Affiliation:
1. Information Systems Program, Graduate School of Business Administration, Bar-Ilan University, Ramat Gan, Israel
Abstract
In dynamic and big data environments the visualization of a segmentation process over time often does not enable the user to simultaneously track entire pieces. The key points are sometimes incomparable, and the user is limited to a static visual presentation of a certain point. The proposed visualization concept, called ExpanDrogram, is designed to support dynamic classifiers that run in a big data environment subject to changes in data characteristics. It offers a wide range of features that seek to maximize the customization of a segmentation problem. The main goal of the ExpanDrogram visualization is to improve comprehensiveness by combining both the individual and segment levels, illustrating the dynamics of the segmentation process over time, providing “version control” that enables the user to observe the history of changes, and more. The method is illustrated using different datasets, with which we demonstrate multiple segmentation parameters, as well as multiple display layers, to highlight points such as new trend detection, outlier detection, tracking changes in original segments, and zoom in/out for more/less detail. The datasets vary in size from a small one to one of more than 12 million records.
Funder
MAGNET program of the Israeli Innovation Authority
Hadassah Academic College
Publisher
Association for Computing Machinery (ACM)
Subject
Information Systems and Management,Information Systems
Reference63 articles.
1. Shape outlier detection and visualization for functional data: The outliergram;Arribas-Gil Ana;Biostatistics,2014
2. A taxonomy and survey of dynamic graph visualization;Beck Fabian;Comput. Graph. Forum,2017
3. Nikos Bikakis. 2018. Big data visualization tools. arXiv:1801.08336. Retrieved from http://arxiv.org/abs/1801.08336. Nikos Bikakis. 2018. Big data visualization tools. arXiv:1801.08336. Retrieved from http://arxiv.org/abs/1801.08336.
4. Decision-making method using a visual approach for cluster analysis problems: Indicative classification algorithms and grouping scope;Bittmann Ran;Expert Syst.,2007