TY - GEN
T1 - Quality matters
T2 - 31st International Conference on Database and Expert Systems Applications, DEXA 2020
AU - Mafrur, Rischan
AU - Sharaf, Mohamed A.
AU - Zuccon, Guido
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Data exploration
KW - Incomplete data
KW - Visualization recommendation
UR - http://www.scopus.com/inward/record.url?scp=85091479950&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091479950&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59003-1_8
DO - 10.1007/978-3-030-59003-1_8
M3 - Conference contribution
AN - SCOPUS:85091479950
SN - 9783030590024
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 122
EP - 138
BT - Database and Expert Systems Applications - 31st International Conference, DEXA 2020, Proceedings
A2 - Hartmann, Sven
A2 - Küng, Josef
A2 - Kotsis, Gabriele
A2 - Khalil, Ismail
A2 - Tjoa, A Min
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 14 September 2020 through 17 September 2020
ER -