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					           Chapter 4

Data and Knowledge Management




            Chapter 4       1
           Learning Objectives
1. Understand information system concepts
    including input, processing, and output,
    data and information.
2. Understand information system components
    including hardware, software, databases
    and telecommunications.
10. Be able to create a database using a
    relational DBMS such as Access.
    Demonstrate the ability to create reports,
    queries and join two tables in the DBMS 2
                      Chapter 4
Chapter 4   3
                 Why Databases?

 Difficulties of Managing Data.
     Data is a company’s most valuable resource.
     Amount of data increases exponentially.
     Data are scattered and collected by many
      individuals using various methods and devices.
     Data come from many sources including internal
      sources, personal sources and external sources.
     Data security, quality and integrity are critical.

                         Chapter 4                         4
                Data Hierarchy

 Bit (a binary digit): a circuit that is either on
  or off.
 Byte: group of 8 bits, represents a single
  character.
 Field: name, number, or characters that
  describe an aspect of a business object or
  activity.

                       Chapter 4                      5
     Data Hierarchy (Continued)

 Record: collection of related data
  fields.
 File (or table): collection of related
  records.
 Database: a collection of integrated and
  related files.

                  Chapter 4              6
Chapter 4   7
                 Before Databases:
              File Processing Systems




Traditional File Processing:
 Data are organized, stored,
   and processed in
   independent files of data
   records

                                Chapter 4   8
           Problems of File Processing
 Data Redundancy – duplicate data requires an update
  to be made to all files storing that data
      Example – what if the same customer has a checking
       account, savings account, and loan?

 Lack of Data Integration – data stored in separate files
  require special programs for output making ad hoc
  reporting difficult

 Data Dependence – programs must include
  information about how the data is stored so a change in
  storage format requires a change in programs
                              Chapter 4                     9
Why Data Redundancy is Bad

                      Checking,
                      Savings, and
                      Loan files will
                      have customer
                      address, etc.

                      If address
                      changes in one
                      file and not the
                      others, you
                      introduce
                      inconsistencies.



          Chapter 4                 10
      Solution: Database Approach

 Database management system (DBMS) provides all users
  with access to all the data.
 DBMSs minimizes the following problems:
    Data redundancy: the same data stored in many places.

    Data isolation: applications cannot access data

     associated with other applications.
    Data inconsistency: various copies of the data do not

     agree.



                          Chapter 4                          11
               Database Management
                    Approach




 DBMS = A software system that is used to create, maintain, and provide
  controlled and secure access to data
                                Chapter 4                              12
    Another example of a
Database Management System
          (DBMS)




            Chapter 4        13
      Database Approach (Continued)

 DBMSs maximize the following issues:
     Data security.
     Data integrity: data meets certain constraints, no
      alphabetic characters in zip code field.
     Data independence: applications and data are
      independent of one another, all applications are
      able to access the same data.


                         Chapter 4                     14
             Designing the Database

 Data model. Diagram that represents the entities in
  the database and their relationships.
      Entity is a person, place, thing or event.
      Attribute is a characteristic or quality of a particular
       entity.
      Primary key is a field that uniquely identifies that
       record.
      Secondary keys are fields that have identifying
       information but may not identify with complete accuracy.

                            Chapter 4                        15
Data Modeling:
 Process where the relationships between data elements are
  identified
 Standard approach is to create an Entity-Relationship (ER)
  diagram


                         Chapter 4                         16
      Entity-Relationship Modeling

 Database designers plan the database design in a process
  called entity-relationship (ER) modeling.
 ER diagrams consists of entities, attributes and
  relationships.
 Entity classes are a group of entities of a given type, e.g.
  STUDENT.
 Instance is the representation of a particular entity, i.e.
  STUDENT(John Smith, 123-45-6789, …).
 Attributes are fields that contain specific data about an
  entity instance.
 Identifiers (primary keys) are attributes unique to that entity
  instance, i.e. StudentIDNumber.

                            Chapter 4                          17
N




                Cardinalities of relationships:
                         1 – to – 1
                         1 – to – many
                         many – to - many
    Chapter 4                                     18
Attributes:
         fields that contain specific data about entities




     Primary key:
             the attribute that uniquely identifies an entity


                            Chapter 4                           19
One customer
may place many
orders, but each
order is placed by
a single customer
 One-to-many
relationship




     Chapter 4       20
One order has
many order lines;
each order line is
associated with a
single order
 One-to-many
relationship




       Chapter 4     21
One product can
be in many
order lines, each
order line refers
to a single
product
 One-to-many
relationship




        Chapter 4   22
Therefore, one
order involves
many products
and one product is
involved in many
orders




 Many-to-many
relationship

    Chapter 4        23
ER Diagram for Product Inventory
     and Ordering System




            Chapter 4          24
ER Diagram of Employee Database

JobTypes   1|N   1|N




                       Employees           M|N            Projects



                                      Entities in boxes
Departments      1|N
                                      Relationships in
                                      diamonds

                          Chapter 4                                  25
    Entities in Employee Database

 Employees - data about people in the
  company
 Departments - data about the organizational
  units
 JobTypes - data about the work
  classifications
 Projects - data about the current projects
  underway
                    Chapter 4                   26
          Relationships in Employee
                  Database
 Each department has many employees, but each
  employee works for only one department (1:N)
 There are many employees of a given job type, but each
  employee has only one job title (1:N)
 An employee who is a manager has many employees
  under her, but each employee has only one manager to
  report to (1:N)
 Each employee can be working on several projects, and
  each project may have several employees working on it
  (M:N)
                          Chapter 4                   27
Chapter 4   28
        What is a relational database?
 A database in which:
     entity classes are represented by TABLES (also
      called RELATIONS),
     specific entity instances are represented by ROWS
      in the TABLE (records)
     attributes are represented by COLUMNS in the
      TABLE (fields)
     relationships between entities are represented by
      associations between primary and foreign keys
      (identifier fields)
                           Chapter 4                      29
  A Table in Microsoft Access




Popular examples of relational databases
are Microsoft Access and Oracle.



                   Chapter 4                30
 Relationships View of the tables in
the Employee Database (MS Access)

                        1




                        8               M:N relationships require an
                                        additional table, called an
                                        intersection (or junction) table.
       The relationships are            The M:N relationship between
       implemented via associations     Employees and Projects is
       between primary keys             implemented via the
       (shown here in boldface) and     EmployeeProject intersection
                                        table.
       foreign keys of tables

                            Chapter 4                                       31
      Design View of Employees Table
               (MS Access)

                              Each field
You
                              is of a
specify the
                              particular
attributes
                              data type
(fields)




                 Chapter 4                 32
         Design View of Employees Table
                  (MS Access)
The
primary
key
(unique
identifier)


                                 You can
                                 specify
                                 properties
                                 like default
                                 values and
                                 constraints
                                 for a field


                    Chapter 4                   33
                    1:N Relationship Between
                   Departments and Employees




The DepartmentID field of the Employees table is a
foreign key. It references the DepartmentID field of
the Departments table (primary key). In this way, we
can see that Sam Smith, Mike Mitri, Alice Friedman,
and Brendan Mitri are all in the Payroll department.
(DepartmentID = 2).
A department has several employees, but each
employee is in only one department.
                                                 Chapter 4   34
M:N relationship between
Employees and Projects




     The EmployeeProject table is an intersection table that implements the M:N
     relationship. The EmployeeID field of the EmployeeProject table is a foreign key
     that references the EmployeeID field of the Employees table. Likewise for the
     ProjectID fields. Here we see that James Smith is one of the four employees who
     works on Accounts Payable project. James Smith also works on the Accounts
     Receivable project.

     Each employee can have several projects and each project can have several
     employees.
                     Chapter 4                                                 35
      Normalization for Well-Structured
                 Databases

 Normalization is a method for analyzing and
  reducing a relational database to its most
  streamlined form for:
     Mimimum redunancy;
     Maximum data integrity;
     Best processing performance.
 Normalized data is when attributes in the
  table depend only on the primary key.

                       Chapter 4              36
Non-Normalized Table




       Chapter 4       37
After Normalization




      Chapter 4       38
Relationships between Tables
  in Normalized Database




          Chapter 4            39
A database query is a request view certain,
selected data from a database.



                  Chapter 4                   40
             Query Languages

 Structured query language (SQL) is the
  most popular query language used to request
  information.
 Query by example (QBE) is a graphical
  grid or template that a user fills out to
  construct a sample or description of the data
  wanted.

                     Chapter 4                41
               The SELECT Statement
 Used for queries on tables in a relational database
 Parts of the SELECT statement:
      SELECT
          List the columns (and expressions) that should be returned from the query
      FROM
          Indicate the table(s) or view(s) from which data will be obtained
      WHERE
          Indicate the conditions under which a row will be included in the result
      GROUP BY
          Indicate categorization of results
      HAVING
          Indicate the conditions under which a category (group) will be included
      ORDER BY
          Sorts the result according to specified criteria


                                           Chapter 4                                   42
        Examples
Suppose we have these tables




          Chapter 4            43
    Query Example #1
Get Names of Employees in
    Alphabetical Order
                                SQL


                      Results
   QBE




          Chapter 4                   44
   Query Example #2
Get Names and Salaries of
   Programmers Only
                         SQL


                     Results
   QBE




         Chapter 4             45
          Query Example #3
      Get Summary Information
(average salaries of different job titles)
                                     SQL


                               Results
           QBE




                   Chapter 4               46
          Query Example #4
Get Related Data from Multiple Tables
 (This kind of query is called a JOIN)
                                          SQL

                                Results

              QBE




                    Chapter 4              47
Chapter 4   48
Data Life Cycle




    Chapter 4     49
              Data Warehousing

 Data warehouse is a repository of historical
  data organized by subject to support decision
  makers in the organization and include:
     Online analytical processing which involves
      the analysis of accumulated data by end users;
     Multidimensional data structure which allows
      data to be represented in a three-dimensional
      matrix (or data cube).

                       Chapter 4                   50
    Benefits of Data Warehousing

 End users can access data quickly and easily
  via Web browsers because they are located in
  one place.
 End users can conduct extensive analysis
  with data in ways that may not have been
  possible before.
 End users have a consolidated view of
  organizational data.

                    Chapter 4                51
        Data Marts & Data Mining

 Data mart is a small data warehouse,
  designed for the end-user needs in a strategic
  business unit (SBU) or a department.
 Data mining involves searching for valuable
  business information in a large database, data
  warehouse, or data mart.
     Used to predict trends and behaviors.
     Identify previously unknown patterns.

                        Chapter 4              52
Data Warehouse Framework and
           Views




          Chapter 4            53
Relational Database




      Chapter 4       54
Converted to Multidimensional
             DB




           Chapter 4            55
Chapter 4   56
     Online Analytical Processing
              (OLAP)
Definition:
 Enables mangers and analysts to interactively
  examine and manipulate large amounts of
  detailed and consolidated data from many
  perspectives




                    Chapter 4                 57
          Analytical Operations

 Consolidation – aggregation of data

 Drill-down – detail data that comprise
  consolidated data

 Slice and Dice – ability to look at the
  database from different viewpoints
                     Chapter 4              58
               OLAP Technology




          Online Demo of OLAP Technology

http://demos3.dundas.com/olapdemostaging/default.aspx

                        Chapter 4                       59
Multidimensional
databases can
be visualized as
cubes…each
dimension refers
to possible
values of
variables


                   Chapter 4   60
        OLAP can be Done in Excel:
              PivotTables
 Three dimensions (page, row, column)
      This enables slice-and-dice, allowing user to view any
       combination of variables for each dimension
 Summary information can be viewed for any
  combination of variables
      This enables consolidation (aggregation of data)
 For any given piece of summary information, user
  can get details
      This enables drill-down, obtaining details of aggregate
       data
                             Chapter 4                           61
                      Data Mining

Definition:
 Analyzing the data in a data warehouse or data mart to reveal
  hidden patterns and trends in historical business activity




                           Chapter 4                         62
              Data Mining Uses
 Perform “market-basket analysis” to identify new
  product bundles.

 Find root causes to quality or manufacturing
  problems.

 Prevent customer attrition and acquire new
  customers.

 Cross-sell to existing customers.

 Profile customers with more accuracy.
                       Chapter 4                     63
          Data Mining Applications

 Retailing and sales. Predict sales, prevent theft and fraud,
  determine correct inventory levels and distribution
  schedules.
 Banking. Forecast levels of bad loans, fraudulent credit card
  use, predict credit card spending by new customers, etc.
 Manufacturing and production. Predict machinery
  failures, find key factors to help optimize manufacturing
  capacity.
 Insurance. Forecast claim amounts, medical coverage costs,
  predict which customers will buy new insurance policies.


                           Chapter 4                         64
          Data Mining Applications
                (Continued)
 Policework. Track crime patterns, locations,
  criminal behavior; identify attributes to assist in
  solving criminal cases.
 Health care. Correlate demographics of patients
  with critical illnesses, develop better insight to
  identify and treat symptoms and their causes.
 Marketing. Classify customer demographics to
  predict how customers will respond to mailing or
  buy a particular product.

                        Chapter 4                       65
Chapter 4   66
      Data Visualization Systems

 Graphically represent complex data using
  interactive three-dimensional forms such as
  charts, graphs, and maps

 Data visualization tools help users to
  interactively sort, subdivide, combine, and
  organize data while it is in its graphical form.

                      Chapter 4                  67
Chapter 4   68
Chapter 4   69
Chapter 4   70
       Data Visualization Technologies

 Geographic Information Systems (GIS) is a
  computer-based system for capturing, integrating,
  manipulating and displaying data using digitized
  maps. (example: GoogleMaps)
      Find locations for new restaurants.
      Emerging GIS applications integrated with global
       positioning systems (GPSs).
 Virtual Reality is interactive, computer-generated,
  three-dimensional graphics delivered to the user
  through a head-mounted display.


                           Chapter 4                      71
Virtual Reality




    Chapter 4     72
Chapter 4   73
Chapter 4   74
         Knowledge Management

 Knowledge management (KM) is a process that
  helps organizations manipulate important
  knowledge that is part of the organization’s
  memory, usually in an unstructured format.
 Knowledge is information that is contextual,
  relevant and actionable; information in action.
 Intellectual capital (or intellectual assets) is
  another term often used for knowledge.

                       Chapter 4                     75
             Knowledge Management
                  (Continued)
 Explicit knowledge deals with more objective, rational and
  technical knowledge.
 Tacit knowledge is the cumulative store of subjective or
  experiential learning.
 Knowledge management systems (KMSs) use modern
  information technologies – Internet, intranets, extranets, data
  warehouses - to systemize, enhance and expedite intrafirm
  and interfirm knowledge management.
 Best practices are the most effective and efficient ways of
  doing things, readily available to a wide range of employees.


                            Chapter 4                          76
Knowledge Management System
           Cycle




            Chapter 4         77
       Knowledge Management System
                  Cycle

   Create knowledge. Determine new ways.
   Capture knowledge. Identify as valuable.
   Refine knowledge. Make it actionable.
   Store knowledge. Store in a reasonable format.
   Manage knowledge. Verify it is relevant, accurate.
   Disseminate knowledge. Made available.



                         Chapter 4                       78

				
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