AI, e-government, and politics 2.0

Hsinchun Chen; Paul Jen Hwa Hu; Han Fen Hu; Eric Liluan Chu; Fang Ming Hsu

doi:10.1109/MIS.2009.91

AI, e-government, and politics 2.0

Hsinchun Chen, Paul Jen Hwa Hu, Han Fen Hu, Eric Liluan Chu, Fang Ming Hsu

Management Information Systems

Research output: Contribution to journal › Article › peer-review

36 Scopus citations

Original language	English (US)
Pages (from-to)	64-86
Number of pages	23
Journal	IEEE Intelligent Systems
Volume	24
Issue number	5
DOIs	https://doi.org/10.1109/MIS.2009.91
State	Published - 2009

ASJC Scopus subject areas

Computer Networks and Communications
Artificial Intelligence

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10.1109/MIS.2009.91

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@article{fb2c55a299904d65b38978335514ef2e,

title = "AI, e-government, and politics 2.0",

author = "Hsinchun Chen and Hu, {Paul Jen Hwa} and Hu, {Han Fen} and Chu, {Eric Liluan} and Hsu, {Fang Ming}",

note = "Funding Information: I greatly appreciate some of the materials presented and discussions that occurred in my recent Knowledge Management class at the University of Arizona. Special thanks to students Adam Aronoff, Ben Gray, Pres-ton Troy, Pooja Bhandari, and Clayton Badeaux. I also acknowledge the previous funding support provided by the NSF Digital Government program for my COPLINK research (“COPLINK Center: Information and Knowledge Management for Law Enforcement,” EAI-9983304, 2003–2008). Funding Information: This research was supported by NSF grant IIS-0704210. Any opinions, findings, conclusions, or recommendations expressed in this essay are of the authors, and do not necessarily reflect those of the sponsor. Funding Information: We gratefully acknowledge the work of Marc Stiegliz and Feifei Pan of the Georgia Institute of Technology on the Georgia Tech Hydrology Model (GTHM), and their permission for our using that model. The visual analytics work is funded by the US National Science Foundation (grants BIO-0417311, CISE-0417311, and CISE-0639588). We also acknowledge the work of Fred Martin and Edward Keeley of the Washington State Department of Natural Resources in our prior study of visual analytics to repurpose ecological research data for managers. The information in this document has been funded in part by the US Environmental Protection Agency. It has been subjected to the Agency{\textquoteright}s peer and administrative review and approved for publication. The conclusions and opinions are solely those of the authors and are not necessarily the views of the Agency. Data and ideas contained in this essay were also provided in part by the HJ Andrews Experimental Forest research program, funded by the National Science Foundation{\textquoteright}s Long-Term Ecological Research Program (DEB 08-23380), US Forest Service Pacific Northwest Research Station, and Oregon State University.",

year = "2009",

doi = "10.1109/MIS.2009.91",

language = "English (US)",

volume = "24",

pages = "64--86",

journal = "IEEE Intelligent Systems and Their Applications",

issn = "1541-1672",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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TY - JOUR

T1 - AI, e-government, and politics 2.0

AU - Chen, Hsinchun

AU - Hu, Paul Jen Hwa

AU - Hu, Han Fen

AU - Chu, Eric Liluan

AU - Hsu, Fang Ming

N1 - Funding Information: I greatly appreciate some of the materials presented and discussions that occurred in my recent Knowledge Management class at the University of Arizona. Special thanks to students Adam Aronoff, Ben Gray, Pres-ton Troy, Pooja Bhandari, and Clayton Badeaux. I also acknowledge the previous funding support provided by the NSF Digital Government program for my COPLINK research (“COPLINK Center: Information and Knowledge Management for Law Enforcement,” EAI-9983304, 2003–2008). Funding Information: This research was supported by NSF grant IIS-0704210. Any opinions, findings, conclusions, or recommendations expressed in this essay are of the authors, and do not necessarily reflect those of the sponsor. Funding Information: We gratefully acknowledge the work of Marc Stiegliz and Feifei Pan of the Georgia Institute of Technology on the Georgia Tech Hydrology Model (GTHM), and their permission for our using that model. The visual analytics work is funded by the US National Science Foundation (grants BIO-0417311, CISE-0417311, and CISE-0639588). We also acknowledge the work of Fred Martin and Edward Keeley of the Washington State Department of Natural Resources in our prior study of visual analytics to repurpose ecological research data for managers. The information in this document has been funded in part by the US Environmental Protection Agency. It has been subjected to the Agency’s peer and administrative review and approved for publication. The conclusions and opinions are solely those of the authors and are not necessarily the views of the Agency. Data and ideas contained in this essay were also provided in part by the HJ Andrews Experimental Forest research program, funded by the National Science Foundation’s Long-Term Ecological Research Program (DEB 08-23380), US Forest Service Pacific Northwest Research Station, and Oregon State University.

PY - 2009

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AI, e-government, and politics 2.0

ASJC Scopus subject areas

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