ExpanDrogram: Dynamic Visualization of Big Data Segmentation over Time

A. Khalemsky, R. Gelbard

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number11
JournalJournal of Data and Information Quality
Issue number2
StatePublished - 13 May 2021

Bibliographical note

Publisher Copyright:
© 2021 Association for Computing Machinery.


This work was supported in part by a grant from the MAGNET program of the Israeli Innovation Authority. We also thank Hadassah Academic College for their support. Authors. address: A. Khalemsky and R. Gelbard, Information Systems Program, Graduate School of Business Administration, Bar-Ilan University, Ramat Gan, Israel; emails: [email protected], [email protected], [email protected]. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. © 2021 Association for Computing Machinery. 1936-1955/2021/05-ART11 $15.00 https://doi.org/10.1145/3434778

FundersFunder number
Israeli Innovation Authority


    • Dynamic segmentation
    • cluster analysis
    • dendrogram
    • taxonomy
    • temporal data visualization
    • version control


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