Lecture 2 ISM - © 2010 Houman Younessi Convener: Houman Younessi 1-860-548-7880 [email protected] Information Systems Spring 2011
Dec 22, 2015
Lecture 2
ISM - © 2010 Houman Younessi
Convener:
Houman Younessi
1-860-548-7880
Information SystemsSpring 2011
Lecture 2
ISM - © 2010 Houman Younessi
Element Concept Information Category Information System
Revenue Demand Market trends and marketingProduct qualityPricing
Data Acquisition Data AnalysisControlForecastingOptimization
Product Orientation
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ISM - © 2010 Houman Younessi
Quality
- Design - Meeting requirements
- Control - Meeting specifications
- Delivery - Meeting expectations
Example: Hospital
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ISM - © 2010 Houman Younessi
The pitfall of Ceteris Paribus• The demand side of the market can be represented by the demand curve
• The shape of the demand curve determines demand points (levels of demand)
• Demand points in turn determines supply levels needed to maximize revenues
However
The demand curve (function) changes and shifts with respect to changes in consumer taste, income, population, commodity prices, availability of other
products, product quality, access, and price stability
Lecture 2
ISM - © 2010 Houman Younessi
Consumer taste,
Income,
Population,
Commodity prices,
Availability of other products,
Product quality,
Access, and
Price stability
We need to not only acquire data on these but also to analyze and
present the information such data might imply.
• Data Acquisition • Data Analysis• Control• Forecasting, and• Optimization
applications are to be used.
Lecture 2
ISM - © 2010 Houman Younessi
Application:Collection of programs that together achieve a particular objective directly related to a task that the user wishes to perform.
Program:
Code written - in a human understandable language - that is to be executed in computer memory where it can perform tasks on data.
Algorithms + Data Structures
Lecture 2
ISM - © 2010 Houman Younessi
Algorithm:
A finite set of well-defined instructions for accomplishing a given task which, given an initial state, will terminate in a defined end-state representing a particular goal.
e.g. A recipe to cook lasagna
e.g. A set of instructions to minimize a path traveled
e.g. A set of instructions to sort a set of numbers
Recursive, iterative; Deterministic, Non-deterministic; Serial, Parallel
Lecture 2
ISM - © 2010 Houman Younessi
Examples of Algorithms:
Recursive Iterative
function fib(n)
if n = 0
return 0
if n = 1
return 1
else
return fib(n-1) + fib(n-2)
function fib(n)
a, c = 0 ,b = 1
Do (n times)
{ c = a+b
a = b
b = c
return b}
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ISM - © 2010 Houman Younessi
Programming Language:Artificial Language created to produce artifacts called programs that control the behavior of automata, usually a computer.
Compilers, Interpreters;
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ISM - © 2010 Houman Younessi
Example Programs in Various Languages:
int fib(int n) {
if (n <= 2) return 1
else return fib(n-1) + fib(n-2) }
(+ (fib(- N 1)) (fib(- N 2)))))
fib1,fib2 :=1
to n do begin
fib1,fib2 :=fib2,fib1; fib1 :=fib1+fib2
end;
C family
Lisp family
Algol family
Lecture 2
ISM - © 2010 Houman Younessi
Data Structure:
A well-defined form of storing data.
• Influences the efficiency of algorithm used.
• Abstract (Data-type), Concrete (Implementation);
• Simple, Composite;
Implemented in Memory
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ISM - © 2010 Houman Younessi
Example Data Structure: Stack
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ISM - © 2010 Houman Younessi
Database:A system that provides organized collection, retention and presentation of data according to a well-defined model that ensures user selected persistence.
• Definition and query schemas,
• Models (Hierarchical, networked, relational, object)
• Transaction
• Concurrency
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ISM - © 2010 Houman Younessi
Application Types:
- Compiler
- Operating System
- DBMS
- Accounting and financial management
- Inventory management
- CAD
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ISM - © 2010 Houman Younessi
Building a Database
Step 1:
Capturing the Data Model
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ISM - © 2010 Houman Younessi
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ISM - © 2010 Houman Younessi
Step 2
NORMALIZATION
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ISM - © 2010 Houman Younessi
What is Normalization?
Normalization is the process of efficiently organizing data in a database. There are two goals of the normalization process: eliminating redundant data (for example, storing the same data in more than one table) and ensuring data dependencies make sense (only storing related data in a table). Both of these are worthy goals as they reduce the amount of space a database consumes and ensure that data is logically stored.
There are five normal forms (1st to 5th). These are cumulative in that to be in 2nd normal form the data has to be in 1st normal form first.
The first to third normal forms are almost always obeyed. The fourth normal form occasionally, and the fifth normal form almost never.
We will cover the first three normal forms here only.
Lecture 2
ISM - © 2010 Houman Younessi
First Normal Form (1NF) * Eliminate duplicative columns from the same table. * Create separate tables for each group of related data and identify each row with a unique column or set of columns (the primary key).
Second Normal Form (2NF) * Meet all the requirements of the first normal form. * Remove subsets of data that apply to multiple rows of a table and place them in separate tables. * Create relationships between these new tables and their predecessors through the use of foreign keys.
Third Normal Form (3NF) * Meet all the requirements of the second normal form. * Remove columns that are not dependent upon the primary key.
Lecture 2
ISM - © 2010 Houman Younessi
First Normal Form:
• Eliminate duplicative columns from the same table.• Create separate tables for each group of related data and identify each row with a unique column (the primary key).
Manager Subordinate 1 Subordinate 2 Subordinate 3 Subordinate 4
We can now eliminate the repeating groups by forming a table such as
Subordinate Manager
We can now ensure that each entry is identified by a single identifier:
Subordinate ID Manager ID Subordinate Manager
Lecture 2
ISM - © 2010 Houman Younessi
Second Normal Form
• Remove subsets of data that apply to multiple rows of a table and place them in separate tables.• Create relationships between these new tables and their predecessors through the use of foreign keys.
reduce the amount of redundant data in a table by extracting it, placing it in new table(s) and creating relationships between those tables.
CUS_ID FIRST LAST STREET CITY STATE ZIP
CUS_ID FIRST LAST STREET ZIP
ZIP CITY STATE
to
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ISM - © 2010 Houman Younessi
Third Normal Form
Remove columns that are not fully dependent upon the primary key.
CUS_ID PROD_ID UNIT_PR QTTY TOTAL
To:
CUS_ID PROD_ID UNIT_PR QTTY
“TOTAL” is derivable as TOTAL=UNIT_PR*QTTY
Lecture 2
ISM - © 2010 Houman Younessi
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