The document outlines the syllabus and schedule for a Business Intelligence course, highlighting key topics such as performance metrics and SQL. It covers essential elements of data management, including data definitions, integrity constraints, and commonly used data types. Various practical examples, discussion questions, and insights into NoSQL use cases and performance measurement methods are also included.

1. P R O F . S T E V E N L . J O H N S O N
T w i t t e r : @ S t e v e n L J o h n s o n
http://stevenljohnson.org
http://community.mis.temple.edu/mis5101fall10/
MIS5101: Business Intelligence
Week 10 – Outcome Measures

2. Schedule Reminders
 11/10 Computer Lab – Accessing Data (SQL)
 11/17 Computer Lab – Analyzing Data (Excel)
 11/24 Happy Thanksgiving (Eve)
 12/1 Topic: Security, Privacy and Data Mining
(2 Cases)
 12/8 Group Project Due
Group Project Presentations
 12/15 Final Exam

3. Today’s Agenda
 Last Week Discussion
 Case Discussion
 Blog + Reading Discussion
 NoSQL + SQL

4. Case Analysis Discussion Questions
 If you ran an in-vitro fertilization clinic what are 2-3
key metrics you would want to track and why?
 Are these the same metrics a potential IVF customer
would care about?
 Are they the same metrics that a national standards
body should focus on?
 What opportunities and challenges are there in
developing better outcome measures and reporting for
the IVF field?
 What specific data, if any, do you think Dr. Goldfarb
should advocate developing national standards for
collecting and reporting?

5. 100 Second Reflection
1. What is something you want to remember about
the outcome measures at the end of the semester?
2. What would you like to know more about?
3. Any comments?

6. Blog Discussion
 Performance Measurement
 KPIs
 Performance measurement systems
 Performance measurement as a process
 Deciding what to measure
 Gathering performance data
 Interpreting performance results
 Avoiding performance measurement pitfalls
 From performance measurement to performance
management

7. Measuring the User Experience: Collecting,
Analyzing, and Presenting Usability Metrics
Summary of section 4.6, performance metrics
 Task success metrics – characteristics of completion
 Time-on-task - how quickly
 Errors - number of mistakes
 Efficiency - amount of effort (cognitive and physical)
 Learnability –changes in efficiency over time
Chapter 8
 Combined and comparative measures
7

8. NoSQL Use Cases
 Frequently-written, rarely read statistical data (e.g., a web hit counter):
in-memory key/value store (Redis), or update-in-place document store
(MongoDB)
 Big Data (e.g., weather stats or business analytics): freeform, distributed db
system (Hadoop).
 Binary assets (e.g., MP3s and PDFs): datastore serves directly to browser
(Amazon S3)
 Transient data (e.g., web sessions, locks, or short-term stats): transient
datastore (e.g., Memcache)
 Replicate your data set to multiple locations (e.g.,as syncing a music database
between a web app and a mobile device): add replication features (CouchDB).
 High availability apps where minimizing downtime is critical: automatically
clustered, redundant setup of datastores (Cassandra,Riak)
 Highly normalized, transactional, ad-hoc-queries: SQL databases. Adding new
tools to our toolbox, not removing old ones.
Source: NoSQL, Heroku, and You - http://blog.heroku.com/archives/2010/7/20/nosql/

9. Introduction to SQL
 Database Languages
 Create database and table structures
 Perform basic data management chores (add, delete, and
modify)
 Perform complex queries to transform data into useful
information
 Structured Query Language (SQL) is a database
computer language designed for managing data in
relational database management systems (RDMS)
 Data definition language (DDL)
 Data manipulation language (DML)
9

10. DDL vs. DML examples
Name DDL: Data Definition
Language
DML: Date Manipulation
Language
Purpose Defines Structure of
Database and Database
Objects
Manipulates the Data housed
in the tables
Add Create table: creates a new
table
Insert Into: adds a new record
to a table
Change Alter table: modifies the
tables structure (add a
column, change a datatype,
add constraint, etc.)
Update: changes the values
of an attribute in a record
Remove Drop table: deletes the table
from the database
Delete: deletes a record from
a table
10

11. Data Dictionary and System Catalog
 Data dictionary
 Provides detailed account of all tables found within database
 Metadata
 Attribute names and characteristics
 System catalog
 Detailed data dictionary
 System-created database
 Stores database characteristics and contents
 Tables can be queried just like any other tables
 Automatically produces database documentation
11

12. Most Common Data Types
Data Type Data Type Description
CHAR(n) • fixed length column can contain any printable characters.
• If the data entered into CHAR field < length of field, field is padded
with spaces.
• maximum length of CHAR column ~ 200.
e.g: a state abbreviation - CHAR(2) since it is always 2 characters long.
VARCHAR2(n) variable length column with a fixed length. If the length of the data is
less than the maximum length of the field, then the field is not
padded with spaces.
• maximum length of the column ~ 2000.
e.g: A customer’s first name - VARCHAR2(35) since name length is
variable.
NUMBER Integer and real values occupying up to 40 spaces.
INTEGER Same as number, but no decimals.
DATE Contains a date and time between the 1st of January 4712 BC to the
31st of December 4712 AD.
• Standard date format: DD-MMM-YY (i.e. 01-JAN-99)
• Any other format will require input mask.
12

13. SQL Data Integrity Constraints
 Entity integrity - enforced automatically with
PRIMARY KEY constraint
 Referential integrity - enforced FOREIGN KEY
constraint
 Other specifications to ensure conditions met:
 ON DELETE RESTRICT
 ON UPDATE CASCADE
13

14. Entity Relationship Diagram
From Wikipedia (ERD) http://en.wikipedia.org/wiki/Entity-relationship_diagram

15. SQL Select Examples
 Mathematical operators
 Mathematical operators on character attributes
 Mathematical operators on dates
15
SELECT P_DESCRIPT, P_INDATE, P_PRICE, V_CODE
FROM PRODUCT
WHERE V_CODE <> 21344;
SELECT P_CODE,P_DESCRIPT,P_ONHAND,P_MIN,P_PRICE
FROM PRODUCT
WHERE P_CODE < ‘1558-QWI’;
SELECT P_DESCRIPT,P_ONHAND,P_MIN,P_PRICE,PINDATE
FROM PRODUCT
WHERE P_INDATE >= ‘01/20/2002’;

16. SQL SELECT Component
A query includes a list of columns to be included in the final result immediately
following the SELECT keyword. An asterisk ("*") can also be used to specify that the
query should return all columns of the queried tables. SELECT is the most complex
statement in SQL, with optional keywords and clauses that include:
 The FROM clause which indicates the table(s) from which data is to be retrieved.
The FROM clause can include optional JOIN subclauses to specify the rules for
joining tables.
 The WHERE clause includes a comparison predicate, which restricts the rows
returned by the query. The WHERE clause eliminates all rows from the result set for
which the comparison predicate does not evaluate to True.
 The GROUP BY clause is used to project rows having common values into a smaller
set of rows. GROUP BY is often used in conjunction with SQL aggregation functions
or to eliminate duplicate rows from a result set. The WHERE clause is applied
before the GROUP BY clause.
 The HAVING clause includes a predicate used to filter rows resulting from the
GROUP BY clause. Because it acts on the results of the GROUP BY clause,
aggregation functions can be used in the HAVING clause predicate.
 The ORDER BY clause identifies which columns are used to sort the resulting data,
and in which direction they should be sorted (options are ascending or descending).
Without an ORDER BY clause, the order of rows returned by an SQL query is
undefined.
Source: http://en.wikipedia.org/wiki/SQL
16

17. Example
SELECT Book.title,
count(*) AS Authors
FROM Book JOIN Book_author
ON Book.isbn = Book_author.isbn
GROUP BY Book.title;
Title Authors
---------------------- -------
SQL Examples and Guide 4
The Joy of SQL 1
An Introduction to SQL 2
Pitfalls of SQL 1
Source: http://en.wikipedia.org/wiki/SQL

18. P R O F . S T E V E N L . J O H N S O N
E M A I L : S T E V E N @ T E M P L E . E D U
T w i t t e r : @ S t e v e n L J o h n s o n
http://stevenljohnson.org
http://community.mis.temple.edu/mis5001fall10johnson/
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