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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.

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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 11 of 11

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Q101.
When does the rule falsification step in CaRLA terminate?

a.

When the set E becomes empty

b.

When the set E becomes larger

c.

When all rules have been validated

d.

When the score threshold smin is reached

Discuss
Answer: (a).When the set E becomes empty Explanation:The rule falsification step in CaRLA terminates when the set E (a set of property values) becomes empty. This means that there are no more property values left to check for similarity above the threshold θ.
Q102.
What determines the final value of the threshold θ in CaRLA?

a.

The size of set P

b.

The size of set N

c.

The score threshold smin

d.

The number of iterations

Discuss
Answer: (c).The score threshold smin Explanation:The final value of the threshold θ in CaRLA is determined by the score threshold smin. It is set based on the minimum score of the transformation rules computed during the rule merging and filtering step.
Q103.
How does CaRLA extend to aCARLA?

a.

By using a larger training set P0 and N0

b.

By introducing additional transformation rules

c.

By using a smaller score threshold smin

d.

By incorporating active learning with small training sets and requesting annotations

Discuss
Answer: (d).By incorporating active learning with small training sets and requesting annotations Explanation:CaRLA extends to aCARLA by incorporating active learning with small training sets and requesting annotations from the user. In aCARLA, the algorithm starts with small training sets and, in each iteration, tries to validate or refute transformation rules with low confidence. It fetches additional property values for validation and continues the learning process until a stopping condition is met, such as a maximum number of questions. This extension helps CaRLA detect pairs of annotations that lead to a larger set of high-quality rules efficiently.
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