Big-data applications in the government sector

By Gang-hoon Kim, Silvana Trimi, Ji-Hyong Chung. Communications of the ACM, 2014

592 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/ec02b379a635346ec1501e801263ea576a10ed4c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Wei Yu
2. Yin Zhang
3. Chun-Wei Tsai
4. Mohammad Mehedi Hassan
5. Stephen T. Newman
6. Partha Pratim Ray
7. George Q. Huang
8. Ray Y. Zhong

In Management Information Systems

1. Partha Pratim Ray
2. Casey G. Cegielski
3. Sven Winkelhaus
4. Eric H. Grosse

In Management Science and Operations Research

1. Wei Yu
2. Casey G. Cegielski
3. Sven Winkelhaus
4. Eric H. Grosse

Citations in the corpus

• Understanding the Factors Affecting the Organizational Adoption of Big Data (2018, background): “For example, in Kim, Trimi, and Chung ’ s article, the authors argued “ Big data also means new challenges involving complexity, security, and risks to privacy, as well as a need for new technology and human skills ” [21].”
• A Survey on Big Data Market: Pricing, Trading and Protection (2018, background): “Indeed, almost 90% of the data in the world was created in the last two years alone [19].”
• Health-CPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data (2017, background): “Electronic health records (EHRs), biomedical database, and public health have been enhanced not only on the availability and traceability but also on the liquidity of data [2].”
• Big Data and cloud computing: innovation opportunities and challenges (2017, methodology): “The variety and veracity of Big Data are redefining the data management paradigm, demanding new technologies (e.g. Hadoop, NoSQL) to clean, store, and organize unstructured data (Kim, Trimi, and Chung 2014).”
• Internet of things for smart agriculture: Technologies, practices and future direction (2017, background): “− Big data analytics: According to [28,36], big data analytics can be applied to find the inner sight of large volume of data that is generated at very high speed and belong to different genre.”
• Beyond the individual: governing AI's societal harm (2021, unknown): “AI systems can be used in the context of law enforcement, public administration or the judicial system, so as to assist public officials in their decision-making processes (Kim et al., 2014; Liu et al., 2019; Zalnieriute et al., 2019; AlgorithmWatch, 2020).”
• Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives (2016, background): “More things will get integrated, and the future looks very much like a framework which is able to store, process, visualize, and support for intelligent, advanced and timely decision-making (Koetsier, 2014).”