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Modelling of E-Governance Framework for Mining Knowledge from Massive Grievance Redressal Data

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 6 No. 1 (2016)

DOI
10.11591/ijece.v6i1.9019
ISSN
2088-8708, 2088-8708
Published
2016-02-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc-nd/4.0

Abstract

With the massive proliferation of online applications for the citizens with abundant resources, there is a tremendous hike in usage of e-governance platforms. Right from entrepreneur, players, politicians, students, or anyone who are highly depending on web-based grievance redressal networking sites, which generates loads of massive grievance data that are not only challenging but also highly impossible to understand. The prime reason behind this is grievance data is massive in size and they are highly unstructured. Because of this fact, the proposed system attempts to understand the possibility of performing knowledge discovery process from grievance Data using conventional data mining algorithms. Designed in Java considering massive number of online e-governance framework from civilian’s grievance discussion forums, the proposed system evaluates the effectiveness of performing datamining for Big data.

Collaboration network of the authors

5 authors · 4 co-authorship links

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  • Bharathiar University
  • Bangalore University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
N. MadhusudanBangalore University3
Salini SureshBharathiar University1
G. SangeethaBharathiar University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Modelling of E-Governance Framework for Mining Knowledge from Massive Grievance Redressal Data</dc:title><dc:creator>G, Sangeetha</dc:creator><dc:creator>Rao, L. Manjunatha</dc:creator><dc:description>&lt;jats:p&gt;With the massive proliferation of online applications for the citizens with abundant resources, there is a tremendous hike in usage of e-governance platforms. Right from entrepreneur, players, politicians, students, or anyone who are highly depending on web-based grievance redressal networking sites, which generates loads of massive grievance data that are not only challenging but also highly impossible to understand. The prime reason behind this is grievance data is massive in size and they are highly unstructured. Because of this fact, the proposed system attempts to understand the possibility of performing knowledge discovery process from grievance Data using conventional data mining algorithms. Designed in Java considering massive number of online e-governance framework from civilian’s grievance discussion forums, the proposed system evaluates the effectiveness of performing datamining for Big data.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2016-02-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v6i1.9019</dc:identifier><dc:identifier>10.11591/ijece.v6i1.9019</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 6 No 1 (2016); 367</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://www.iaescore.com/journals/index.php/IJECE/article/viewFile/116/132</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0</dc:rights></oai_dc:dc>