Lecture 2 ISM - © 2010 Houman Younessi Convener: Houman Younessi Information Systems Spring 2011

Lecture 2 ISM - © 2010 Houman Younessi ElementConceptInformation CategoryInformation System RevenueDemandMarket trends and marketing Product quality Pricing Data Acquisition Data Analysis Control Forecasting Optimization Product Orientation

Lecture 2 ISM - © 2010 Houman Younessi Quality - Design - Meeting requirements - Control - Meeting specifications - Delivery - Meeting expectations Example: Hospital

Lecture 2 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}

Lecture 2 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;

Lecture 2 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

Lecture 2 ISM - © 2010 Houman Younessi Example Data Structure: Stack

Lecture 2 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

Lecture 2 ISM - © 2010 Houman Younessi Application Types: - Compiler - Operating System - DBMS - Accounting and financial management - Inventory management - CAD

Lecture 2 ISM - © 2010 Houman Younessi Building a Database Step 1: Capturing the Data Model

Lecture 2 ISM - © 2010 Houman Younessi

Lecture 2 ISM - © 2010 Houman Younessi Step 2 NORMALIZATION

Lecture 2 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 (1 st to 5 th ). These are cumulative in that to be in 2 nd normal form the data has to be in 1 st 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). ManagerSubordinate 1 Subordinate 2Subordinate 3 Subordinate 4 We can now eliminate the repeating groups by forming a table such as SubordinateManager We can now ensure that each entry is identified by a single identifier: Subordinate IDManager IDSubordinateManager

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_IDFIRSTLASTSTREETCITYSTATEZIP CUS_IDFIRSTLASTSTREETZIP CITYSTATE to

Lecture 2 ISM - © 2010 Houman Younessi Third Normal Form Remove columns that are not fully dependent upon the primary key. CUS_IDPROD_IDUNIT_PRQTTYTOTAL To: CUS_IDPROD_IDUNIT_PRQTTY “TOTAL” is derivable as TOTAL=UNIT_PR*QTTY

Lecture 2 ISM - © 2010 Houman Younessi Next Step