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 11 of 11

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Q101.

When does the rule falsification step in CaRLA terminate?

When the set E becomes empty

When the set E becomes larger

When all rules have been validated

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?

The size of set P

The size of set N

The score threshold smin

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?

By using a larger training set P0 and N0

By introducing additional transformation rules

By using a smaller score threshold smin

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.

Page 11 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 11 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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