Efficient Framework of e-Government for Mining Knowledge from Massive Grievance Redressal Data

Sangeetha G, Manjunatha. L Rao
<span title="2015-05-30">2015</span> <i title="Tejass Publisheers"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ezm4lr6uezhi5pfgr6xkdexmgy" style="color: black;">IJARCCE</a> </i> &nbsp;
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
more &raquo; ... 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17148/ijarcce.2015.45142">doi:10.17148/ijarcce.2015.45142</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/slly6h36yvarfnnjscfk4o7tli">fatcat:slly6h36yvarfnnjscfk4o7tli</a> </span>
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