Quality matters: Understanding the impact of incomplete data on visualization recommendation

Rischan Mafrur, Mohamed A. Sharaf, Guido Zuccon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Incomplete data is a crucial challenge to data exploration, analytics, and visualization recommendation. Incomplete data would distort the analysis and reduce the benefits of any data-driven approach leading to poor and misleading recommendations. Several data imputation methods have been introduced to handle the incomplete data challenge. However, it is well-known that those methods cannot fully solve the incomplete data problem, but they are rather a mitigating solution that allows for improving the quality of the results provided by the different analytics operating on incomplete data. Hence, in the absence of a robust and accurate solution for the incomplete data problem, it is important to study the impact of incomplete data on different visual analytics, and how those visual analytics are affected by the incomplete data problem. In this paper, we conduct a study to observe the interplay between incomplete data and recommended visual analytics, under a combination of different conditions including: (1) the distribution of incomplete data, (2) the adopted data imputation methods, (3) the types of insights revealed by recommended visualizations, and (4) the quality measures used for assessing the goodness of recommendations.

Original languageEnglish
Title of host publicationDatabase and Expert Systems Applications - 31st International Conference, DEXA 2020, Proceedings
EditorsSven Hartmann, Josef Küng, Gabriele Kotsis, Ismail Khalil, A Min Tjoa
PublisherSpringer Science and Business Media Deutschland GmbH
Pages122-138
Number of pages17
ISBN (Print)9783030590024
DOIs
Publication statusPublished - 2020
Event31st International Conference on Database and Expert Systems Applications, DEXA 2020 - Bratislava, Slovakia
Duration: Sep 14 2020Sep 17 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12391 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference31st International Conference on Database and Expert Systems Applications, DEXA 2020
Country/TerritorySlovakia
CityBratislava
Period9/14/209/17/20

Keywords

  • Data exploration
  • Incomplete data
  • Visualization recommendation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint

Dive into the research topics of 'Quality matters: Understanding the impact of incomplete data on visualization recommendation'. Together they form a unique fingerprint.

Cite this