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1
Applied Database Management Systems
Formatting Style APA
by
Jemilson Pierrelouis
Student ID# UD2866SIS7122
An Assignment PaperSubmitted in Partial Fulfillment of the
Requirements for theDoctor of Science Degree
in
Business Administration with major Information System
_______________________________Assignment adviser: Gilroy Newball, Ph.D
The PH.D School
Atlantic International University
06, 2006
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2
ABSTRACT
As the integration of computer aided design and manufacturing (CAD/CAM) systems
progresses, the need for management of the resulting data becomes critical. Database
management systems (DBMS) have been developed to assist with this task, but currently
do not satisfy all of the needs of CAD/CAM data. This thesis examines and proposes
DBMS requirements for design and manufacturing data associated with mechanical parts.
A case study approach was used, involving examples of parts produced by numerically
controlled (NC) milling and sheet metal punching machines. Representative examples of
currently available relational and object oriented DBMS's were used to construct
prototype CAD/CAM databases. Insights concerning the application of relational and
object oriented DBMS's to CAD/CAM data were gained. The advantages and
deficiencies of each were examined and discussed. The prototypes and resulting
discussions provided a basis for the development of the proposed DBMS requirements.
TABLE OF CONTENTS
ABSTRACT........................................................................................................................2
Introduction.................................................................................................................3Applied Database Management...................................................................................4
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3Applied Database Systems...........................................................................................6Database Systems........................................................................................................6Special Track on Database Theory, Technology, and Applications (DTTA)..............7Stream-Based Data Management Systems..................................................................9Database consists of schema and test data................................................................10Automatically Update all Database Developers.......................................................11Accessing database management systems.................................................................12Creating and deleting database tables......................................................................12Database menus.........................................................................................................16Data Warehousing.....................................................................................................19The Scope of Data Mining.........................................................................................22Conclusions................................................................................................................23References..................................................................................................................24
Introduction
This paper is to introduce fundamentals of modern database management systems, in
particular relational database systems. Also, I will touch on many areas in applied
database management slightly. Further, this paper is intended as a text that can be used as
an overview. There are multitude parts of applied database management, and in this paper
I will cover the basic concepts. Organization uses applied database management to make
most business decision, and these decisions are by way of decision sciences of
information. Information and Decision Sciences incorporates the use of data processing
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4equipment, such as computers and their peripherals. These methods are applied to
systems management, programming design, analysis of information flow, decision
support, database organization, small business problems, data communication
networking, and distributed processing. The automated, prospective analyses offered by
data mining move beyond the analyses of past events provided by retrospective tools
typical of decision support systems. In aggregates information and stored into a database,
most companies feel a need to use data mining tools that can answer their business
questions that traditionally were too time consuming to resolve. They scour databases for
hidden patterns, finding predictive information that experts may miss because it lies
outside their expectations. The core components of data mining technology have been
under development for decades, in research areas such as statistics, artificial intelligence,
and machine learning. Today, the maturity of these techniques, coupled with high-
performance relational database engines and broad data integration efforts, make these
technologies practical for current data warehouse environments.
Applied Database Management
There are many parts to database management such as: 1. A data access interface that
communicates with Microsoft Jet and ODBC-compliant data sources to connect to,
retrieve, manipulate, and update data and the database structure. 2. The process of
obtaining data from another source, usually one outside a specific system. It usually
includes a description of the placement of the data blocks and their relation to the entire
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5set. 3. Structural information about data that describes its context and meaning. 4. A file
composed of records, each containing fields together with a set of operations for
searching, sorting, recombining, and other functions. 4. Database administrator is who
manages a database. The administrator determines the content, internal structure, and
access strategy for a database, defines security and integrity, and monitors performance.
5. Database manager is one who provides the analytic functions needed to design and
maintain applications requiring a database. 6. Database designer is also one who designs
and implements functions required for applications that use a database. 7.Database engine
is a program module or modules that provide access to a database management system
(DBMS). 8. Database machine is a peripheral that executes database tasks, thereby
relieving the main computer from performing them. A database server that performs only
database tasks.
Further, in applied database management a software interface between the database and
the user. A database management system handles user requests for database actions and
allows for control of security and data integrity requirements. The use of desktop
publishing or Internet technology to produce reports containing information obtained
from a database. A network node, or station, dedicated to storing and providing access to
a shared database. In addition, a database structure is a general description of the format
of records in a database, including the number of fields, specifications regarding the type
of data that can be entered in each field, and the field names used. In asynchronous
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6communications, one of a group of from 5 to 8 bits that represents a single character of
data for transmission. Data bits are preceded by a start bit and followed by an optional
parity bit and one or more stop bits.
The following topics is to perform different database operations:
A brief tour of the Smalltalk classes for creating database applications
Instructions for accessing your database management system and binding to your
database
How to create and delete databases and tables
Step-by-step instructions for querying databases using VA Smalltalk and IBM
Smalltalk database classes
Tips for handling errors, ensuring row schema consistency, binary data limits, and
intercepting SQL 000 codes
The database classes can be divided into four categories: base classes, classes for defining
database resources and operations, classes for manipulating database data, and classes for
using database data links.
Applied Database Systems
This course I’ve learned database systems with a focus on how to use them in practice.
This gives an overview of the capabilities of modern database systems, and how to build
database-backed applications. Topics covered include the relational model, SQL,
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7transactions, database design and tuning, three-tier architectures, web data management
with XML, service-oriented architectures, data mining, and data warehousing.
Database Systems
Database system is a modern relational database systems concentrating on the internals
of relational database systems. Concepts covered include query languages (SQL,
relational algebra and relational calculus), storage structures, access methods, query
processing, query optimization, and database design. This course is usually offered in the
fall semester. It consists of several large programming assignments where students build
part of a small relational database system called Minibase. This deals with the
architecture of large-scale information systems, with special emphasis on Internet-based
systems. Topics covered include three-tier architectures, edge caches, distributed
transaction management, web services, workflows, high-availability architectures, and
content management. Also include a significant number of programming assignments in
the context of three-tier architectures, involving web servers, application servers and
database systems.
Special Track on Database Theory, Technology, and Applications (DTTA)
For many years, the Database Theory, Technology, and Applications track has been
one of the important parts of the ACM SAC conference. To support ACM SAC, a special
track on Database Theory, Technology, and Applications will be held again in SAC 2006.
The DTTA track will be a forum for database scholars, research scientists, engineers, and
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8practitioners throughout the world to share their theoretical results, technical ideas, and
exploratory experiences relating to implementation and applications. You are cordially
invited to submit technical papers to the DTTA track of SAC 2006, and major topics of
interest for the track include, but are not limited to the following:
Active, Deductive, and Logic Databases
Audio/Video Database Systems
Cache and Buffer Management
Cooperative Database Systems and Workflow Management
Database Indexing and Tuning
Data Privacy and Security
Data Warehousing, Data Cubes, and Aggregate Processing
Digital Library
Disk Arrays and Tertiary Storage Systems for Very Large Databases
Distributed, Parallel and Heterogeneous Databases and Their Query Processing
Histogram and Sampling Techniques for Database Query Processing
Hypertext/Hypermedia/Multimaedia Database and Information Systems
Image, Pictorial and Visual Databases
Internet and Web-Based Database Systems
Knowledge Discovery and Data Mining in Databases
Mobile Data Management and Mobile Database Systems
Multi-Database Systems/Federated Database Systems/Trusted Database Systems
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9 Multidimensional Data Models/Indices/Database Systems
Object-Oriented and Object-Relational Database Systems
Probabilistic/Fuzzy Databases and Similarity/Approximate Query Processing
Real-Time and High Performance Database Systems
Scientific, Biological and Bioinformatics Data Management and Data Mining
Semantic Modeling and management of Web-Based Databases
Semantic Web and Ontology
Semi-Structural Data Management, Meta Data, and XML
Spatial and Temporal Databases
Statistical and Historical Databases
Transaction Management and Secure Transaction Processing
Researchers and practitioners in the database, information systems and internet fields
over the years have made significant progress towards the building of solutions that
involve such systems for a wide range of application domains. In doing this, solutions
necessarily concentrated mainly on syntax as the readily available unifying formalism for
representation and structure, rather more than on the broad variety of semantics involved.
One of the recent unifying visions is that of Semantic Web, which proposed semantic
annotation of data, so that programs can understand it, and help in making decisions.
Researchers have subsequently seen the value of using semantics to understand
information and decision making needs of humans, so that data and human? needs can be
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10semantically intermediated. The scope of semantics-based solutions has also moved from
data and information to services and processes.
A review of active research funding and projects shows extensive investigations based
AI and knowledge representation branches of computer science. For example, logic-
based descriptions and inference techniques are being extensively investigated as part of
projects under the Semantic Web umbrella. This includes many projects funded by
DARPA and EC 5th Framework Program, including the DAML and Onto Web initiatives
and programs. There is a visible dearth of investigations from the database and
information systems community. This workshop seeks to investigate relationships
between challenges in developing semantic solutions for the Web and Enterprises, and
the experience and expertise of the database and information systems community.
Research in database management and workflow management has an extensive history
of achieving high impact through improving methods of other scientific endeavors as
well as in developing new technologies leading to commercialization and in establishing
new high-tech industry sectors. This workshop will investigate research directions that
can lead to similar long-term impact in Semantic Web and Enterprise solutions by our
community.
Stream-Based Data Management Systems
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11 Continuous query processing is a relatively new field in query processing. It deals with
the execution of queries over infinite streams of data, rather than over fixed collections of
data. Traditional query processing systems are powerful tools for examining stores of
data. Continuous query systems are similarly powerful, but focus on processing and
reacting to the data as it is collected. These systems are specially designed for “stream
processing” problems. Stream processing problems involve input data that is coming into
existence over time. The data rate may be very high, or come in bursts. Output is
calculated as soon as the required input data is available. Output is a function of all input
data available up to the present time. Stream processing problems often make explicitly
use of the time domain of their input data. For example, calculating the maximum value
seen in the last 5 minutes. There can also be real-time requirements on processing, where
results are required within a specified amount of time after data becomes available. A
continuous query system will allow stream-processing problems to be specified by
programmers, and executed efficiently.
Database consists of schema and test data
When we talk about a database here, we mean not just the schema of the database, but
also a fair amount of data. This data consists of common standing data for the
application, such as the inevitable list of all the states in the US, and also sample test data
such as a few sample customers. The data is there for a number of reasons. The main
reason is to enable testing. We are great believers in using a large body of automated tests
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12to help stabilize the development of an application. Such a body of tests is a common
approach in agile methods. For these tests to work efficiently, it makes sense to work on a
database that is seeded with some sample test data, which all tests can assume is in place
before they run. As well as helping test the code, this sample test data also allows to test
our migrations as we alter the schema of the database. By having sample data, we are
forced to ensure that any schema changes also handle sample data.
In most projects we've seen this sample data be fictional. However in a few projects
we've seen people use real data for the samples. In these cases this data's been extracted
from prior legacy systems with automated data migration scripts. Obviously you can't
migrate all the data right away, as in early iterations only a small part of the database is
actually built. But the idea is to iteratively develop the migration scripts just as the
application and the database are developed iteratively. Not just does this help flush out
migration problems early, it makes it much easier for domain experts to work with the
growing system as they are familiar with the data they are looking at and can often help
to identify problem cases that may cause problems for the database and application
design. As a result we are now of the view that you should try to introduce real data from
the very first iteration of your project.
Automatically Update all Database Developers
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13 It's all very well for people to make changes and update the master, but how do they
find out the master has changed? In a traditional continuous integration environment with
source code, developers update to the master before doing a commit. That way they can
resolve any build issues on their own machine before committing their changes to the
shared master. There's no reason you can't do that with the database, but we found a
better way. We automatically update everyone on the project whenever a change is made
to the database master. The same refractory script that updates the master automatically
updates everyone's databases. When we've described this, people are usually concerned
that automatically updating developer’s databases underneath them will cause a problem,
but we found it worked just fine. This only worked when people were connected to the
network. If they worked offline, such as on an airplane, then they had to resync with the
master manually once they got back to the office.
Accessing database management systems
This section explains the concepts you need to understand to establish a connection between VA Smalltalk and a database management system. It also includes instructions for establishing a database connection.
Database connection concepts Connecting to databases
o Working with connection specifications o Working with logon specifications o Establishing database connections o Working with active connections
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14o Working with database managers
Creating and deleting database tables
This section explains how to create and delete database tables using IBM Smalltalk.
Each section is illustrated with examples. You can use the examples without modifying
them to create and work with a database called CORPDATA. Each example builds on the
one before it. Follow the examples in the sequence given. Some of the code samples also
include a block of code you can evaluate to see the effect of the task you have just
performed. These code samples do such things as display all databases, display the names
of table columns, and display a result table from a query. The examples also provide
instructions for modifying the sample code to create a database of your own design. Each
section explains the parts of the sample code you need to change to work with your own
database.
If you need to query an existing database below is a list of activity:
Adding database support
Creating the application
Accessing a database management system
Loading database features
Connecting to a database manager
Defining a database query
Creating a query
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15 Using the SELECT Details window
Creating static DB2 queries
Setting fetch and update policies
Using the results of a query
Tearing off results
Using quick forms
Running a query
Working with the packeting container details part
Using a host variable
Running a query - host variables
Precompiling static SQL
Extra practice
What to watch for
More database techniques
Formatting query results
Displaying a result column
Displaying rows as strings
Creating more complex SELECT statements
Using high-level qualifiers
Sorting result table rows
Restricting result rows
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16 Using a dynamic WHERE clause
Nesting SELECT statements
Using the SQL Statement part
Defining an UPDATE statement
Defining an INSERT statement
Defining a DELETE statement
Using the Single-Row Query part
Using stored procedures
Using the Stored Procedure part
Running stored procedures
Handling result sets from stored procedures
Using static SQL
Adding database queries to packages
Database basics
Base database classes
Database definition classes
Data manipulation classes
Data link support classes (DB/2 only)
Accessing database management systems
Database connection concepts
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17 Connecting to databases
Working with connection specifications
Working with logon specifications
Establishing database connections
Working with active connections
Working with database managers
Creating and deleting database tables
Preparing to use the code samples
Creating and accessing tables
Adding rows and data
Deleting tables and databases
Querying databases
Writing SELECT statements
Selecting a row from a table
Selecting a row
Selecting rows from multiple tables (join operation)
Using a GROUP BY clause
Writing UPDATE, INSERT, and DELETE statements
Updating rows in a table
Inserting rows in a table
Deleting rows from a table
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18 Using database classes for scripts
Error detection and other tips
Handling error objects
Ensuring row schema consistency
Intercepting SQL 000 codes
Database menus
Query
Create
LOB definitions
Query
Create
UPDATE
INSERT
DELETE
Edit
Import
Export
Manual create
Manual edit
Host variables
Options
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19 High-level qualifiers
Pop-up menu for adding and deleting data fields
Add before
Add after
Edit
Delete
Get Schema
Pop-up menu for adding data fields
Get schema
Unary operator
Left operand
Right operand
Nested SELECT
Unary operator
Left operand
Right operand
Move before
Move after
Column value
Select all
Deselect all
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20 Ascending (ASC)
Descending (DESC)
Move before
Move after
Move before
Move after
Select all
Deselect all
Select all in table
Deselect all in table
Select all
Deselect all
Create
Edit
Delete
System values
Clause
WHERE
GROUP BY
HAVING
ORDER BY
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21 Column value
Specify expression
Unary operator
Left operand
Right operand
Nested SELECT
Pop-up menu for Database Query and Stored Procedure parts
Pop-up menu for Query Result Table and Current Row parts
Database Functions Category
Multi-row Query
Multi-Row Query - Settings
Query Result Table
Current Row
Single-Row Query
Single-Row Query - Settings
Result Row
SQL Statement
SQL Statement - Settings
Stored Procedure
Stored Procedure - Settings
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22Data Warehousing
Data warehousing takes a relatively simple idea and incorporates it into the
technological underpinnings of a company. The idea is that a unified view of all data that
a company collects will help improve operations. If hiring data can be combined with
sales data, the idea is that it might be possible to discover and exploit patterns in the
combined entity. The most basic component in a data warehouse is a relational database.
This database is the place where the data is stored. Relational databases are designed to
be able to efficiently insert new data and locate existing data using a standardized query
language. Given the fact that a company usually has very large amounts of data, the sizes
of these databases can reach terabytes (trillions of bytes). Underneath the database is a
maze of connections and transformations connecting the data warehouse with other
systems. Because data in a company is often created and stored in functionally specific
systems (e.g., a payroll system), the data may need to be replicated and moved between a
data warehouse and these other systems. There are a wide variety of tools that facilitate
this replication and movement process.
The design of the data architecture is probably the most critical part of a data
warehousing project. The key is to plan for growth and change, as opposed to trying to
design the perfect system from the start. The design of the data architecture involves
understanding all of the data and how different pieces are related. For example, payroll
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23data might be related to sales data by the ID of the sales person, while the sales data
might be related to customers by the customer ID. By connecting these two relationships,
payroll data could be related to customers (e.g., which employees have ties to which
customers).
Once the data architecture has been designed, you can then consider the kinds of reports
that you are interested in. You might want to see a breakdown of employees by region, or
a ranked list of customers by revenue. These kinds of reports are fairly simple. The power
of a data warehouse becomes more obvious when you want to look at links between data
associated with disparate parts of a organization (e.g., HR, accounts payable, and project
management).
Consider an exception report showing all projects more than 90 days in arrears that are
managed by someone with less than two years of experience. This report would be nearly
impossible to generate without the links between different databases that the warehouse
provides. In addition to the capability to link data together, a data warehouse can give
users the ability to view data at different levels of aggregation.
The Foundations of Data Mining
Data mining techniques are the result of a long process of research and product
development. This evolution began when business data was first stored on computers,
continued with improvements in data access, and more recently, generated technologies
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24that allow users to navigate through their data in real time. Data mining takes this
evolutionary process beyond retrospective data access and navigation to prospective and
proactive information delivery. Data mining is ready for application in the business
community because it is supported by three technologies that are now sufficiently mature:
Massive data collection
Powerful multiprocessor computers
Data mining algorithms
Commercial databases are growing at unprecedented rates. A recent META Group survey
of data warehouse projects found that 19% of respondents are beyond the 50 gigabyte
level, while 59% expect to be there by second quarter of 1996.1 In some industries, such
as retail, these numbers can be much larger. The accompanying need for improved
computational engines can now be met in a cost-effective manner with parallel
multiprocessor computer technology. Data mining algorithms embody techniques that
have existed for at least 10 years, but have only recently been implemented as mature,
reliable, understandable tools that consistently outperform older statistical methods.
The Scope of Data Mining
Data mining derives its name from the similarities between searching for valuable
business information in a large database — for example, finding linked products in
gigabytes of store scanner data — and mining a mountain for a vein of valuable ore. Both
processes require either sifting through an immense amount of material, or intelligently
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25probing it to find exactly where the value resides. Given databases of sufficient size and
quality, data mining technology can generate new business opportunities by providing
these capabilities:
Automated prediction of trends and behaviors. Data mining automates the
process of finding predictive information in large databases. Questions that
traditionally required extensive hands-on analysis can now be answered directly
from the data — quickly. A typical example of a predictive problem is targeted
marketing. Data mining uses data on past promotional mailings to identify the
targets most likely to maximize return on investment in future mailings. Other
predictive problems include forecasting bankruptcy and other forms of default,
and identifying segments of a population likely to respond similarly to given
events.
Automated discovery of previously unknown patterns. Data mining tools
sweep through databases and identify previously hidden patterns in one step. An
example of pattern discovery is the analysis of retail sales data to identify
seemingly unrelated products that are often purchased together. Other pattern
discovery problems include detecting fraudulent credit card transactions and
identifying anomalous data that could represent data entry keying errors.
Data mining techniques can yield the benefits of automation on existing software and
hardware platforms, and can be implemented on new systems, as existing platforms are
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26upgraded and new products developed. When data mining tools are implemented on high
performance parallel processing systems, they can analyze massive databases in minutes.
Faster processing means that users can automatically experiment with more models to
understand complex data. High speed makes it practical for users to analyze huge
quantities of data. Larger databases, in turn, yield improved predictions.
Databases can be larger in both depth and breadth:
Conclusions
In conclusion, comprehensive data warehouses that integrate operational data with
customer, supplier, and market information have resulted in an explosion of information.
Competition requires timely and sophisticated analysis on an integrated view of the data.
However, there is a growing gap between more powerful storage and retrieval systems
and the users’ ability to effectively analyze and act on the information they contain. Both
relational and OLAP technologies have tremendous capabilities for navigating massive
data warehouses, but brute force navigation of data is not enough. A new technological
leap is needed to structure and prioritize information for specific end-user problems.
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