2018 22nd International Conference Information Visualisation (IV)最新文献_第2页

Temporal Visualization of Sets and Their Relationships Using Time-Sets 集的时间可视化及其使用时间集的关系

2018 22nd International Conference Information Visualisation (IV) Pub Date : 2018-07-01 DOI: 10.1109/iV.2018.00025

M. Masoodian, Laura Koivunen

引用次数: 5

Concept as a Bridge between Abstraction and Concretization in Design Knowledge Visualization 概念是设计知识可视化中抽象与具体化的桥梁

2018 22nd International Conference Information Visualisation (IV) Pub Date : 2018-07-01 DOI: 10.1109/iV.2018.00076

B. Eilouti

引用次数: 3

Data-Driven Logotype Design 数据驱动的标识设计

2018 22nd International Conference Information Visualisation (IV) Pub Date : 2018-07-01 DOI: 10.1109/iV.2018.00022

Jéssica Parente, T. Martins, J. Bicker

引用次数: 3

Visual Analytics for Decomposing Temporal Event Series of Production Lines 生产线时间事件系列分解的可视化分析

2018 22nd International Conference Information Visualisation (IV) Pub Date : 2018-07-01 DOI: 10.1109/iV.2018.00051

Dominik Herr, Fabian Beck, T. Ertl

{"title":"Visual Analytics for Decomposing Temporal Event Series of Production Lines","authors":"Dominik Herr, Fabian Beck, T. Ertl","doi":"10.1109/iV.2018.00051","DOIUrl":"https://doi.org/10.1109/iV.2018.00051","url":null,"abstract":"The temporal analysis of events in a production line helps manufacturing experts get a better understanding of the line’s performance and provides ideas for improvement. Especially the identification of recurring error patterns is important, because these patterns can be an indicator of systematic production issues. We present a visual analytics approach to analyze event reports of a production line. Reported events are shown as a time series plot that can be decomposed into a trend, seasonal, and remainder component by applying Seasonal Trend decomposition using Loess (STL). To find specific event patterns, the data is filtered based on aspects such as the event description or the processed product. Identified temporal patterns can be extracted from the original event series and compared visually with each other. In addition to predefined settings, experts can define a subseries of the event series and the period length of STL’s seasonal component through an automatically optimized brushing of the undecomposed plot. We developed the approach together with an industry partner. To evaluate our approach, we conducted two pair analytics sessions with our industry partner’s experts. We demonstrate use cases from these sessions that showcase our approach’s analytical potential. Moreover, we present general expert feedback that we collected through semi-structured interviews after the pair analytics sessions.","PeriodicalId":312162,"journal":{"name":"2018 22nd International Conference Information Visualisation (IV)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130766704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Volume-Based Large Dynamic Graph Analytics 基于体积的大型动态图形分析

2018 22nd International Conference Information Visualisation (IV) Pub Date : 2018-07-01 DOI: 10.1109/iV.2018.00045

Valentin Bruder, Marcel Hlawatsch, S. Frey, Michael Burch, D. Weiskopf, T. Ertl

引用次数: 7

Interactive Network Visualization of Gene Expression Time-Series Data 基因表达时序数据的交互式网络可视化

2018 22nd International Conference Information Visualisation (IV) Pub Date : 2018-07-01 DOI: 10.1109/iV.2018.00105

António Cruz, Joel P. Arrais, P. Machado

{"title":"Interactive Network Visualization of Gene Expression Time-Series Data","authors":"António Cruz, Joel P. Arrais, P. Machado","doi":"10.1109/iV.2018.00105","DOIUrl":"https://doi.org/10.1109/iV.2018.00105","url":null,"abstract":"Visualization models have shown to be remarkably important in the interpretation of datasets across many fields of study. In the field of Biology, data visualization is used to better understand processes that range from phylogenetic trees to multiple layers of molecular networks. The latter is especially challenging due to the large quantities of varying elements and complex relationships, often with no perceptible structure. Although various tools have been proposed to improve the visualization of molecular networks, many challenges still persist. In this paper, we propose a tool that uses interactive visualization models to represent the dynamic behaviors of molecular networks. The tool employs various methods to explore and organize the data, including clustering, force-directed layouts, and a timeline for navigating through time-series data. To further analyze temporal attributes, the timeline can be distorted through a force-directed layout to spatially position time points according to their similarity. Additionally, gene expression can be annotated through an integrated biological database. The visualization model was validated with the use of time-series gene expression RNA-Seq data from the HIV-1 infection.","PeriodicalId":312162,"journal":{"name":"2018 22nd International Conference Information Visualisation (IV)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121840425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

The Branching Data Model, the Foundation for Automated Tree Visualization 分支数据模型，自动树可视化的基础

2018 22nd International Conference Information Visualisation (IV) Pub Date : 2018-07-01 DOI: 10.1109/iV.2018.00016

H. P. Zellweger

{"title":"The Branching Data Model, the Foundation for Automated Tree Visualization","authors":"H. P. Zellweger","doi":"10.1109/iV.2018.00016","DOIUrl":"https://doi.org/10.1109/iV.2018.00016","url":null,"abstract":"The paper presents the newly discovered Branching Data Model; it is a building block for the tree structure. This arrangement of data originates in the relational table by formalizing the so-called parent-child data relations located between two attributes. A well-defined SQL SELECT statement, motivated by theoretical mathematics, establishes a uniform pattern of data relations in the table that has a tree structure. This query both exposes these data relations as well as models them. Program logic operates on these models to enable tree structures to expand and grow computationally. Labels on the nodes of these trees allow end-users to visualize the table's data content. An early form of artificial intelligence (AI) generalizes this data relationship beyond the database table. It uses brute force to verify that another type of Branching Data Model exists between the tables in a database system. The former type of data model is a conceptual model; it organizes the data contained in a database table into menu data for an end-user navigation interface. The latter is a linkage model that connects tables throughout the database system. Together, these two types of Branching Data Models enable program logic to generate database applications entirely by automation. This end-user applications are data-driven. They allow end-users to locate information in a database by visualizing its data content.","PeriodicalId":312162,"journal":{"name":"2018 22nd International Conference Information Visualisation (IV)","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122519472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Visual Search in Digital Libraries and the Usage of External Terms 数字图书馆的视觉检索与外部术语的使用

2018 22nd International Conference Information Visualisation (IV) Pub Date : 2018-07-01 DOI: 10.1109/iV.2018.00074

Arben Hajra, K. Tochtermann

引用次数: 2

A Visual Exploration of Melodic Relationships within Traditional Music Collections 传统音乐收藏中旋律关系的视觉探索

2018 22nd International Conference Information Visualisation (IV) Pub Date : 2018-07-01 DOI: 10.1109/iV.2018.00089

C. Walshaw

引用次数: 6

Time-Tunnel: 3D Visualization Tool and Its Aspects as 3D Parallel Coordinates 时间隧道:三维可视化工具及其三维平行坐标方面

2018 22nd International Conference Information Visualisation (IV) Pub Date : 2018-07-01 DOI: 10.1109/iV.2018.00019

Y. Okada

{"title":"Time-Tunnel: 3D Visualization Tool and Its Aspects as 3D Parallel Coordinates","authors":"Y. Okada","doi":"10.1109/iV.2018.00019","DOIUrl":"https://doi.org/10.1109/iV.2018.00019","url":null,"abstract":"This paper treats a 3D visualization tool called Time-tunnel, especially describes its aspects as 3D Parallel Coordinates by showing its actual visualization examples. Originally, Time-tunnel is a multidimensional data visualization tool and it was extended to support Parallel Coordinates called PCTT(Parallel Coordinates version of Time-tunnel). Furthermore, as its aspects of 3D Parallel Coordinates, 2Dto2D visualization functionality was added. Although PCTT can visualize network data because IP packets consist of many attributes and such multiple-attributes data can be visualized using Parallel Coordinates, 2Dto2D visualization functionality can more effectively visualize patterns of IP packets that seem network attacks. The authors have already proposed the combinatorial use of PCTT and 2Dto2D visualization for the intrusion detection of the Internet. This paper also introduces Spline Parallel Coordinates representation as one of the new features of PCTT. The authors also proposed the use of PCTT for learning analytics by visualizing leaners' learning activity data and introduced 3D mode into PCTT to visualize each learner's learning pattern more efficiently. This 3D mode is regarded as 3D Parallel Coordinates. However, because such 3D mode was not enough to distinguish each learner's leaning pattern, the authors implemented more effective 3D mode, and clarify the usefulness of the new 3D mode by showing visualization results.","PeriodicalId":312162,"journal":{"name":"2018 22nd International Conference Information Visualisation (IV)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116713871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4