mds:dsd:start
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mds:dsd:start [20/09/2024 alle 19:29 (6 giorni fa)] – Salvatore Ruggieri | mds:dsd:start [26/09/2024 alle 12:37 (15 ore fa)] (versione attuale) – Salvatore Ruggieri | ||
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The module presents the main approaches to the design and implementation of decision support databases, and the characteristics of business intelligence tools and computer based information systems used to produce summary information to facilitate appropriate decision-making processes and make them more quick and objectives. Specific attention will be paid to themes such as conceptual and logical Data Warehouses design, data analysis using analytic SQL, algorithms for selecting materialized views, data warehouse systems technology (indexes, star query optimization, | The module presents the main approaches to the design and implementation of decision support databases, and the characteristics of business intelligence tools and computer based information systems used to produce summary information to facilitate appropriate decision-making processes and make them more quick and objectives. Specific attention will be paid to themes such as conceptual and logical Data Warehouses design, data analysis using analytic SQL, algorithms for selecting materialized views, data warehouse systems technology (indexes, star query optimization, | ||
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=====Instructor===== | =====Instructor===== | ||
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| Wednesday | | Wednesday | ||
| Thursday | | Thursday | ||
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^ Date ^ Hour ^ Room ^ Notes ^ | ^ Date ^ Hour ^ Room ^ Notes ^ | ||
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=====Class calendar ===== | =====Class calendar ===== | ||
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|04| 24/09 9-11 | Fib-C | DW modeling. Representation of facts, measures, dimensions, attributes and dimensional hierarchies. Key steps in conceptual design from business questions. How to identify facts, measures, dimensions, dimensional attributes and hierarchies. Examples. {{: | |04| 24/09 9-11 | Fib-C | DW modeling. Representation of facts, measures, dimensions, attributes and dimensional hierarchies. Key steps in conceptual design from business questions. How to identify facts, measures, dimensions, dimensional attributes and hierarchies. Examples. {{: | ||
|05| 26/09 11-13 | Fib-A1 | The example of a data model for Master program exams. Presentation and discussion of the Hospital case study. | |05| 26/09 11-13 | Fib-A1 | The example of a data model for Master program exams. Presentation and discussion of the Hospital case study. | ||
- | |06| 27/09 11-13 | Fib-L1 | Recalls: the relational model and relational algebra. Exercises. {{: | + | |06| 27/09 11-13 | Fib-C1 | Recalls: the relational model and relational algebra. Exercises. {{: |
|07| 01/10 9-11 | Fib-C | More about data mart conceptual design, changing dimensions and advanced data model features. From Conceptual design to relational logical design. Star model, snowflake, and constellation. Logical schema of the Hospital case study. {{: | |07| 01/10 9-11 | Fib-C | More about data mart conceptual design, changing dimensions and advanced data model features. From Conceptual design to relational logical design. Star model, snowflake, and constellation. Logical schema of the Hospital case study. {{: | ||
|08| 03/10 11-13 | Fib-A1 | Recalls: the relational model and relational algebra. Logical trees. {{: | |08| 03/10 11-13 | Fib-A1 | Recalls: the relational model and relational algebra. Logical trees. {{: | ||
- | |09| 04/10 11-13 | Fib-L1 | Discussion of students' | + | |09| 04/10 11-13 | Fib-C1 | Discussion of students' |
- | |10| 8/10 11-13 | Fib-A1 | Data Warehouse design approaches. Data mart logical design. | + | |10| 8/10 11-13 | Fib-C | Data Warehouse design approaches. Data mart logical design. |
- | |11| 15/10 11-13 | Fib-A1 | Slowly changing dimensions, fast changing dimensions, shared dimensions. Recursive hierarchies. Multivalued dimensions. {{: | + | |11| 15/10 11-13 | Fib-C | Slowly changing dimensions, fast changing dimensions, shared dimensions. Recursive hierarchies. Multivalued dimensions. {{: |
|12| 16/10 16-18 | Fib-H | A DW to support Analytical CRM Analysis. Wrap up on DW design. | |12| 16/10 16-18 | Fib-H | A DW to support Analytical CRM Analysis. Wrap up on DW design. | ||
|13| 17/10 11-13 | Fib-A1 | Multidimensional Cube model: OLAP Operations. The extended cube and the lattice of cuboids. Pivot tables in Excel. [[http:// | |13| 17/10 11-13 | Fib-A1 | Multidimensional Cube model: OLAP Operations. The extended cube and the lattice of cuboids. Pivot tables in Excel. [[http:// | ||
- | |14| 18/10 11-13 | Fib-L1 | Recalls on: DBMS, from SQL to extended relational algebra. Exercises. {{: | + | |14| 18/10 11-13 | Fib-C1 | Recalls on: DBMS, from SQL to extended relational algebra. Exercises. {{: |
|15| 24/10 11-13 | Fib-A1 | OLAP systems. Data Analysis Using SQL. Simple reports. Examples. Moderately Difficult Reports. Solutions in SQL. {{: | |15| 24/10 11-13 | Fib-A1 | OLAP systems. Data Analysis Using SQL. Simple reports. Examples. Moderately Difficult Reports. Solutions in SQL. {{: | ||
- | |16| 25/10 11-13 | Fib-L1 | Examples of variance reports. Very Difficult Reports without Analytic SQL. Example of reports with ranks. Analytic Functions with the use of partitions and running totals. Examples. | + | |16| 25/10 11-13 | Fib-C1 | Examples of variance reports. Very Difficult Reports without Analytic SQL. Example of reports with ranks. Analytic Functions with the use of partitions and running totals. Examples. |
|17| 31/10 11-13 | Fib-A1 | Analytic Functions with the use of moving windows. Examples. Exercises on Analytic SQL. {{: | |17| 31/10 11-13 | Fib-A1 | Analytic Functions with the use of moving windows. Examples. Exercises on Analytic SQL. {{: | ||
|18| 05/11 9-11 | Fib-C | Recalls of relational DBMS internals: Storage, Indexing and Query Evaluation. Physical operators and physical plans for projection, selection, joins and grouping. Examples. [[http:// | |18| 05/11 9-11 | Fib-C | Recalls of relational DBMS internals: Storage, Indexing and Query Evaluation. Physical operators and physical plans for projection, selection, joins and grouping. Examples. [[http:// | ||
|19| 07/11 11-13 | Fib-A1 | Data Warehouse Systems: Special-Purpose Indexes and Star Query Plan. Bitmap indexes. Join indexes. Star queries optimization and query plans. Examples. Table partitioning. [[http:// | |19| 07/11 11-13 | Fib-A1 | Data Warehouse Systems: Special-Purpose Indexes and Star Query Plan. Bitmap indexes. Join indexes. Star queries optimization and query plans. Examples. Table partitioning. [[http:// | ||
- | |20| 08/11 11-13 | Fib-L1 | The problem of materialized views selection. The lattice of views and the greedy algorithm HRU for the selection of materialized views. Examples. Other algorithms for the choice of the views to materialize with a workload and dimensional hierarchies. | + | |20| 08/11 11-13 | Fib-C1 | The problem of materialized views selection. The lattice of views and the greedy algorithm HRU for the selection of materialized views. Examples. Other algorithms for the choice of the views to materialize with a workload and dimensional hierarchies. |
- | |21| 15/11 11-13 | Fib-L1 | Recalls of functional dependency properties and how they are used to reason about the properties of the result of a query. Properties of the group-by operator. | + | |21| 15/11 11-13 | Fib-C1 | Recalls of functional dependency properties and how they are used to reason about the properties of the result of a query. Properties of the group-by operator. |
- | |22| 22/11 11-13 | Fib-L1 | The problem of evaluating the group-by before the join operator. First case: Invariant grouping. Examples. Other cases: double grouping, grouping and counting. Examples with star queries. | + | |22| 22/11 11-13 | Fib-C1 | The problem of evaluating the group-by before the join operator. First case: Invariant grouping. Examples. Other cases: double grouping, grouping and counting. Examples with star queries. |
- | |23| 29/11 11-13 | Fib-L1 | The problem of query rewrite to use a materialized view. Hypothesis and two approaches: With a compensation on the logical view plan, and with a transformation of logical query plan. Examples. [[http:// | + | |23| 29/11 11-13 | Fib-C1 | The problem of query rewrite to use a materialized view. Hypothesis and two approaches: With a compensation on the logical view plan, and with a transformation of logical query plan. Examples. [[http:// |
- | |24| 6/12 11-13 | Fib-L1 | Data Warehousing trends: column-oriented DW, main-memory DW, Big Data framework. [[http:// | + | |24| 6/12 11-13 | Fib-C1 | Data Warehousing trends: column-oriented DW, main-memory DW, Big Data framework. [[http:// |
mds/dsd/start.1726860575.txt.gz · Ultima modifica: 20/09/2024 alle 19:29 (6 giorni fa) da Salvatore Ruggieri