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International Journal of Advances in
Engineering & Technology (IJAET)
ISSN : 2231-1963
VOLUME-7, ISSUE-3

Smooth, Simple and Timely Publishing of
Review and Research Articles!
July—2014

Date:

International Journal of Advances in Engineering & Technology, Jul. 2014.
©IJAET
ISSN: 2231-1963

Table of Content
Page No’s

Article Title & Authors (Volume 7, Issue 3, July-2014)

S. No.

Data Warehouse Design and Implementation Based on Quality
Requirements

1.

642-651

Khalid Ibrahim Mohammed
A New Mechanism in Hmipv6 to Improve Micro and Macro
Mobility

2.

652-665

Sahar Abdul Aziz Al-Talib
Efficiency Optimization of Vector-Controlled Induction Motor
Drive

3.

666-674

Hussein Sarhan
Flexible Differential Frequency-to-Voltage and Voltage-toFrequency
Converters
using
Monolithic
Analogue
Reconfigurable Devices

4.

675-683

Ivailo Milanov Pandiev
A Review Search Bitmap Image for Sub Image and the Padding
Problem

5.

684-691

Omeed Kamal Khorsheed
Potential Use of Phase Change Materials with Reference to
Thermal Energy Systems in South Africa

6.

692-700

Basakayi J.K., Storm C.P.
Improving Software Quality in the Service Process Industry
using Agility with Software Reusable Components as Software
Product Line: An Empirical Study of Indian Service Providers

7.

701-711

Charles Ikerionwu, Richard Foley, Edwin Gray
Produce Low-Pass and High-Pass Image Filter in Java
8.

712-722

Omeed Kamal Khorsheed
The Comparative Analysis of Social Network in International
and Local Corporate Business

9.

723-732

Mohmed Y. Mohmed AL-SABAAWI
i

Vol. 7, Issue 3, pp. i-vi

International Journal of Advances in Engineering & Technology, Jul. 2014.
©IJAET
ISSN: 2231-1963
Precise Calculation Unit Based on a Hardware Implementation
of a Formal Neuron in a FPGA Platform
10.

Mohamed
BOUSSAA

ATIBI,

Abdelattif

BENNIS,

Mohamed

Temperature Profiling at Southern Latitudes by Deploying
Microwave Radiometer
11.

12.

733-742

743-755

A. K. Pradhan, S. Mondal, L. A. T. Machado and P. K.
Karmakar
Development and Evaluation of Trolley-cum-Batch Dryer for
Paddy

756-764

Mohammed Shafiq Alam and V K Sehgal

13.

Joint Change Detection and Image Registration Method for
Multitemporal SAR Images

765-772

Lijinu M Thankachan, Jeny Jose

14.

Load - Settlement Behaviour of Granular Pile in Black Cotton
Soil

773-781

Siddharth Arora, Rakesh Kumar and P. K. Jain

15.

Harmonic Study of VFDS and Filter Design: A Case Study for
Sugar Industry with Cogeneration

782-789

V. P. Gosavi and S. M. Shinde

16.

Precipitation and Kinetics of Ferrous Carbonate in Simulated
Brine Solution and its Impact on CO2 Corrosion of Steel

790-797

G. S. Das
Performance Comparison of Power System Stabilizer with and
Without Facts Device
17.

Amit Kumar Vidyarthi, Subrahmanyam Tanala, Ashish
Dhar Diwan
Hydrological Study of Man (Chandrabhaga) River

18.

19.

ii

798-806

807-817

Shirgire Anil Vasant, Talegaokar S.D.
Crop Detection by Machine Vision for Weed Management

818-826

Vol. 7, Issue 3, pp. i-vi

International Journal of Advances in Engineering & Technology, Jul. 2014.
©IJAET
ISSN: 2231-1963
Ashitosh K Shinde and Mrudang Y Shukla
Detection & Control of Downey Mildew Disease in Grape Field
20.

Vikramsinh Kadam, Mrudang Shukla
Ground Water Status- A Case Study of Allahabad, UP, India

21.

22.

826-837

838-844

Ayush Mittal, Munesh Kumar
Clustering and Noise Detection For Geographic Knowledge
Discovery

845-855

Sneha N S and Pushpa
Proxy Driven FP Growth Based Prefetching
23.

24.

856-862

Devender Banga and Sunitha Cheepurisetti
Search Network Future Generation Network for Information
Interchange

863-867

G. S. Satisha

25.

A Brief Survey on Bio Inspired Optimization Algorithms For
Molecular Docking

868-878

Mayukh Mukhopadhyay
Heat Transfer Analysis of Cold Storage
26.

27.

879-886

Upamanyu Bangale and Samir Deshmukh
Localized RGB Color Histogram Feature Descriptor for Image
Retrieval

887-895

K. Prasanthi Jasmine, P. Rajesh Kumar
Witricity for Wireless Sensor Nodes
28.

29.

896-904

M. Karthika and C. Venkatesh
Study of Swelling Behaviour of Black Cotton Soil Improved
with Sand Column

905-910

Aparna, P.K. Jain and Rakesh Kumar
30.

iii

Effective Fault Handling Algorithm for Load Balancing using
Ant Colony Optimization in Cloud Computing

911-916

Vol. 7, Issue 3, pp. i-vi

International Journal of Advances in Engineering & Technology, Jul. 2014.
©IJAET
ISSN: 2231-1963
Divya Rastogi and Farhat Ullah Khan
Handling Selfishness over Mobile Ad Hoc Network
31.

32.

917-922

Madhuri D. Mane and B. M. Patil
A New Approach to Design Low Power CMOS Flash A/D
Converter

923-929

C Mohan and T Ravisekhar

33.

Optimization and Comparative Analysis of Non-Renewable and
Renewable System

930-937

Swati Negi and Lini Mathew

34.

A Feed Forward Artificial Neural Network based System to
Minimize DOS Attack in Wireless Network

938-947

Tapasya Pandit & Anil Dudy

35.

Improving Performance of Delay Aware Data Collection Using
Sleep and Wake Up Approach in Wireless Sensor Network

948-956

Paralkar S. S. and B. M. Patil

36.

Improved New Visual Cryptographic Scheme Using One
Shared Image

957-966

Gowramma B.H, Shyla M.G, Vivekananda

37.

Segmentation of Brain Tumour from Mri Images by Improved
Fuzzy System

967-973

Sumitharaj.R, Shanthi.K

38.

Implementation of Classroom Attendance System Based on
Face Recognition in Class

974-979

Ajinkya Patil, Mrudang Shukla

39.

A Tune-In Optimization Process of AISI 4140 in Raw Turning
Operation using CVD Coated Insert

980-990

C. Rajesh

40.

A Modified Single-frame Learning based Super-Resolution and
its Observations

991-997

Vaishali R. Bagul and Varsha M. Jain
iv

Vol. 7, Issue 3, pp. i-vi

International Journal of Advances in Engineering & Technology, Jul. 2014.
©IJAET
ISSN: 2231-1963

41.

Design Modification and Analysis of Two Wheeler Cooling
Fins-A Review

998-1002

Mohsin A. Ali and S.M Kherde
Virtual Wireless Keyboard System with Co-ordinate Mapping
42.

Souvik Roy, Ajay Kumar Singh, Aman Mittal, Kunal
Thakral
Secure Key Management in Ad-Hoc Network: A Review

43.

1009-1017

Anju Chahal and Anuj Kumar, Auradha
Prediction of Study Track by Aptitude Test using Java

44.

45.

1003-1008

1018-1026

Deepali Joshi and Priyanka Desai
Unsteady MHD Three Dimensional Flow of Maxwell Fluid
Through a Porous Medium in a Parallel Plate Channel under
the Influence of Inclined Magnetic Field

1027-1037

L. Sreekala, M. Veera Krishna, L. Hari Krishna and E.
Kesava Reddy

46.

Video Streaming
Networking Sites

Adaptivity

and

Efficiency

in

Social
1038-1043

G. Divya and E R Aruna

47.

Intrusion Detection System using Dynamic Agent Selection and
Configuration

1044-1052

Manish Kumar, M. Hanumanthappa

48.

Evaluation of Characteristic Properties of Red Mud For
Possible use as a Geotechnical Material in Civil Construction

1053-1059

Kusum Deelwal, Kishan Dharavath, Mukul Kulshreshtha

49.

Performance Analysis of IEEE 802.11e EDCA with QoS
Enhancements Through Adapting AIFSN Parameter

1060-1066

Vandita Grover and Vidusha Madan
Data Derivation Investigation
50.

v

1067-1074

S. S. Kadam, P.B. Kumbharkar

Vol. 7, Issue 3, pp. i-vi

International Journal of Advances in Engineering & Technology, Jul. 2014.
©IJAET
ISSN: 2231-1963

51.

Design and Implementation of Online Patient Monitoring
System

1075-1081

Harsha G S

52.

Comparison Between Classical and Modern Methods of
Direction of Arrival (DOA) Estimation

1082-1090

Mujahid F. Al-Azzo, Khalaf I. Al-Sabaaw

53.

Modelling Lean, Agile, Leagile Manufacturing Strategies: An
Fuzzy Analytical Hierarchy Process Approach For Ready
Made Ware (Clothing) Industry in Mosul, Iraq

1091-1108

Thaeir Ahmed Saadoon Al Samman

Members of IJAET Fraternity

vi

A-N

Vol. 7, Issue 3, pp. i-vi

International Journal of Advances in Engineering & Technology, July, 2014.
©IJAET
ISSN: 22311963

DATA WAREHOUSE DESIGN AND IMPLEMENTATION BASED
ON QUALITY REQUIREMENTS
Khalid Ibrahim Mohammed
Department of Computer Science, College of Computer, University of Anbar, Iraq.

ABSTRACT
The data warehouses are considered modern ancient techniques, since the early days for the relational
databases, the idea of the keeping a historical data for reference when it needed has been originated, and the
idea was primitive to create archives for the historical data to save these data, despite of the usage of a special
techniques for the recovery of these data from the different storage modes. This research applied of structured
databases for a trading company operating across the continents, has a set of branches each one has its own
stores and showrooms, and the company branch’s group of sections with specific activities, such as stores
management, showrooms management, accounting management, contracts and other departments. It also
assumes that the company center exported software to manage databases for all branches to ensure the safety
performance, standardization of processors and prevent the possible errors and bottlenecks problems. Also the
research provides this methods the best requirements have been used for the applied of the data warehouse
(DW), the information that managed by such a applied must be with high accuracy. It must be emphasized to
ensure compatibility information and hedge its security, in schemes domain, been applied to a comparison
between the two schemes (Star and Snowflake Schemas) with the concepts of multidimensional database. It
turns out that Star Schema is better than Snowflake Schema in (Query complexity, Query performance,
Foreign Key Joins),And finally it has been concluded that Star Schema center fact and change, while Snowflake
Schema center fact and not change.

KEYWORDS: Data Warehouses, OLAP Operation, ETL, DSS, Data Quality.

I.

INTRODUCTION

A data warehouse is a subject-oriented, integrated, nonvolatile, and time-variant collection of data in
support of management’s decisions. The data warehouse contains granular corporate data. Data in the
data warehouse is able to be used for many different purposes, including sitting and waiting for future
requirements which are unknown today [1]. Data warehouse provides the primary support for
Decision Support Systems (DSS) and Business Intelligence (BI) systems. Data warehouse, combined
with On-Line Analytical Processing (OLAP) operations, has become and more popular in Decision
Support Systems and Business Intelligence systems. The most popular data model of Data warehouse
is multidimensional model, which consists of a group of dimension tables and one fact table
according to the functional requirements [2]. The purpose of a data warehouse is to ensure the
appropriate data is available to the appropriate end user at the appropriate time [3]. Data warehouses
are based on multidimensional modeling. Using On-Line Analytical Processing tools, decision
makers navigate through and analyze multidimensional data [4].
Data warehouse uses a data model that is based on multidimensional data model. This model is also
known as a data cube which allows data to be modeled and viewed in multiple dimensions [5]. And
the schema of a data warehouse lies on two kinds of elements: facts and dimensions. Facts are used to
memorize measures about situations or events. Dimensions are used to analyze these measures,
particularly through aggregations operations (counting, summation, average, etc.) [6, 7]. Data
Quality (DQ) is the crucial factor in data warehouse creation and data integration. The data
warehouse must fail and cause a great economic loss and decision fault without insight analysis of

642

Vol. 7, Issue 3, pp. 642-651

International Journal of Advances in Engineering & Technology, July, 2014.
©IJAET
ISSN: 22311963
data problems [8]. The quality of data is often evaluated to determine usability and to establish the
processes necessary for improving data quality. Data quality may be measured objectively or
subjectively. Data quality is a state of completeness, validity, consistency, timeliness and accuracy
that make data appropriate for a specific use [9]. The paper is divided into seven sections. Section 1
introduction, Definition of Data Warehouse and The Quality of Data Warehouse. Section 2 presents
related work, Section 3 presents Data Warehouse Creation and the main idea is that a Data warehouse
database gathers data from an overseas trading company databases. Section 4describes Data
Warehouse Design For this study, we suppose a hypothetical company with many branches around
the world, each branch has so many stores and showrooms scattered within the branch location. Each
branch has a database to manage branch information. Section 5 describes our evaluation Study of
Quality Criteria for DW, which covers aspects related both to quality and performance of our
approach, and the obtained results, and work on compare between star schema and snowflake schema.
Section 6 provides conclusions. Finally, Section 7 describes open issues and our planned future work.

1.1 Definition of Data Warehouse
A data warehouse is a relational database that is designed for query and analysis rather than for
transaction processing. It usually contains historical data derived from transaction data, but it can
include data from other sources. It separates analysis workload from transaction workload and
enables an organization to consolidate data from several sources. In addition to a relational database,
a data warehouse environment can include an extraction, transportation, transformation, and loading
(ETL) solution, an online analytical processing (OLAP) engine, client analysis tools, and other
applications that manage the process of gathering data and delivering it to business users. A common
way of introducing data warehousing is to refer to the characteristics of a data warehouse as set forth
by William Inmon [10]:
1. Subject Oriented.
2. Integrated.
3. Nonvolatile.
4. Time Variant.

1.2 The Quality of Data Warehouse
Data quality has been defined as the fraction of performance over expectancy, or as the loss imparted
to society from the time a product is shipped [11]. The believe was the best definition is the one found
in [12, 13, and 14]: data quality is defined as "fitness for use". The nature of this definition directly
implies that the concept of data quality is relative. For example, data semantics is different for each
distinct user. The main purpose of data quality is about horrific data - data which is missing or
incorrect or invalid in some perspective. A large term is that, data quality is attained when business
uses data that is comprehensive, understandable, and consistent, indulging the main data quality
magnitude is the first step to data quality perfection which is a method and able to understand in an
effective and efficient manner, data has to satisfy a set of quality criteria. Data gratifying the quality
criterion is said to be of high quality [9].

II.

RELATED WORK

In this section we will review related work in Data Warehouse Design and Implementation Based on
Quality Requirements. We will start with the former. The paper introduced by Panos Vassiladis,
Mokrane Bouzegeghoub and Christoph Quix, (2000), the proposed approach covers the full lifecycle
of the data warehouse, and allows capturing the interrelationships between different quality factors
and helps the interested user to organize them in order to fulfill specific quality goals. Furthermore,
they prove how the quality management of the data warehouse can guide the process of data
warehouse evolution, by tracking the interrelationships between the components of the data
warehouse. Finally, they presented a case study, as a proof of concept for the proposed methodology
[15]. The paper introduced by Leo Willyanto Santoso and Kartika Gunadi, (2006), this paper
describes a study which explores modeling of the dynamic parts of the data warehouse. This
metamodel enables data warehouse management, design and evolution based on a high level
conceptual perspective, which can be linked to the actual structural and physical aspects of the data

643

Vol. 7, Issue 3, pp. 642-651


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