Batch :E8 Case study on Big UniversitySuppose that a data warehouse for Big University consists of the following four dimensions: student, course, semester, and instructor, and two measures count and avg grade. When at the lowest conceptual level (e.g., for a given student, course, semester, and instructor combination), the avg grade measure stores the actual course grade of the student. At higher conceptual levels, avg grade stores the average grade for the given combination. (a) Draw a snowflake schema diagram for the data warehouse. (b) Starting with the base cuboid [student; course; semester; instructor], what specific OLAP operations (e.g., roll-up from semester to year) should one perform in order to list the average grade of CS courses for each Big University student. (c) What is a staging area? Do we need it? What is the purpose of a staging area? Problem 4: (25 points) Do problem 3.4 on page 152 Suppose that a data warehouse for Big University consists of the following four dimensions: student, course, semester, and instructor, and two measures count and avg_grade. When at the lowest conceptual level (e.g., for a given student, course, semester, and instructor combination), the avg_grade measure stores the actual course grade of the student. At higher conceptual levels, avg_grade stores the average grade for the given combination. (a) Draw a snowflake schema diagram for the data warehouse. (b) Starting with the base cuboid [student, course, semester, instructor], what specific OLAP operations (e.g., roll-up from semester to year) should one perform in order to list the average grade of CS courses for each Big University student. (c) If each dimension has five levels (including all), such as “student < major < status < university < all”, how many cuboids will this cube contain (including the base and apex cuboids)? Solution: (a) (b) slice for patient = “all” 4. Problem 3: (25 points) Do problem 3. patient]. doctor. (c) Starting with the base cuboid [day. what specific OLAP operations should be performed in order to list the total fee collected by each doctor in 2004? 1. where charge is the fee that a doctor charges a patient for a visit. viewed as a collection of stars.Starting with the base cuboid [student. Dice on course. course. write an SQL query assuming the data are stored in a relational database with the schema fee (day. student with department =”CS” and university=”Big University” 4. patient]. Suppose that a data warehouse consists of the three dimensions time. Solution: (a) star schema: a fact table in the middle connected to a set of dimension tables snowflake schema: a refinement of star schema where some dimensional hierarchy is normalized into a set of smaller dimension tables. what specific OLAP operations should be performed in order to list the total fee collected by each doctor in 2004? (d) To obtain the same list. roll up from day to month to year 2. (b) Draw a schema diagram for the above data warehouse using one of the schema classes listed in (a). semester. Drill-down on student from university to student name (c) The cube will contain 54=625 cuboids. year. therefore called galaxy schema or fact constellation. (b) As figures below (c) Starting with the base cuboid [day. slice for year = “2004” 3. (a) Enumerate three classes of schemas that are popularly used for modeling data warehouses. get the list of total fee collected by each doctor in 2004 (d) Select doctor. roll-up on student from (student_key) to university 3. hospital.3 on page 152. doctor. Fact constellations: multiple fact tables share dimension tables. roll-up on course from (course_key) to major 2. and patient. charge). Sum(charge) From fee Where year = 2004 Group by doctor . count. forming a shape similar to snowflake. roll up on patient from individual patient to all 4. patient. doctor. instructor] 1. doctor. and the two measures count and charge. month. course.course and instructor. and instructor. At higher conceptual levels.4.. The schema contains a central fact table for Big –University that contains keys to each of the four dimensions. along with two measures: count and avg_grade . and instructor combination). the avg_ grade measure stores the actual course grade of the student. semester. course. student . avg _grade stores the average grade for the given combination. for a given student. When at the lowest conceptual level (e. . P116 答： Big university are considered along four dimensions. semester. semester. (a) Draw a snowflake schema diagram for the data warehouse.g. namely. Suppose that a data warehouse for Big University consists of the following four dimensions: student. and two measures count and avg _grade. . roll-up from semester to year) should one perform in order to list the average grade of CS courses for each Big University student. 答： Starting with the base cuboid [student. the total number of cuboids that can be generated is: 54=625 www. resulting in a subcube.com/doc/6756591/Snowflake-Gau www. either by climbing up a concept hierarchy for a dimension or by dimension reduction. Roll-up: The roll-up operation performs aggregation on a data cube. One is added to Li in Equation to include the virtual top level. where Li is the number of levels associated with dimension i =1 n i. what specific OLAP operations (e.we use the following specific OLAP operations in order to list the average grade of CS courses for each Big University student.g. semester.4 Snowflake schema of a data warehouse for Big _university (b) Starting with the base cuboid [student.scribd. the total number of cuboids that can be generated (including the cuboids generated by climbing up the hierarchies along each dimension) is Total number of cuboids=∏ ( Li + 1). So . course.com/doc/43505352/Chapter-3 . course. (c) If each dimension has five levels (including all). all.” Slice and dice: The slice operation performs a selection on one dimension of the given cube.semester dimension table semester _key quarter year Big _university fact table semester _key course _key student _key instructor _key count avg _grade student dimension table student _key student _ No. instructor]. name age sex class major _key major dimension table major _key major _type course dimension table course _key course _number course _name Property credit instructor dimension table instructor _key name age office _key office dimension table office _key Office _telephone office _address Figure3. semester.scribd. instructor]. he cube has 4 dimensions and each dimension has 5 levels (including all). This hierarchy was defined as the total order “quarter<year. such as “student < major <status < university < all”. how many cuboids will this cube contain (including the base and apex cuboids)? 答： For an n-dimensional data cube. only remains around temporarily). it is not feasible to extract all the data from all Operational databases at exactly the same time. it might be feasible to extract "customer" data from a database in Singapore at noon eastern standard time.e. and perform data cleansing and merging .e. all required data must be available before data can be integrated into the Data Warehouse. the data load will have to go from the OLTP system to the OLAP system directly.In the absence of a staging area. it might be reasonable to extract sales data on a daily basis. For example. daily extracts might not be suitable for financial data that requires a month-end reconciliation process. This is the primary reason for the existence of a staging area. which in fact can severely hamper the performance of the OLTP system. In addition. before loading the data into warehouse. The Data Warehouse Staging Area is temporary location where data from source systems is copied. Due to varying business cycles. A staging area is mainly required in a Data Warehousing Architecture for timing reasons. hardware and network resource limitations and geographical factors. it also offers a platform for carrying out data cleansing. data processing cycles. remains around for a long period) or transient (i. . Staging tables are connected to work area or fact tables.What is a staging area?Do we need it?What is the purpose of a staging area? Staging area is place where you hold temporary tables on data warehouse server. but this would not be feasible for "customer" data in a Chicago database. Not all business require a Data Warehouse Staging Area. Similarly. Data in the Data Warehouse can be either persistent (i. For many businesses it is feasible to use ETL to copy data directly from operational databases into the Data Warehouse. We basically need staging area to hold the data . In short. however.