visualization to E...

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IEEE Pacific visualization Symposium (IEEE PacificVis)

作者： Du, Meng Chou, Jia-Kai Ma, Chen Chandrasegaran, Senthil Ma, Kwan-Liu Univ Calif Davis Davis CA 95616 USA

ISBN: (纸本)9781538614242

Studies on augmenting visualization with sound are typically based on the assumption that sound can be complementary and assist in data analysis tasks. While sound promotes a different sense of engagement than vision, we conjecture that by augmenting non-speech audio to a visualization can not only help enhance the users' perception of the data but also increase their engagement with the data exploration process. We have designed a preliminary user study to test users' performance and engagement while exploring in a data visualization system under two different settings: visual-only and audiovisual. For our study, we used basketball player movement data in a game and created an interactive visualization system with three linked views. We supplemented sound to the visualization to enhance the users' understanding of a team's offensive/defensive behavior. The results of our study suggest that we need to better understand the effect of sound choice and encoding before considering engagement. We also find that sound can be useful to draw novice users' attention to patterns or anomalies in the data. Finally, we propose follow-up studies with designs informed by the findings from this study.

关键词： sound-augmented visualization engagement

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ImageX - Explore and Search Local/Private Images 24th

ImageX - Explore and Search Local/Private Images

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24th International conference on MultiMedia Modeling (MMM)

作者： Hezel, Nico Barthel, Kai Uwe Jung, Klaus HTW Berlin Univ Appl Sci Visual Comp Grp Wilhelminenhofstr 75a D-12459 Berlin Germany

ISBN: (纸本)9783319736006;9783319735993

In this paper we present a system to visually explore and search large sets of untagged images, running on common operating systems and consumer hardware. High quality image descriptors are computed using activations of a convolutional neural network. By applying normalization and a principal component analysis of the activations compact feature vectors of only 64 bytes are generated. The L1-distances for these feature vectors can be calculated very fast using a novel computation approach and allows search-by-example queries to be processed in fractions of a second. We further show how entire image collections can be transferred into hierarchical image graphs and describe a scheme to explore this complex data structure in an intuitive way. To enable keyword search for untagged images, reference features for common keywords are generated. These features are constructed by collecting and clustering examples images from the web.

关键词： Image exploration visualization CBIR Keyword search

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Patterns and Sequences: Interactive exploration of Clickstreams to Understand Common Visitor Paths

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2017年第1期23卷 321-330页

作者： Liu, Zhicheng Wang, Yang Dontcheva, Mira Hoffman, Matthew Walker, Seth Wilson, Alan Adobe Res San Jose CA USA Univ Calif Davis Davis CA USA Adobe Syst Inc San Jose CA USA

Modern web clickstream data consists of long, high-dimensional sequences of multivariate events, making it difficult to analyze. Following the overarching principle that the visual interface should provide information about the dataset at multiple levels of granularity and allow users to easily navigate across these levels, we identify four levels of granularity in clickstream analysis: patterns, segments, sequences and events. We present an analytic pipeline consisting of three stages: pattern mining, pattern pruning and coordinated exploration between patterns and sequences. Based on this approach, we discuss properties of maximal sequential patterns, propose methods to reduce the number of patterns and describe design considerations for visualizing the extracted sequential patterns and the corresponding raw sequences. We demonstrate the viability of our approach through an analysis scenario and discuss the strengths and limitations of the methods based on user feedback.

关键词： Clickstream data sequence mining visual analytics event sequences

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17th Wuhan International conference on E-Business, WHICEB 2018

17th Wuhan International Conference on E-Business, WHICEB 20...

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17th Wuhan International conference on E-Business, WHICEB 2018

The proceedings contain 74 papers. The special focus in this conference is on E-Business. The topics include: e-Control of Online Customers: When Companies Make Customers Compliant … Lessons for 21st Century China;A Bibliometric Review of Research Trends in Social CRM;a Hybrid Model to Analyze Air Pollution Spread Scales in Xi' an and Surrounding Cities;a Net Loan Monitoring Platform for University Students Based on visual Micro-blog;a Personalization-Privacy Paradox in Usage of Mobile Health Services: A Game Theoretic Perspective;a Research about Consumer’s Usage Intention to Green Finance Products----Taking the Alipay’s Ant forest as the Example;a Research on the Influential Factors of Listing Sales Based on Online Information in Short Rental Markets;a Study on Consumers' Learning Effect in the Price Reduction Auction: a case study of Gongtianxia;a Study on Online-Shopping Opinion Leaders Based on Massive data;a Text Mining Based Approach for Mining Customer Attribute data on Undefined Quality Problem;an analysis on the Development Model of China’s County-level E-commerce;an analysis with Evolutionary Game of the Resource Sharing in Supply Chain Under Cloud Platform;An exploration of Cross-border E-commerce Consumer Feedbacks: An LDA Approach;assessing Customer-Based Brand Equity of E-Commerce Platform Based on Promotion Benefits;can the Adjustment of Consumer Finance Credit Line Cause Impulse Purchase?----An analysis from the Perspective of E-commerce Promotion;considering correlation retarded growth for personalized recommendation in social tagging;consumer Coupon Redemption Behavior Prediction on B2C E-commerce;determinants of online sellers’ advertising: An empirical study on Chinese C2C market.

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Identification of haptic exploration procedures over textile surfaces with a Leap Motion Controller 13

Identification of haptic exploration procedures over textile...

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13th Biennial Norddesign conference, NordDesign 2018

作者： Gussen, Lars C. Ellerich, Max Schmitt, Robert H. Werkzeugmaschinenlabor der RWTH Aachen Department Quality Intelligence Aachen Germany Werkzeugmaschinenlabor der RWTH Aachen Department Quality Intelligence Aachen Germany Werkzeugmaschinenlabor der RWTH Aachen Production Metrology and Quality Management Aachen Germany

ISBN: (纸本)9789176851852

The development of high-quality products, which simultaneously address the customer's needs, is a key challenge for companies nowadays. Besides features and technology a customer assesses a product by its sensory characteristics, i.e. primarily on the basis of visual, acoustics and haptic perception. Although the general importance of the sensory design of products has been recognized by industry, methodical aspects regarding the realization thereof are still insufficient in certain areas. Besides e.g. the simulation of sensory perception, the reproduction of customers' habitus to sensorily approach and explore products is still not fully understood. In this context, especially the manner of haptic exploration of product surfaces plays an important role during the overall assessment of quality. Knowing the haptic exploration is necessary for the technical replication of customers' perception e.g. with special haptic sensors. Haptic characteristics of products or materials are explored performing specific movements of the hands, so-called exploration procedures. An exploration procedure is a movement pattern, which is motivated by the object properties such as shape, size and surface. The aim of the work is to devise an automatic system, which is able to record specific surface exploratory procedures and to effectively identify representative gestures by means of machine learning. It is assumed that customers explore surfaces in their own way;however, the gestures they use are similar between customers to a great extent and can therefore be clustered into homogeneous groups of gestures. For recording the exploratory movements and the human surface interaction, a Leap Motion Controller by the American company Leap Motion, Inc. is applied. To investigate the usability of the Leap Motion Controller for the intended aim, two empirical studies are conducted by asking subjects to explore a textile surface. The extracted data from the controller of the first study is used t

关键词： Textiles

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Gaussian Cubes: Real-Time Modeling for visual exploration of Large Multidimensional datasets

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2017年第1期23卷 681-690页

作者： Wang, Zhe Ferreira, Nivan Wei, Youhao Bhaskar, Aarthy Sankari Scheidegger, Carlos Univ Arizona Tucson AZ 85721 USA Univ Fed Pernambuco Recife PE Brazil

Recently proposed techniques have finally made it possible for analysts to interactively explore very large datasets in real time. However powerful, the class of analyses these systems enable is somewhat limited: specifically, one can only quickly obtain plots such as histograms and heatmaps. In this paper, we contribute Gaussian Cubes, which significantly improves on state-of-the-art systems by providing interactive modeling capabilities, which include but are not limited to linear least squares and principal components analysis (PCA). The fundamental insight in Gaussian Cubes is that instead of precomputing counts of many data subsets (as state-of-the-art systems do), Gaussian Cubes precomputes the best multivariate Gaussian for the respective data subsets. As an example, Gaussian Cubes can fit hundreds of models over millions of data points in well under a second, enabling novel types of visual exploration of such large datasets. We present three case studies that highlight the visualization and analysis capabilities in Gaussian Cubes, using earthquake safety simulations, astronomical catalogs, and transportation statistics. The dataset sizes range around one hundred million elements and 5 to 10 dimensions. We present extensive performance results, a discussion of the limitations in Gaussian Cubes, and future research directions.

关键词： data modeling dimensionality reduction interactive visualization data cubes

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Vision-Based Autonomous Underwater Swimming in Dense Coral for Combined Collision Avoidance and Target Selection

Vision-Based Autonomous Underwater Swimming in Dense Coral f...

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25th IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Manderson, Travis Higuera, Juan Camilo Gamboa Cheng, Ran Dudek, Gregory McGill Univ Sch Comp Sci Mobile Robot Lab Montreal PQ Canada

ISBN: (纸本)9781538680940

We address the problem of learning vision-based, collision-avoiding, and target-selecting controllers in 3D, specifically in underwater environments densely populated with coral reefs. Using a highly maneuverable, dynamic, six-legged (or flippered) vehicle to swim underwater, we exploit real time visual feedback to make close-range navigation decisions that would be hard to achieve with other sensors. Our approach uses computer vision as the sole mechanism for both collision avoidance and visual target selection. In particular, we seek to swim close to the reef to make observations while avoiding both collisions and barren, coral-deprived regions. To carry out path selection while avoiding collisions, we use monocular image data processed in real time. The proposed system uses a convolutional neural network that takes an image from a forward-facing camera as input and predicts unscaled and relative path changes. The network is trained to encode our desired obstacle-avoidance and reef-exploration objectives via supervised learning from human-labeled data. The predictions from the network are transformed into absolute path changes via a combination of a temporally-smoothed proportional controller for heading targets and a low-level motor controller. This system enables safe and autonomous coral reef navigation in underwater environments. We validate our approach using an untethered and fully autonomous robot swimming through coral reef in the open ocean. Our robot successfully traverses 1000 m of the ocean floor collision-free while collecting close-up footage of coral reefs.

关键词： Navigation Cameras Robot vision systems Task analysis visualization Neural networks

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Multi-Granular Trend Detection for Time-Series analysis

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2017年第1期23卷 661-670页

作者： van Goethem, Arthur Staals, Frank Loffler, Maarten Dykes, Jason Speckmann, Bettina TU Eindhoven Eindhoven Netherlands Aarhus Univ MADALGO DK-8000 Aarhus C Denmark Univ Utrecht NL-3508 TC Utrecht Netherlands City Univ London London England

Time series (such as stock prices) and ensembles (such as model runs for weather forecasts) are two important types of one-dimensional time-varying data. Such data is readily available in large quantities but visual analysis of the raw data quickly becomes infeasible, even for moderately sized data sets. Trend detection is an effective way to simplify time-varying data and to summarize salient information for visual display and interactive analysis. We propose a geometric model for trend-detection in one-dimensional time-varying data, inspired by topological grouping structures for moving objects in two-or higher-dimensional space. Our model gives provable guarantees on the trends detected and uses three natural parameters: granularity, support-size, and duration. These parameters can be changed on-demand. Our system also supports a variety of selection brushes and a time-sweep to facilitate refined searches and interactive visualization of (sub-)trends. We explore different visual styles and interactions through which trends, their persistence, and evolution can be explored.

关键词： Interactive exploration Trend Detection Time Series

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