The IUP Journal of Information Technology
Reshaping the Paradox of Information Power and Overload: Data Mining Strategies for Knowledge Workers

Article Details
Pub. Date : Mar, 2021
Product Name : The IUP Journal of Information Technology
Product Type : Article
Product Code : IJIT20321
Author Name :Umesh Arya
Availability : YES
Subject/Domain : Engineering
Download Format : PDF Format
No. of Pages : 20

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Abstract

Researchers and journalists comprise a good part of the "knowledge workers", i.e., the profession where most of the people are engaged in the knowledge intensive industries. The digital workplace offers unprecedented opportunities, while decimating the strongly guarded 'traditional' tower of working and learning. The same is quite evident with huge disruptions in industry and academia. The tsunami of information overload has created different power relations and negotiated hierarchies in scholarship, citizenship and governance. Different actors are now asserting their identities and influencing the knowledge sphere with their tech-enabled approach to data mining and info scraping. The data, being the "new oil" of information economy, seem to lubricate the knowledge sphere for enhanced understanding, mapping and better feeling of the events happenings day in and day out. The paper dwells on the concept of information overload, data retrieval strategies and finally the data mining or the data scraping strategies.


Description

"Information is power" concept has been doing the rounds in academia and field for an extended period of time in the human history. Since time immemorial, academicians have taught the students to gain as much possible access to information sources. ICTs (Information and Communication Technologies) have emerged as a true agent of information dissemination. "A new technology does not add or subtract something. It changes everything." It changes the structures of our interests, arena of thought development, meaning of symbols and the nature of community (Postman, 1982).


Keywords

Information overload, Data harvesting, Data mining, Boolean search

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