Big Data Visualisation and Visual Analytics for Music Data Mining

被引:36
作者
Barkwell, Katrina E. [1 ]
Cuzzocrea, Alfredo [2 ]
Leung, Carson K. [1 ]
Ocran, Ashley A. [1 ]
Sanderson, Jennifer M. [1 ]
Stewart, James Ayrton [1 ]
Wodi, Bryan H. [1 ]
机构
[1] Univ Manitoba, Dept Comp Sci, Winnipeg, MB, Canada
[2] Univ Trieste, Dept Engn & Architecture DIA, Trieste, TS, Italy
来源
2018 22ND INTERNATIONAL CONFERENCE INFORMATION VISUALISATION (IV) | 2018年
基金
加拿大自然科学与工程研究理事会;
关键词
big data; data visualisation; visual analytics; visualiser; frequent patterns; musical data; music data analytics; music data mining; ASSOCIATION RULES;
D O I
10.1109/iV.2018.00048
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
As high volumes of a wide variety of valuable data of different veracities can be easily generated or collected at a high velocity nowadays, big data visualisation and visual analytics are in demand in various real-life applications. Musical data are examples of big data. Embedded in these big data are useful information and valuable knowledge. Many existing big data mining algorithms return useful information and valuable knowledge in textual or tabular forms. Knowing that "a picture is worth a thousand words", big data visualisation and visual analytics are also in demand. In this paper, we present a system for visualising and analysing big data. In particular, our system focuses on the big data science task of the discovery and exploration of frequent patterns (i.e., collections of items that frequently occurring together) from musical data. Evaluation results show the applicability of our system in big data visualisation and visual analytics for music data mining.
引用
收藏
页码:235 / 240
页数:6
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