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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 11 of 14

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Q101.
What is a bitemporal database?

a.

A database that stores both abstract and concrete temporal data

b.

A database that captures real-world time

c.

A database that uses timestamps for data representation

d.

A database that contains only spatial data

Discuss
Answer: (a).A database that stores both abstract and concrete temporal data Explanation:A bitemporal database is a database that stores both abstract and concrete temporal data, capturing both valid time and transaction time.
Q102.
In the temporal SQL query "SELECT ID, Location, Value FROM Sensor WHERE Start >= 20 and End <= 27," what is the expected result with respect to abstract temporal databases?

a.

{(S_42,Loc_1,17)}

b.

{(S_42,Loc_1,17),(S_42,Loc_2,17)}

c.

{(S_42,17)}

d.

There will be no result.

Discuss
Answer: (b).{(S_42,Loc_1,17),(S_42,Loc_2,17)} Explanation:With respect to abstract temporal databases, the expected result of the query is {(S_42,Loc_1,17),(S_42,Loc_2,17)}.
Q103.
What does the SQL query "SELECT ID, Value, Start, End FROM Sensor WHERE Start <= 23 and End >= 27" retrieve?

a.

The IDs and values of sensors that have a start time less than or equal to 23 and an end time greater than or equal to 27.

b.

The IDs and values of sensors that have a start time greater than or equal to 23 and an end time less than or equal to 27.

c.

The IDs and values of sensors that have a start time less than or equal to 23 or an end time greater than or equal to 27.

d.

The IDs and values of sensors that have a start time greater than or equal to 23 or an end time less than or equal to 27.

Discuss
Answer: (a).The IDs and values of sensors that have a start time less than or equal to 23 and an end time greater than or equal to 27. Explanation:The SQL query "SELECT ID, Value, Start, End FROM Sensor WHERE Start <= 23 and End >= 27" retrieves the IDs and values of sensors that have a start time less than or equal to 23 and an end time greater than or equal to 27.
Q104.
In the context of stream-based query answering, what is the key idea?

a.

Storing all historical data for future queries.

b.

Continuously answering multiple registered queries over a fused stream of data.

c.

Implementing queries over multiple combined streams with sliding windows.

d.

Using temporal aggregation operators to compute aggregate values.

Discuss
Answer: (b).Continuously answering multiple registered queries over a fused stream of data. Explanation:In the context of stream-based query answering, the key idea is to continuously answer multiple registered queries over a fused stream of data.
Q105.
What is the purpose of sliding time windows in stream-based query answering?

a.

To define the semantics of stream-based queries appropriately.

b.

To store historical data for future analysis.

c.

To support temporal aggregation operators.

d.

To optimize query plans for continuous queries.

Discuss
Answer: (a).To define the semantics of stream-based queries appropriately. Explanation:Sliding time windows in stream-based query answering are used to define the semantics of stream-based queries appropriately by limiting the subset of data considered by a query.
Q106.
What are temporal aggregation operators used for in stream-based query languages?

a.

To store data efficiently in a database.

b.

To compute aggregate values over sliding time windows.

c.

To optimize query plans for continuous queries.

d.

To define the semantics of stream-based queries.

Discuss
Answer: (b).To compute aggregate values over sliding time windows. Explanation:Temporal aggregation operators in stream-based query languages are used to compute aggregate values over sliding time windows, allowing for operations like computing sums, averages, etc., over temporal data.
Q107.
What are some extensions to the RDF query language SPARQL that have been proposed for stream-based access scenarios?

a.

XML-based extensions

b.

Aggregate functions

c.

Resource Description Format (RDF) extensions

d.

Dynamic replanning techniques

Discuss
Answer: (c).Resource Description Format (RDF) extensions Explanation:Various extensions to the RDF query language SPARQL have been proposed for stream-based access scenarios in the RDF context. These extensions are designed to work with RDF data and enable stream-based queries.
Q108.
In the context of stream-based query answering, what does C-SPARQL rely on for dealing with entailments for RDFS or OWL 2 RL?

a.

Incremental materialization

b.

Manual RDF data entry

c.

Direct implementation of stream-based queries

d.

Prolog-based logical inferences

Discuss
Answer: (a).Incremental materialization Explanation:C-SPARQL relies on incremental materialization to deal with entailments for RDFS or OWL 2 RL.
Q109.
What is SNEEQL?

a.

A query processor for wireless sensor networks

b.

A stream-based query language for RDF

c.

A Prolog-based real-time event recognition system

d.

A mapping language for relating source data to RDF ontologies

Discuss
Answer: (a).A query processor for wireless sensor networks Explanation:SNEEQL is a query processor for wireless sensor networks.
Q110.
What is EP-SPARQL tailored for?

a.

Querying RDF data

b.

Complex event processing

c.

Logical inferences

d.

Event recognition with expressive ontologies

Discuss
Answer: (b).Complex event processing Explanation:EP-SPARQL is tailored for complex event processing.
Page 11 of 14

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