Relational database optimized for online transactions

Business Intelligence And Data Warehouses

Business Intelligence And Data Warehouses

https://blackboard.strayer.edu/webapps/rubric/do/course/gradeRubric?mode=grid&isPopup=true&rubricCount=1&prefix=_12677489_1&course_id=_227624_1&maxValue=100.0&rubricId=_633162_1&viewOnly=true&d

Unacceptable Below 60% F Meets Minimum Expectations 60-69% D Fair 70-79% C Proficient 80-89% B Exemplary 90-100% A

CIS111-A3-1 1. Outline the main differences between the structure of a relational database optimized for online transactions versus a data warehouse optimized for processing and summarizing large amounts of data. Weight: 21%

0 (0%) - 12.59 (12.59%) Did not submit or incompletely outlined the main differences between the structure of a relational database optimized for online transactions versus a data warehouse optimized for processing and summarizing large amounts of data.

12.6 (12.6%) - 14.69 (14.69%) Did not submit or incompletely outlined the main differences between the structure of a relational database optimized for online transactions versus a data warehouse optimized for processing and summarizing large amounts of data.

14.7 (14.7%) - 16.79 (16.79%) Partially outlined the main differences between the structure of a relational database optimized for online transactions versus a data warehouse optimized for processing and summarizing large amounts of data.

16.8 (16.8%) - 18.89 (18.89%) Satisfactorily outlined the main differences between the structure of a relational database optimized for online transactions versus a data warehouse optimized for processing and summarizing large amounts of data.

18.9 (18.9%) - 21 (21%) Thoroughly outlined the main differences between the structure of a relational database optimized for online transactions versus a data warehouse optimized for processing and summarizing large amounts of data.

CIS111-A3-2 2. Outline the main differences between database requirements for operational data and for decision support data. Weight: 21%

0 (0%) - 12.59 (12.59%) Did not submit or incompletely outlined the main differences between database requirements for operational data and for decision support data.

12.6 (12.6%) - 14.69 (14.69%) Insufficiently outlined the main differences between database requirements for operational data and for decision support data.

14.7 (14.7%) - 16.79 (16.79%) Partially outlined the main differences between database requirements for operational data and for decision support data.

16.8 (16.8%) - 18.89 (18.89%) Satisfactorily outlined the main differences between database requirements for operational data and for decision support data.

18.9 (18.9%) - 21 (21%) Thoroughly outlined the main differences between database requirements for operational data and for decision support data.

CIS111-A3-3 3. Describe three (3) examples in which databases could be used to support decision making in a large organizational environment. Weight: 21%

0 (0%) - 12.59 (12.59%) Did not submit or incompletely described three (3) examples in which databases could be used to support decision making in a large organizational environment.

12.6 (12.6%) - 14.69 (14.69%) Insufficiently described three (3) examples in which databases could be used to support decision making in a large organizational environment.

14.7 (14.7%) - 16.79 (16.79%) Partially described three (3) examples in which databases could be used to support decision making in a large organizational environment.

16.8 (16.8%) - 18.89 (18.89%) Satisfactorily described three (3) examples in which databases could be used to support decision making in a large organizational environment.

18.9 (18.9%) - 21 (21%) Thoroughly described three (3) examples in which databases could be used to support decision making in a large organizational environment.

CIS111-A3-4 4. Describe three (3) examples in which data warehouses and data mining could be used to support data processing and trend analysis in large organizational environment. Weight: 21%

0 (0%) - 12.59 (12.59%) Did not submit or incompletely described three (3) examples in which data warehouses and data mining could be used to support data processing and trend analysis in large organizational environment.

12.6 (12.6%) - 14.69 (14.69%) Insufficiently described three (3) examples in which data warehouses and data mining could be used to support data processing and trend analysis in large organizational environment.

14.7 (14.7%) - 16.79 (16.79%) Partially described three (3) examples in which data warehouses and data mining could be used to support data processing and trend analysis in large organizational environment.

16.8 (16.8%) - 18.89 (18.89%) Satisfactorily described three (3) examples in which data warehouses and data mining could be used to support data processing and trend analysis in large organizational environment.

18.9 (18.9%) - 21 (21%) Thoroughly described three (3) examples in which data warehouses and data mining could be used to support data processing and trend analysis in large organizational environment.

CIS111-A3-5 5. 3 references Weight: 6%

0 (0%) - 3.59 (3.59%) No references provided

3.6 (3.6%) - 4.19 (4.19%) Does not meet the required number of references; all references poor quality choices.

4.2 (4.2%) - 4.79 (4.79%) Does not meet the required number of references; some references poor quality choices.

4.8 (4.8%) - 5.39 (5.39%) Meets number of required references; all references high quality choices.

5.4 (5.4%) - 6 (6%) Exceeds number of required references; all references high quality choices.

CIS111-A3-6 6. Clarity, writing mechanics, and formatting requirements Weight: 10%

0 (0%) - 5.99 (5.99%) More than 8 errors present

6 (6%) - 6.99 (6.99%) 7-8 errors present

7 (7%) - 7.99 (7.99%) 5-6 errors present

8 (8%) - 8.99 (8.99%) 3-4 errors present

9 (9%) - 10 (10%) 0-2 errors present

Name: CIS111 Week 9 Assignment 3: Business Intelligence and Data Warehouses

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Name:CIS111 Week 9 Assignment 3: Business Intelligence and Data Warehouses

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