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Welcome to the Scalable End User Access to Big Data MCQs Page

Dive deep into the fascinating world of Scalable End User Access to Big Data with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Scalable End User Access to Big Data, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Scalable End User Access to Big Data, 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.

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Check out the MCQs below to embark on an enriching journey through Scalable End User Access to Big Data. 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 Scalable End User Access to Big Data. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Scalable End User Access to Big Data MCQs | Page 7 of 14

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Discuss
Answer: (c).It offers context-sensitive completion and navigation support. Explanation:The importance of providing a textual query interface with context-sensitive completion and navigation support for technically versed users dealing with complex queries.
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Answer: (c).To adapt to changing vocabularies and cater to omissions in the ontology. Explanation:The importance of supporting on-the-fly extension of the ontology to adapt to changing vocabularies and cater to omissions.
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Answer: (c).By using advanced tools and methodologies from ontology alignment. Explanation:Users can relate new vocabulary to existing ontology terms by using advanced tools and methodologies from ontology alignment.
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Answer: (c).By using advanced tools and methodologies from ontology alignment.Both a textual and a diagrammatic query interface. Explanation:Users can relate new vocabulary to existing ontology terms by using advanced tools and methodologies from ontology alignment.Both textual and diagrammatic query interface views should be provided, and users should ideally be able to switch between them.
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Answer: (b).To provide a global schema for data sources. Explanation:The ontology in the OBDA architecture acts as a "global schema" onto which the schemas of various data sources can be mapped.
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Answer: (b).It is a time-consuming process. Explanation:Developing ontologies from scratch is considered expensive because it is a time-consuming process.
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Answer: (d).By incorporating new vocabulary required in user queries. Explanation:In OBDA systems, ontologies can evolve by incorporating new vocabulary required in user queries.
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Answer: (c).Ensuring coherent addition of vocabulary to the ontology. Explanation:One of the challenges in managing large, evolving sets of mappings in OBDA is ensuring coherent addition of vocabulary to the ontology.
Q69.
What do ontology management tools like Protégé and NeOn primarily support?
Discuss
Answer: (c).Complex ontologies. Explanation:Ontology management tools like Protégé and NeOn primarily support the construction and maintenance of complex ontologies.
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Answer: (c).To characterize mapping languages in terms of expressive power and complexity. Explanation:The goal of comparing schema mapping languages in research is to characterize mapping languages in terms of expressive power and complexity.

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