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MIS5101: Business Intelligence Week 10 – Outcome Measures

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

Today’s Agenda

  Last Week Discussion

  Case Discussion

  Blog + Reading Discussion

  NoSQL + SQL

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?

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?

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

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

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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/

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)

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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

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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

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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.

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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

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Entity Relationship Diagram

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

SQL Select Examples

  Mathematical operators

  Mathematical operators on character attributes

  Mathematical operators on dates

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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’;

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

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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

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