Solution

In a star schema, a fact table typically has a many-to-one relationship with dimension tables. Each entry in the fact table is related to multiple entries in the dimension table.

Solution

The snowflake schema is a normalized version of the star schema, which reduces data redundancy and therefore reduces storage space. However, this comes at the cost of more complex queries and joins.

Solution

A fact constellation schema, also known as a galaxy schema, is suitable for complex systems where multiple fact tables share dimension tables. This allows for more complex queries and data integration.

Solution

The snowflake schema enhances data integrity by normalizing dimension tables, which reduces redundancy and ensures that updates and deletions are propagated throughout the schema.

Solution

In a star schema, queries are typically faster because they involve fewer joins between the fact and dimension tables. The snowflake schema, while normalized, introduces more joins due to the normalized dimension tables, which can slow down query performance.

Solution

• A is correct because concept hierarchies enable roll-up (aggregation) and drill-down (decomposition) operations.
• B is incorrect. Concept hierarchies can be both predefined and dynamically created.
• C is correct because they help in summarizing and aggregating data at various levels of granularity.
• D is incorrect. Concept hierarchies are useful in both star and snowflake schemas.

Solution

• A is incorrect. Concept hierarchies do not directly affect the number of joins required but help in data organization.
• B is correct because concept hierarchies provide a structured way to group and organize data.
• C is correct because they allow for flexible drill-down and roll-up operations, enhancing data exploration and analysis.
• D is correct because they help to standardize data organization, making it easier to integrate data from different sources.

Solution

• A is correct because distributive measures can be computed by aggregating over subsets and combining the results.
• B is incorrect. Algebraic measures can be computed by applying algebraic functions to subsets of data.
• C is correct because holistic measures require examining all data records to compute the measure.
• D is correct because COUNT, SUM, and MIN are examples of distributive measures.

Solution

• A is correct because distributive measures can be computed by aggregating results from partitions.
• B is incorrect. Distributive measures do not always require a single pass; they can be computed incrementally.
• C is correct because distributive measures are scalable and suitable for parallel processing.
• D is incorrect. The median is a holistic measure, not a distributive measure.

Solution

• A is correct because the average can be computed using the sum and count, which are algebraic components.
• B is incorrect. SUM is a distributive measure.
• C is correct because standard deviation can be computed using algebraic functions applied to the data.
• D is incorrect. COUNT is a distributive measure.

Solution

• A is correct because holistic measures require access to all data records.
• B is incorrect. Holistic measures cannot be computed with a fixed number of passes regardless of data size.
• C is correct because the median and mode are examples of holistic measures.
• D is correct because holistic measures are generally less efficient to compute in large datasets compared to distributive and algebraic measures.

Solution

• A is correct because distributive measures can be computed incrementally by aggregating partition results.
• B is correct because algebraic measures often combine distributive measures, like average using sum and count.
• C is correct because holistic measures often require sorting or complete dataset access to compute.
• D is incorrect. While algebraic measures are generally more efficient than holistic measures, it is not always the case.

Solution

• Distributive Measures: Can be computed by applying the function to subsets and then combining results (e.g., SUM, COUNT).
• Algebraic Measures: Can be computed using a combination of distributive measures (e.g., AVERAGE, VARIANCE).
• Holistic Measures (Correct Answer): Require access to the entire dataset for computation; cannot be easily computed by combining subset results (e.g., MEDIAN).
• Aggregative Measures: This term is not commonly used in the context of measure types.

Solution

• Data Normalization: Involves restructuring data to reduce redundancy, not directly related to hierarchical analysis.
• Hierarchical Aggregation (Correct Answer): Utilizes the dimension hierarchy to perform detailed evaluations and trends by summarizing data at various levels (e.g., from Product level to Category level).
• Data Encryption: Involves securing data, unrelated to hierarchical data analysis.
• Data Cleaning: Focuses on correcting and preparing data, not on hierarchical analysis.

Solution

• Star Schema with Distributive Measures: While a star schema simplifies queries and distributive measures can be aggregated efficiently, it is less suited for multiple fact tables and complex hierarchical aggregations.
• Snowflake Schema with Holistic Measures: A snowflake schema supports normalized dimensions, which helps in minimizing redundancy, but holistic measures require the entire dataset for accurate computation and aren’t ideal for hierarchical aggregation.
• Fact Constellation Schema with Algebraic Measures (Correct Answer): The fact constellation schema is well-suited for handling multiple fact tables and shared dimensions. Algebraic measures like Average Sales Price can be computed by aggregating distributive measures like Total Sales, making this combination optimal for complex aggregations and maintaining efficiency.

Snowflake Schema with Distributive Measures: Although a snowflake schema is effective in reducing redundancy, it doesn’t handle multiple fact tables as well as the fact constellation schema. Distributive measures are good for simple aggregation but may not fully address complex hierarchical analysis involving multiple fact tables.