Demo Data from EDBT 2026 / reviewers in the wild / expert
Kunal Samant
dblp:294/8270
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% | |
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining › structured data mining › graph mining
community detection |
0.5 | 1 | 2021 | CoWiz: Interactive Covid-19 Visualization Based On Multilayer Network Analysis · ICDE 2021 |
Data mining › structured data mining › graph mining
multi-layer graph |
0.5 | 1 | 2021 | CoWiz: Interactive Covid-19 Visualization Based On Multilayer Network Analysis · ICDE 2021 |
Visualization and visual analytics
interactive visualization |
0.5 | 1 | 2021 | CoWiz: Interactive Covid-19 Visualization Based On Multilayer Network Analysis · ICDE 2021 |
Computational social science and digital humanities
demographic data analysis |
0.1 | 1 | 2021 | CoWiz: Interactive Covid-19 Visualization Based On Multilayer Network Analysis · ICDE 2021 |
Methods — techniques the papers use, named apart from their topics
community detection · 1.5centrality analysis · 1.5multilayer network analysis · 1.0multi-layer network analysis · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | CoWiz: Interactive Covid-19 Visualization Based On Multilayer Network AnalysisabstractCovid Wizard or CoWiz is a Covid-19 visualization dashboard based on Multilayer Network (MLN) analysis underneath1. Online dashboards typically plot/visualize statistical information gleaned from raw data, such as daily cases, deaths, recoveries, tests, etc. However, for a better understanding, we need aggregate analysis (e.g., community, centrality) and its visualization which is the purpose of CoWiz. As an example, grouping counties across a country/region based on similarity of increase/decrease in cases, deaths, hospitalizations over intervals is not possible without aggregate analysis. This is where CoWiz utilizes community and other concepts over MLNs that are inferred from Covid and other relevant data sets for visualization.This demo presents a flexible, interactive dashboard which is capable of visualizing various aspects of Covid-19 data, including composition of Covid data with demographics (population density, education level, average earning, vehicle movements, and change in purchase patterns) at the granularity of county for USA. This paper elaborates on the types of analysis, underlying model, and how a flexible visualization dashboard has been developed using open source software and data sets. As new data becomes available, they can be incorporated into the visualization with no manual intervention. Kunal Samant, Endrit Memeti, Abhishek Santra, Enamul Karim, Sharma Chakravarthy |
ICDE | 1 |