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Mobile UX design: learning from the Flyover Country mobile app

JOURNAL OF MAPS 2021年第2期17卷 39-50页

作者： Loeffler, Shane Roth, Robert E. Goring, Simon Myrbo, Amy Univ Minnesota Dept Earth Sci Minneapolis MN USA Univ Wisconsin Cartog Lab Madison WI USA Univ Wisconsin Dept Geog Madison WI 53706 USA Univ Minnesota CSDCO LacCore Minneapolis MN USA

Technology has transformed maps into interactive tools for exploring and understanding the world. In this article, we present the user experience (UX) design process for Flyover Country (), a mobile mapping application that visualizes information about the Earth's geology and history, allowing users to save and then access the information offline while flying, driving, or hiking. Specifically, the paper presents a UX design study that tracks the conceptualization, implementation, and revision of Flyover Country, and employs scenario-based design to walk through a pair of use case scenarios that informed our UX design: science outreach to the general public and field trip guides for geoscience education. In the paper, we note UX design insights that are potentially transferable to other mobile mapping contexts, organizing insights by mobile representation design (scale, projection, symbolization, and typography) versus mobile interaction design (map entry point, bottom navigation, floating action button, and pull-up information panel).

关键词： UX design mobile maps interactive cartography location-based services big data visualization geoscience

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A Digital Twin for Maritime Situational Awareness 23

A Digital Twin for Maritime Situational Awareness

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Proceedings of the IEEE/ACM 10th International Conference on big data Computing, Applications and Technologies

作者： Alexandros Troupiotis-Kapeliaris Giannis Spiliopoulos Georgios Grigoropoulos Evangelia Filippou Ilias Chamatidis Marios Vodas Manolis Kaliorakis Dimitris Zissis MarineTraffic (Kpler) Athens Greece MarineTraffic (kpler) Athens Greece University of the Aegean 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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The Spherical Retractable Bubble Space: An Egocentric Graph visualization throughout a Retractable visualization Space

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INFORMATION 2023年第10期14卷 531页

作者： Kobina, Piriziwe Duval, Thierry Brisson, Laurent IMT Atlantique Lab STICC CNRS UMR 6285 F-29238 Brest France

In this paper, we present a new egocentric metaphor for graph visualization that consists in positioning a graph between two concentric spheres of different radii. It improves the expansion of nodes in space, contrary to 3D spatialization algorithms. The edge drawing is optimized by pushing all the edges into the area delimited by our two concentric spheres so that a user can move freely without being encumbered by edges. Our new metaphor also makes it possible to reduce the display angles in order to have a global view of the graph without leaving the egocentricity.

关键词： immersive analytics big data visualization virtual reality

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RawVis: A System for Efficient In-situ Visual Analytics 21

RawVis: A System for Efficient In-situ Visual Analytics

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ACM SIGMOD International Conference on Management of data (SIGMOD)

作者： Maroulis, Stavros Bikakis, Nikos Papastefanatos, George Vassiliadis, Panos Vassiliou, Yannis Nat Techn Univ Athens Athens Greece ATHENA Res Ctr Maroussi Greece Univ Ioannina Ioannina Greece

ISBN: (纸本)9781450383431

In-situ processing has received a great deal of attention in recent years. In in-situ scenarios, big raw data files which do not fit in main memory, must be efficiently handled on-the-fly using commodity hardware, without the overhead of a preprocessing phase or the loading of data into a database system. This paper presents RawVis, an open source data visualization system for in-situ visual exploration and analytics over big raw data. RawVis implements novel indexing schemes and adaptive processing techniques allowing users to perform efficient visual and analytics operations directly over the data files. RawVis provides real-time interaction, reporting low response time, over large data files, using commodity hardware.

关键词： big Raw data visualization Tool Adaptive and Progressive Indexing big data visualization data Exploration Visual Analytics RawVis Interactive Query Processing

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In-situ visual exploration over big raw data

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INFORMATION SYSTEMS 2021年 95卷 101616-101616页

作者： Bikakis, Nikos Maroulis, Stavros Papastefanatos, George Vassiliadis, Panos Univ Ioannina Ioannina Greece

data exploration and visual analytics systems are of great importance in Open Science scenarios, where less tech-savvy researchers wish to access and visually explore big raw data files (e.g., json, csv) generated by scientific experiments using commodity hardware and without being overwhelmed in the tedious processes of data loading, indexing and query optimization. In this paper, we present our work for enabling efficient query processing on large raw data files for interactive visual exploration scenarios and analytics. We introduce a framework, named RawVis, built on top of a lightweight in-memory tile-based index, VALINOR, that is constructed on-the-fly given the first user query over a raw file and progressively adapted based on the user interaction. We evaluate the performance of a prototype implementation compared to three other alternatives and show that our method outperforms in terms of response time, disk accesses and memory consumption. Particularly during an exploration scenario, the proposed method in most cases is about 5-10x faster compared to existing solutions, and requires significantly less memory resources. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Visual analytics Progressive & adaptive indexes User-driven incremental processing Interactive indexing RawVis In-situ query processing big data visualization

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Dependency visualization in data Stream Profiling

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big data RESEARCH 2021年 25卷

作者： Breve, Bernardo Caruccio, Loredana Cirillo, Stefano Deufemia, Vincenzo Polese, Giuseppe Univ Salerno Dept Comp Sci Via Giovanni Paolo II 132 I-84084 Fisciano SA Italy

data stream profiling concerns the automatic extraction of metadata from a data stream, without having the possibility to store it. Among the metadata of interest, functional dependencies (Fds), and their extensions relaxed functional dependencies (RFds), represent an important semantic property of data. Nowadays, there are many algorithms for automatically discovering them from static datasets, and some are being proposed for data streams. However, one of the main problems is that the stream nature of data requires a different paradigm of monitoring, since the "big" number of (R)Fds that might hold on a given dataset continuously change as new data are read from the stream. In this paper, we present a tool for visualizing RFds discovered from a data stream. The tool permits to explore results for different types of RFds, and uses quantitative measures to monitor how discovery results evolve. Moreover, the tool enables the comparison among RFds discovered across several executions, also proving visual manipulation operators to dynamically compose and filter results. A user study has been conducted to assess the effectiveness of the proposed visualization tool. (C) 2021 Elsevier Inc. All rights reserved.

关键词： data stream profiling big data visualization Metadata visualization Continuous discovery Relaxed functional dependencies

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AGAMI: Scalable Visual Analytics over Multidimensional data Streams 7

AGAMI: Scalable Visual Analytics over Multidimensional Data ...

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7th IEEE/ACM International Conference on big data Computing, Applications and Technologies (BDCAT)

作者： Lu, Mingxin Wong, Edmund Barajas, Daniel Li, Xiaochen Ogundipe, Mosopefoluwa Wilson, Nate Garg, Pragya Joshi, Alark Malensek, Matthew Univ San Francisco Dept Comp Sci San Francisco CA 94117 USA

ISBN: (纸本)9780738123967

As worldwide capability to collect, store, and manage information continues to grow, the resulting datasets become increasingly difficult to understand and extract insights from. Interactive data visualizations offers a promising avenue to efficiently navigate and gain insights from highly complex datasets, but the velocity of modern data streams often means that precomputed representations or summarizations of the data will quickly become obsolete. Our system, AGAMI, provides live-updating, interactive visualizations over streaming data. We leverage in-memory data sketches to summarize and aggregate information to be visualized, and also allow users to query future feature values by leveraging online machine learning models. Our approach facilitates low-latency, iterative exploration of data streams and can scale out incrementally to handle increasing stream velocities and query loads. We provide a thorough evaluation of our data structures and system performance using a real-world meteorological dataset.

关键词： Streaming visualization big data visualization in-memory data sketches online machine learning

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Visualizing Effects of COVID-19 Social Isolation with Residential Activity big data Sensor data 8

Visualizing Effects of COVID-19 Social Isolation with Reside...

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8th IEEE International Conference on big data (big data)

作者： Rajkumar, Anuradha Wallace, Bruce Ault, Laura Lariviere-Chartier, Julien Knoefel, Frank Goubran, Rafik Kaye, Jeff Thomas, Neil Carleton Univ Syst & Comp Engn Ottawa ON Canada Bruyere Res Inst Ottawa ON Canada SAM3 Innovat Hub Ottawa ON Canada Univ Ottawa Fac Med Ottawa ON Canada Oregon Ctr Aging & Technol ORCATECH NIA Layton Aging & Alzheimers Dis Ctr Portland OR USA

ISBN: (纸本)9781728162515

The ability to understand and visualize big data sets is of increasing interest to caregivers and clinicians as ambient home sensing can provide massive amounts of data related to the activities of residents. However, this data is only useful if it can be effectively and simply visualized for review and analysis. This paper presents the visualization of longitudinal data sets from ambient well-being sensors deployed in 3 residences that have a spousal pair dyad where 1 resident has been diagnosed with Mild Cognitive Impairment or Dementia and the spousal partner is acting as a caregiver. The paper presents the differences in activity and behaviour that can be observed in the 3 residences by comparing two 30-day periods prior to and one 30-day period during COVID-19 social isolation precautions. The work shows the potential for this circle plot based visualization technique to summarize resident activity and also to convey external factors such as the variation in solar day that can itself influence behaviour.

关键词： big data visualization ambient sensing smart home technology IoT cloud processing

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MLV-Viewer: data Mining visualizations in Decision Support System 15

MLV-Viewer: Data Mining Visualizations in Decision Support S...

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15th Iberian Conference on Information Systems and Technologies (CISTI)

作者： Oliveira Alves, Carlos Manuel Perez Cota, Manuel Gonzalez Castro, Miguel Ramon Inst Politecn Castelo Branco Castelo Branco Portugal Univ Vigo Vigo Spain

ISBN: (纸本)9789895465903

Information visualization is defined as interactive visual representations supported by the computer, in order to increase cognition. The tools and methods applied can help to accelerate the understanding of a large volume of data, improving understanding, especially with multidimensional data sets. Visual analysis methods allow decision makers to combine flexibility, creativity and human knowledge with the enormous storage and processing resources of today's computers to gain insight into complex problems. If we add data mining to advanced visual interfaces, decision makers will be able to interact directly with data analysis, adapting to their tastes and needs. The MLV-Viewer prototype, in its data mining aspect, will be described in this article.

关键词： data Mining in DSS DSS big data visualization

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Design of a Human-Computer Interaction Method for Intelligent Electric Vehicles

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WORLD ELECTRIC VEHICLE JOURNAL 2022年第10期13卷 179页

作者： Ba, Tao Li, Shan Gao, Ying Wang, Shijun Zhengzhou Univ Light Ind Sch Art & Design Zhengzhou 450002 Peoples R China Henan Agr Univ Coll Landscape Architecture & Art Zhengzhou 450002 Peoples R China Yutong Bus Co Ltd Tech Ctr Zhengzhou 450016 Peoples R China

In order to improve the satisfaction of users during the human-machine interaction with intelligent electric vehicles, this paper presents the human-machine interaction method of intelligent electric vehicles. Firstly, the principle of human-computer interaction of intelligent electric vehicles is analyzed, the application of interaction in big data visualization is expounded, and the cognitive mechanism of big data visualization interaction is designed. According to the above mechanism, the design the of information interface and the HUD interface is completed, and the interaction model is established. So far, the design of a human-computer interaction method of intelligent electric vehicles is completed. The experimental results show that the human-computer interaction response time of the design method is was only 5 ms, and the human-computer interaction satisfaction of the intelligent electric vehicle can reach 99%, which has certain application value.

关键词： interactive model human computer interaction design cognitive mechanism big data visualization

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