Name
Abstract | ||
|---|---|---|
In the current era of big data, the advancement in data generation and management has created an avenue for decision makers to utilize these huge data collected from many data-driven application domains for different purposes. Big data science enables application developers and data scientists to utilize these big data, to learn more about the data, and then to explore and model hidden features for analysis purposes. In this paper, we present a data science solution to support social and economic analysis. Our solution makes good use of data mining techniques to cluster similar data, analyze time series, find frequent patterns, reveal interesting associations, and visualize these relationships. We evaluate our solution with two sets of real-life employment data. Our solution utilizes employment data to support social and economic analysis. It enables users to explore and discover implicit, previously unknown information and useful knowledge from the data. This, in turn, can enable the decision makers to take appropriate actions for social good and/or economic benefits. As an example, it reveals to job seekers some interesting characteristics of different data-related jobs, which helps them to find jobs that match better with their needs and profiles. As another example, it also reveals to social scientists and economists impacts of COVID-19 to the job markets, which helps them to get a better understanding of social and economic situations at the COVID-19 pandemic era and plan for the post-pandemic era. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/COMPSAC51774.2021.00252 | 2021 IEEE 45TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2021) |
Keywords | DocType | ISSN |
data science, data-driven applications, social analysis, economic analysis, time series analysis, employment data analytics, visual analytics, COVID-19, data mining, frequent pattern mining | Conference | 0730-3157 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yubo Chen | 1 | 313 | 28.78 |
| Carson K. Leung | 2 | 1625 | 115.64 |
| Hao Li | 3 | 25 | 11.35 |
| Siyuan Shang | 4 | 5 | 1.44 |
| Wanmeng Wang | 5 | 0 | 0.34 |
| Zhi Zheng | 6 | 0 | 0.34 |