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Welcome to the Management of Big Semantic Data MCQs Page

Dive deep into the fascinating world of Management of Big Semantic Data with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Management of Big Semantic Data, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Management of Big Semantic 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 Management of Big Semantic 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 Management of Big Semantic Data. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Management of Big Semantic Data MCQs | Page 14 of 15

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Q131.
Why is accessing data by object challenging in HDT-FoQ?

a.

Objects are not sorted in any particular order.

b.

Objects are stored in a separate data structure.

c.

Objects are scattered throughout the sequence So.

d.

Objects are usually encoded in plain text.

Discuss
Answer: (c).Objects are scattered throughout the sequence So. Explanation:Accessing data by object in HDT-FoQ is challenging because the occurrences of objects are scattered throughout the sequence So, making it difficult to locate them efficiently.
Q132.
How does HDT-FoQ address the challenge of accessing data by object?

a.

By using a wavelet tree for object access

b.

By employing an additional index called O-Index

c.

By sorting objects in a particular order

d.

By using a separate data structure for objects

Discuss
Answer: (b).By employing an additional index called O-Index Explanation:HDT-FoQ addresses the challenge of accessing data by object by employing an additional index called O-Index.
Q133.
What is the role of the O-Index in HDT-FoQ?

a.

It gathers the positions of subjects in the So sequence.

b.

It facilitates subject-based access to the data set.

c.

It enables efficient access by object in HDT-FoQ.

d.

It provides an index of predicates in the data set.

Discuss
Answer: (c).It enables efficient access by object in HDT-FoQ. Explanation:The role of the O-Index in HDT-FoQ is to enable efficient access by object in the data set.
Q134.
How does the O-Index affect the overall requirements of HDT-FoQ?

a.

It significantly reduces the space required by HDT-FoQ.

b.

It has a minor impact on the space required by HDT-FoQ.

c.

It eliminates the need for other data structures in HDT-FoQ.

d.

It increases the space required by HDT-FoQ.

Discuss
Answer: (d).It increases the space required by HDT-FoQ. Explanation:The O-Index has a significant impact on the overall space requirements of HDT-FoQ, as it takes up considerable space compared to other data structures used for modeling the Triples component.
Q135.
What type of queries are efficiently resolved by HDT-FoQ's infrastructure?

a.

Basic triple patterns

b.

Conjunctive queries

c.

Optional patterns

d.

Complex graph patterns

Discuss
Answer: (a).Basic triple patterns Explanation:HDT-FoQ's infrastructure efficiently resolves basic triple patterns.
Q136.
Which concept naturally emerges in the context of Big Data and machine-processable semantics on the WWW?

a.

XML

b.

HTML

c.

RDF

d.

JSON

Discuss
Answer: (c).RDF Explanation:RDF naturally emerges in the context of Big Data and machine-processable semantics on the WWW.
Q137.
What is the common workflow that exists in most applications in the Web of Data?

a.

Query-Result-Display

b.

Search-Filter-Export

c.

Publication-Exchange-Consumption

d.

Create-Modify-Delete

Discuss
Answer: (c).Publication-Exchange-Consumption Explanation:The common workflow is Publication-Exchange-Consumption.
Q138.
What is HDT-FoQ designed for?

a.

Data encoding

b.

Data publication

c.

Query optimization

d.

Data visualization

Discuss
Answer: (c).Query optimization Explanation:HDT-FoQ is designed for query optimization.
Q139.
What is one of the benefits of using HDT instead of traditional approaches for data consumption?

a.

Faster data encoding

b.

Reduced query performance

c.

Slower I/O transferences

d.

Faster data retrieval and indexing

Discuss
Answer: (d).Faster data retrieval and indexing Explanation:Using HDT instead of traditional approaches for data consumption results in faster data retrieval and indexing.
Q140.
What supporting tool can be set up on top of an HDT file for publishers?

a.

RDF indexing

b.

SPARQL endpoint

c.

Dereferenceable URI

d.

JSON conversion

Discuss
Answer: (b).SPARQL endpoint Explanation:A useful tool for a publisher is setting up an SPARQL endpoint on top of an HDT file.
Page 14 of 15

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