adplus-dvertising

Welcome to the Big Textual Data Analytics and Knowledge Management MCQs Page

Dive deep into the fascinating world of Big Textual Data Analytics and Knowledge Management with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Big Textual Data Analytics and Knowledge Management, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Big Textual Data Analytics and Knowledge Management, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Big Data Computing.

frame-decoration

Check out the MCQs below to embark on an enriching journey through Big Textual Data Analytics and Knowledge Management. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Big Data Computing.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Big Textual Data Analytics and Knowledge Management. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Big Textual Data Analytics and Knowledge Management MCQs | Page 10 of 11

Explore more Topics under Big Data Computing

Q91.
What are the two essential phases in the knowledge life cycles according to Firestone's approach to knowledge management?
Discuss
Answer: (b).Knowledge generation and knowledge dissemination Explanation:According to Firestone's approach, the two essential phases in the knowledge life cycles are knowledge generation and knowledge dissemination.
Discuss
Answer: (c).Information acquisition and learning Explanation:Knowledge production activities include information acquisition (integrating information from various sources) and learning (generating and testing hypotheses relevant to the problem to be solved).
Q93.
What is the key task of knowledge production in the overall knowledge life cycle?
Discuss
Answer: (c).Producing codified knowledge claims Explanation:The key task of knowledge production in the overall knowledge life cycle is producing codified knowledge claims.
Discuss
Answer: (c).It becomes a knowledge asset. Explanation:When a validated knowledge claim is adopted, it means that the knowledge produced becomes a knowledge asset or part of the organization's intellectual capital.
Discuss
Answer: (b).Searching and knowledge sharing Explanation:Knowledge integration processes in Firestone's approach include searching and knowledge sharing.
Q96.
In a business context, how can unstructured information processing contribute to the knowledge production phase of the knowledge life cycle?
Discuss
Answer: (c).By aiding in information extraction on the relational level Explanation:Unstructured information processing can contribute to the knowledge production phase by aiding in information extraction on the relational level, helping to formulate meaningful sentences and gathering evidence for or against hypotheses.
Discuss
Answer: (c).Dynamic reorganization of textual material Explanation:Content syndication in knowledge integration processes offers dynamic reorganization of textual material with respect to the needs of specific interests and user communities.
Discuss
Answer: (c).By identifying topics of interest and proximity relationships Explanation:Text clustering can contribute to the knowledge production phase by identifying topics of interest and estimating their proximity and interrelationships, helping to formulate specific hypotheses relevant to the knowledge life cycle.
Discuss
Answer: (b).Monitoring and control over knowledge transfer Explanation:Unstructured information processing can offer monitoring and control over knowledge transfer, making it more effective for teaching purposes.
Discuss
Answer: (b).By enabling better searching and retrieving processes Explanation:Unstructured information processing can enhance knowledge dissemination by enabling better searching and retrieving processes, allowing more comprehensive sets of knowledge resources to be integrated.

Suggested Topics

Are you eager to expand your knowledge beyond Big Data Computing? We've curated a selection of related categories that you might find intriguing.

Click on the categories below to discover a wealth of MCQs and enrich your understanding of Computer Science. Happy exploring!