VARIABLE QUEST: Network Visualization of Variable Labels Unifying Co-occurrence Graphs
VARIABLE QUEST: Network Visualization of Variable Labels Unifying Co-occurrence Graphs
复制标题
变量探索：变量标签的网络可视化统一共现图
DOI：
10.1109/icdmw.2017.81
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Yukio Ohsawa
中科院分区：
文献类型：
--
作者：
Teruaki Hayashi;Yukio Ohsawa
数据夹（DJ）是一种用于共享数据信息和考虑数据集潜在价值的技术，数据本身隐藏，通过自然语言描述数据摘要。在DJ中，变量由变量标签（VL）描述，变量标签是变量的名称/含义。在先前的研究中，提出了基于矩阵的方法来推断的虚拟语言的DJ的虚拟语言是未知的，使用的文本大纲的DJ。然而，先前的方法仅显示具有与查询的相似性分数的VL的列表。此外，列表中没有显示虚拟L之间的关系，用户很难理解虚拟L之间的联系。在本文中，我们提出了变量查询，这是网络可视化系统的虚拟现实使用矩阵为基础的推理方法的虚拟现实，通过统一的共生图。VARIABLE QUEST表示DJ中VL之间的共现和频率。VL的共现是这样一种特征，即在数据中可能存在同时出现的一对高频率的VL，例如，“纬度”和“经度”，或“天气”和“温度”。“统一共现图的网络可视化不仅可以支持那些想要在数据市场中获得新数据的人，而且还可以作为帮助研究人员设计实验的工具。
Data Jacket (DJ) is a technique for sharing information about data and for considering the potential value of datasets, with the data itself hidden, by describing the summary of data in natural language. In DJs, variables are described by variable labels (VLs), which are the names/meanings of variables. In the previous study, the matrix-based method for inferring VLs in DJs whose VLs are unknown, using the texts in outlines of DJs was proposed. However, the previous method showed only the list of VLs with the similarity scores to queries. Moreover, the relationships among VLs were not displayed in the lists, which was difficult for users to understand the connections of VLs. In this paper, we propose VARIABLE QUEST which is the network visualization system of VLs using the matrix-based inferring method of VLs by unifying co-occurrence graphs. VARIABLE QUEST represents the co-occurrence and the frequency between VLs in DJs. The co-occurrence of VLs is a feature that there may be a highly frequent pair of VLs appearing at the same time in data, e.g., "latitude" and "longitude," or "weather" and "temperature." The network visualization unifying co-occurrence graphs may not only support those who want to obtain new data in the Market of Data but also function as a tool for aiding the design of experiments of the researchers.
数据夹创新者市场，用于评估、共享和综合数据
影响因子：
3.9
作者：
Y. Ohsawa;Hiroyuki Kido;Teruaki Hayashi;Chang Liu;Kazuhiro Komoda
通讯作者：
Kazuhiro Komoda
DOI：
--
发表时间：
2017
期刊：
影响因子：
--
作者：
青木輝夫;庭野匡思;谷川朋範;的場澄人;山口悟;山崎哲秀;藤田耕史;本山秀明;堀雅裕;島田利元;久世洋嗣，原康貴，作井大介，瀧野皓哉，永井敬志，松井ゆり，津村康介，平田哲夫，大久保宗則，川村一太，山田純生，松尾仁司;Yukio Ohsawa
通讯作者：
Yukio Ohsawa