Welcome to the Linked Data in Enterprise Integration MCQs Page

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

Check out the MCQs below to embark on an enriching journey through Linked Data in Enterprise Integration. 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 Linked Data in Enterprise Integration. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Linked Data in Enterprise Integration MCQs | Page 10 of 11

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Q91.

How is the score of rules <x → ε> adjusted in CaRLA?

It is increased by a factor κ

It is decreased by a factor κ

It is multiplied by a factor σ

It is multiplied by a factor ε

Discuss

Answer: (b).It is decreased by a factor κ Explanation:In CaRLA, the score of rules <x → ε> is decreased by a factor κ to ensure that the computation does not favor deletions.

Q92.

How does CaRLA handle ties when choosing between rules <x → y> and <x → y′>?

It selects <x → y> if σ(x, y) > σ(x, y′)

It selects <x → y′> if σ(x, y) > σ(x, y′)

It selects both <x → y> and <x → y′>

It randomly selects <x → y> or <x → y′>

Discuss

Answer: (a).It selects <x → y> if σ(x, y) > σ(x, y′) Explanation:CaRLA selects rule <x → y> if the similarity between x and y is greater than the similarity between x and y′.

Q93.

What is the final score function used in CaRLA?

scorefinal(<x → ε>) = 0

scorefinal(<x → y>) = score(<x → y>)

scorefinal(<x → ε>) = κ

scorefinal(<x → y>) = score(<x → y>) + σ(x, y)

Discuss

Answer: (d).scorefinal(<x → y>) = score(<x → y>) + σ(x, y) Explanation:The final score function in CaRLA is computed as scorefinal(<x → y>) = score(<x → y>) + σ(x, y).

Q94.

What does CaRLA do in the rule merging step?

It computes the Jaccard similarity

It discards rules <x → y> if <y → x> is in the other set of rules

It tokenizes property values

It computes the transformation function φR

Discuss

Answer: (b).It discards rules <x → y> if <y → x> is in the other set of rules Explanation:In the rule merging step of CaRLA, it discards rules <x → y> if <y → x> is in the other set of rules.

Q95.

How are low-weight rules handled in CaRLA during rule merging and filtering?

They are always discarded

They are always kept

They are multiplied by a factor κ

They are multiplied by a factor σ

Discuss

Answer: (a).They are always discarded Explanation:In CaRLA, low-weight rules are discarded during rule merging and filtering.

Q96.

What determines the initial similarity threshold θ in CaRLA?

The size of set P

The size of set N

The Jaccard similarity

The tokenization function

Discuss

Answer: (a).The size of set P Explanation:The initial similarity threshold θ in CaRLA is determined by the size of set P.

Q97.

How does CaRLA compute the final value of θ?

By taking the minimum of the similarity thresholds of both sets of rules

By taking the maximum of the similarity thresholds of both sets of rules

By averaging the similarity thresholds of both sets of rules

By summing the similarity thresholds of both sets of rules

Discuss

Answer: (a).By taking the minimum of the similarity thresholds of both sets of rules Explanation:CaRLA computes the final value of θ by taking the minimum of the similarity thresholds of both sets of rules.

Q98.

What does the output of CaRLA consist of?

The initial similarity threshold

The tokenization function

The transformation function φR

The retrieved set of rules and the value of θ

Discuss

Answer: (d).The retrieved set of rules and the value of θ Explanation:The output of CaRLA consists of the retrieved set of rules and the value of θ.

Q99.

What is the goal of the rule falsification step in CaRLA?

To compute the transformation functions φR1 and φR2

To detect a set of transformations that lead to a maximal number of elements of N having a similarity superior to θ

To tokenize property values

To compute the initial similarity threshold

Discuss

Answer: (b).To detect a set of transformations that lead to a maximal number of elements of N having a similarity superior to θ Explanation:The goal of the rule falsification step in CaRLA is to detect a set of transformations that leads to a minimal number of elements of N (negative training examples) having a similarity superior to the threshold θ via the similarity function σ.

Q100.

How does CaRLA handle the rule falsification process?

It tries to validate all rules simultaneously

It tries to refute or validate rules with a score below the score threshold smin

It discards all rules that apply to elements of E by descending score

It discards all rules with a score above the score threshold smin

Discuss

Answer: (b).It tries to refute or validate rules with a score below the score threshold smin Explanation:CaRLA handles the rule falsification process by trying to refute or validate rules with a score below the score threshold smin. It does this iteratively by picking the most unsure rule and fetching a set of property values that map the left side (premise) of the rule. It then requests annotations for these property values to determine if the rule is valid or should be discarded.

Page 10 of 11

Introduction to Big Data 🔒

Management of Big Semantic Data 🔒

Scalable End User Access to Big Data 🔒

Big Data Exploration 🔒

Big Data Processing with MapReduce 🔒

Advanced Data Analytics for Business 🔒

Real Time Big Data Processing 🔒

Big Textual Data Analytics and Knowledge Management 🔒

Welcome to the Linked Data in Enterprise Integration MCQs Page

Linked Data in Enterprise Integration MCQs | Page 10 of 11

Explore more Topics under Big Data Computing

Big Data Challenges and Opportunities

Semantic Data Interoperability

Efficient Processing of Stream Data over Persistent Data

Economics of Big Data

Big Social Data Analysis

Application of Big Data in Analyzing Electric Meter Data

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