TY - GEN
T1 - Growing Process of Communities on Data Platforms
T2 - 2021 IEEE International Conference on Big Data, Big Data 2021
AU - Hayashi, Teruaki
AU - Shimizu, Takumi
AU - Fukami, Yoshiaki
AU - Sakaji, Hiroki
AU - Matsushima, Hiroyasu
N1 - Funding Information:
ACKNOWLEDGMENT This study was supported by JSPS KAKENHI (JP20H02384). We wish to thank Editage for providing English language editing.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In recent years, there have been growing expectations for the creation of new businesses and the improvement of the value of existing services by exchanging data in different fields. Data stored in-house within organizations have become a new source of innovation. While there is a high need for the value creation of data, determining the data value is not an easy task, as there is a wide range of factors to be considered, such as data pricing, acquisition cost, usage value, and update frequency. In this study, we observe communication, such as the sharing of know-hows in data exchange and analysis, and discuss the growing process of a community on the data platform. For the experiment, we focused on the data community in the COVID-19 disaster and used a unique dataset from the data platform Kaggle, which is the data analysis competition service. The results suggest that user actions differ in the discussion of the dataset and analysis. Moreover, providing topics, user participation, and activating actions in the early stages after the dataset is released are essential for forming a data community. We argue that the actions on the data analysis, such as comments and votes, are also crucial for fostering a common understanding of the data value.
AB - In recent years, there have been growing expectations for the creation of new businesses and the improvement of the value of existing services by exchanging data in different fields. Data stored in-house within organizations have become a new source of innovation. While there is a high need for the value creation of data, determining the data value is not an easy task, as there is a wide range of factors to be considered, such as data pricing, acquisition cost, usage value, and update frequency. In this study, we observe communication, such as the sharing of know-hows in data exchange and analysis, and discuss the growing process of a community on the data platform. For the experiment, we focused on the data community in the COVID-19 disaster and used a unique dataset from the data platform Kaggle, which is the data analysis competition service. The results suggest that user actions differ in the discussion of the dataset and analysis. Moreover, providing topics, user participation, and activating actions in the early stages after the dataset is released are essential for forming a data community. We argue that the actions on the data analysis, such as comments and votes, are also crucial for fostering a common understanding of the data value.
KW - case analysis
KW - data ecosystem
KW - data exchange
KW - data marketplace
KW - online community
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U2 - 10.1109/BigData52589.2021.9671535
DO - 10.1109/BigData52589.2021.9671535
M3 - Conference contribution
AN - SCOPUS:85125349672
T3 - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
SP - 3466
EP - 3471
BT - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
A2 - Chen, Yixin
A2 - Ludwig, Heiko
A2 - Tu, Yicheng
A2 - Fayyad, Usama
A2 - Zhu, Xingquan
A2 - Hu, Xiaohua Tony
A2 - Byna, Suren
A2 - Liu, Xiong
A2 - Zhang, Jianping
A2 - Pan, Shirui
A2 - Papalexakis, Vagelis
A2 - Wang, Jianwu
A2 - Cuzzocrea, Alfredo
A2 - Ordonez, Carlos
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 December 2021 through 18 December 2021
ER -