Assignment 3 Grading Rubric: Highly Competent, Competent, Mi

Assignment 3 Grading Rubricnamehighly Competentcompetentminimally

This assignment covers multiple database concepts, including SQL query formulation, relational algebra, graph representation in SQL, and advanced query techniques for data aggregation. The tasks involve writing SQL queries based on given schemas, understanding relational algebra equivalences, designing graph schemas in SQL, and implementing queries for reachability, as well as solving an advanced problem involving aggregation for minimal diversity in tuple values.

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The assignment encompasses three major problems, each demanding a deep understanding of relational databases, query formulation, and data representation techniques. The first problem focuses on translating relational schema attributes into SQL queries that include counting, comparison, and aggregation. Specifically, it involves identifying employees assigned to exactly one project, comparing salaries, computing project counts based on budgets, and filtering projects by average budget per agency. Additionally, it requests an explanation about the relational algebra equivalent—highlighting the importance of query equivalences in relational algebra and SQL.

The second problem emphasizes graph representation within relational databases, requiring the design of a schema for directed graphs and implementation of reachability queries through SQL. It involves calculating directly reachable nodes, all reachable nodes (including indirect), and set differences for nodes not reachable from a specific node, both directly and indirectly. This problem tests knowledge of recursive queries, particularly SQL2 or SQL3 dialects like recursive common table expressions (CTEs), which are essential for graph traversal algorithms.

The third, extra-credit problem is a more advanced challenge that asks for an SQL query to identify tuples with the minimal number of distinct attribute values, emphasizing aggregation and grouping techniques. It includes examples demonstrating the expected output and hints at designing a solution that does not rely on tuple identifiers but rather on intrinsic tuple characteristics. This demands creative use of aggregation functions and grouping, as well as some form of tuple comparison based on the diversity of attribute values.

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The context of this assignment underscores the significance of mastering SQL for effective data querying and manipulation. The problems demonstrate core relational database skills, such as constructing precise SQL queries for complex conditions, understanding relational algebra's role in query optimization and equivalence, and representing graph structures within relational models to facilitate traversal analyses. These skills are critical when designing and querying real-world databases, which often require complex queries, recursive traversals, and advanced aggregation techniques.

In Problem 1, understanding how to express set operations, joins, and aggregate functions in SQL is foundational. For example, to find employees assigned to exactly one project, SQL's COUNT and GROUP BY clauses can be combined with HAVING to filter employees. Comparing salaries involves self-joins or subqueries. Computing project counts with higher budgets across projects within each project entails nested queries or window functions. Filtering projects below the average budget per agency involves grouping, aggregation, and filtering techniques. The relational algebra equivalence primarily applies to the first two queries: set-based operations and selection/projection are directly translatable, whereas aggregate operations may be more nuanced depending on the algebraic expressions used.

Problem 2 introduces graph theory concepts integrated within relational databases. Representing nodes and directed edges in SQL involves creating tables for nodes and edges, with foreign keys. Reachability queries—particularly those spanning beyond immediate connections—necessitate recursive queries, such as recursive CTEs available in SQL2 and SQL3 standards. These recursive queries systematically explore paths between nodes, allowing us to compute direct reachability, as well as transitive closure, thus enabling the calculation of indirectly reachable nodes. Reporting nodes not reachable from a specific node involves set difference operations, which combine recursion with set operations.

The extra-credit problem illustrates sophisticated aggregation techniques, focusing on tuples with the minimal diversity in attribute values. This problem requires developing an approach that groups tuples based on the count of distinct values across specified columns. The challenge is to compute this minimal diversity efficiently without relying on tuple identifiers, likely involving nested queries, correlated subqueries, or window functions to count distinct values per tuple and then identify those with the minimal count. Such techniques are valuable in data cleaning, anomaly detection, and other domains where intrinsic data characteristics matter more than identifiers.

References

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