Jemilson Pierrelouis.doc

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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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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.

References


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