SALES BASED DATA EXTRACTION FOR BUSINESS INTELLIGENCE

Rupak Bhattacharyya et al. (Eds) : ACER 2013,
pp. 279–286, 2013. © CS & IT-CSCP 2013 DOI : 10.5121/csit.2013.3226
SALES BASED DATA EXTRACTION FOR
BUSINESS INTELLIGENCE
Sukanta Singh
Department of Computer Science & Engineering, Global Institute of
Management & Technology, Krishnagar, Nadia, West Bengal
sukantasingh2008@gmail.com
ABSTRACT
Data warehouse use has increased significantly in recent years and now plays a fundamental
role in many organizations’ decision-support processes. An effective business intelligence
infrastructure that leverages the power of a data warehouse can deliver value by helping
companies enhance their customer experience. In this paper is to generate reports with various
drilldowns and slier conditions with suitable parameters which provide a complete business
solution which is helpful for monitor the company inflow and outflow. The goal of the work is
for potential users of the data warehouse in their decision making process in the Business
process system to get a complete visual effort of those reports by creating the chart and grid
interface from warehouse. The example in this paper relate directly to the Adventure Work Data
Warehouse Project implementation which helps to know the internet sales amount according to
different date.
KEYWORDS
Data Warehouse Architecture, ETL, Business Intelligence (BI), Dimension Table, Fact table,
Star Schema, Snowflake Schema, Business Intelligence Reporting
1. INTRODUCTION
A data warehouse is the main repository of an organization’s historical data, its corporate
memory. It contains the raw material for management’s decision support system. The critical
factor leading to the use of a data warehouse is that a data analyst can perform complex queries
and analysis, such as data mining, on the information without slowing down the operational
systems. [3] Data warehouse contain a wide variety of data that present a coherent picture of
business conditions at a single point in time. It is a repository of integrated information, available
for queries and analysis. [6]
Organizations now consolidate information from several databases into a data warehouse.
Organization decision making requires a comprehensive view of all aspects of an enterprise, and
many organizations have therefore created consolidated data warehouses that contain data drawn
from several databases maintained by different business units together with historical and
summary information. The trend toward data warehousing is complemented by an increased
emphasis on powerful analysis tools. [2]
By working with real deployment scenarios, we gain a complete understanding of how to work
with the tools. My goal is to address the full gamut of concerns that a large company would face
during their own real-world deployment. [1]This paper focuses on the SQL Server Integration

280 Computer Science & Information Technology (CS & IT)
Services (SSIS) extraction, transformation, and loading (ETL) design for Project Adventure Work
Data Warehouse.
In this system, metadata plays an important role and provides the foundation for all actions in all
stages. It can be considered as glue sticking together all individual parts of these systems. In this
paper, I propose my data warehouse architecture with new metadata layer and describe the design
and implementation of star schema in conceptual data model.
2. THE DATA WAREHOUSE ARCHITECTURE
This architecture consists of core components and processes which constitute a complex
analytical information system. The bottom layer of the warehouse connects to the data stores of
operational information systems (e.g. accounting, sales, human resources) in order to extract
relevant data which are pooled into a staging area. In addition to this, external data (e.g. from
market research institutes or financial services) can be acquired to complete the data needed for
management decisions.[7] The staging area serves as a temporary data store and allows the
consolidation of data from heterogeneous sources. A typical transformation task is to remove
homonyms (e.g. using an identical label for different types of attributes) and synonyms (e.g. using
two different labels for an attribute). As a result, the integrated data is loaded into a central data
warehouse layer which is typically normalized in order to avoid redundancies. In figure 1 it is
shown how different data from multiple sources can be fetched and store in data warehouse as
form of metadata. Finally, generate reports from this data warehouse.
Figure 1: Data Warehouse Architecture
Typically, the informational needs of the marketing department differ from those of the
accounting department. As a consequence of this, specific departmental views on the data have to
be created.[4] These views, which are called data marts, can be further customized in order to
comply with the informational needs of single users (e.g., a specific salesperson in a defined
region). To get information from these data marts, end users are provided with a set of tools
which allow analytical processing. Most commonly are report generation tools which support
simple aggregations (e.g. calculation of statistical measures like mean values, etc.). In order to
provide interactive analysis with user defined views, OLAP tools are frequently used. These early
tasks of the data warehouse process are executed by use of ETL-tools (extraction, transformation,

Computer Science & Information Technology (CS & IT) 281
loading). These tools provide connectivity to a broad set of different data storage formats (e.g.
different database systems like Oracle, DB2 or SQL Server or different text file formats).
3. MULTIDIMENSIONAL DATA MODEL AND SCHEMAS
Multidimensional models highly take advantage of inherent relationships existing in data to
populate data in multidimensional matrices referred to as data cubes. The Multidimensional
storage model involves two types of tables: dimension tables and fact tables.
In Data Warehouse schema is three types- Star Schema, Fact Constellation Schema and
Snowflake schema. Two common multidimensional schemas are star schema and snowflake
schema.
3.1. Dimension Table
Dimensional modeling produces dimension tables in which each table contains fact attributes that
are independent of those in other dimensions. For example, a customer dimension table contains
data about customers, a product dimension table contains information about products, and a store
dimension table contains information about stores.
3.2. Fact Table
A key characteristic of a fact table is that it contains numerical data (facts) that can be
summarized to provide information about the history of the operation of the organization. Each
fact table also includes a multipart index that contains as foreign keys the primary keys of related
dimension tables, which contain the attributes of the fact records.
3.3. Star Schema
The star schema consists of a fact table with a single table for each dimension. In star schema a
single fact table and for each dimension one dimension table and does not capture hierarchies
directly. The primary keys of each of the dimension tables are linked together to form the
composite primary key of the fact table. In figure 2 show a star schema design, there is only one
de-normalized table for a given dimension.

Figure 2: Star Schema for proposed system
3.4. Snowflake Schema
The snowflake schema is a variation on the star schema in which the dimensional tables from a
star schema are organized into a hierarchy by normalizing them. But, in figure 3 show snowflake
schema represent dimensional hierarchy directly by normalizing tables and easy to maintain and
saves storage.
Figure 3: Snowflake Schema for proposed system
4. DATA EXTRACTION FOR PROPOSED SYSTEM
An ETL application is the most comprehensive line between two points. These two points are the
enterprise and all its source systems on one end and a data warehouse on the other. The ETL
process is much more than code written to move data. The ETL architect also serves as the central

point for understanding the various technical standards that need to be developed if they don't
already exist. In figure 4, a query for retrieving data from ExcelSheet to MS SQL Server for
Customer Table shown that how data can be extracted from different data source and finally load
as data mart in warehouse.
INSERT INTO
AdventureWorksDW(CustomerKey,GeographyKey,CustomerAlternateKey,
Title,FirstName,MiddleName,LastName,NameStyle,BirthDate,MaritalStatus,S
uffix,
Gender,EmailAddress,YearlyIncome,TotalChildren,NumberChildrenAtHome,
EnglishEducation,SpanishEducation,FrenchEducation,EnglishOccupation,
SpanishOccupation,FrenchOccupation,HouseOwnerFlag,NumberCarsOwned,
AddressLine1,AddressLine2,Phone,DateFirstPurchase,CommuteDistance)
SELECT CustomerKey,GeographyKey,CustomerAlternateKey,Title,FirstName,
MiddleName,LastName,NameStyle,BirthDate,MaritalStatus,Suffix,Gender,
EmailAddress,YearlyIncome,TotalChildren,NumberChildrenAtHome,
EnglishEducation,SpanishEducation,FrenchEducation,EnglishOccupation,
SpanishOccupation,FrenchOccupation,HouseOwnerFlag,NumberCarsOwned,Addre
ssLine1,
AddressLine2,Phone,DateFirstPurchase,CommuteDistance
FROM OPENROWSET
('Microsoft.Jet.OLEDB.4.0',
'Excel 8.0;Database=C:Test.xls;',
'SELECT CustomerKey,GeographyKey,CustomerAlternateKey,
Title,FirstName,MiddleName,LastName,NameStyle,BirthDate,
MaritalStatus,Suffix,Gender,EmailAddress,YearlyIncome,TotalChildren,
NumberChildrenAtHome,EnglishEducation,SpanishEducation,FrenchEducation,
EnglishOccupation,SpanishOccupation,FrenchOccupation,HouseOwnerFlag,Num
berCarsOwned,
AddressLine1,AddressLine2,Phone,DateFirstPurchase,CommuteDistance FROM
[Sheet2]')
--EXEC sp_configure 'show advanced options', 1;
--GO
--RECONFIGURE;
--GO
--EXEC sp_configure 'Ad Hoc Distributed Queries', 1;
--GO
--RECONFIGURE;
--GO
Figure 4: Retrieving data from ExcelSheet to MS SQL Server for Customer Table
5. IMPLEMENTATION FOR PROPOSED SYSTEM
In proposed system user can be able for visualization about total Internet sales amount, Internet
gross profit etc. according to date, geography. BI Reporting is what the data warehouse customers
see. All the hardware, software, data architectures, data models, Source System Analysis, Target
System Analysis, and ETL applications culminate in data displayed on a computer monitor or
printed on a piece of paper. All that work, the effort and investment, will be counted a blazing
failure if the BI Reporting fails to deliver or all of that work, the effort and investment, will be
counted a tremendous success if the BI Reporting delivers the data and value expected of a data
warehouse, which will lead to subsequent iterations of data warehouse development. Here, some
screenshots of web based business intelligence report of Adventure Work database included.
Figure 5 generate a report about chart view of internet sales amount by year wise which is fetch
from Adventure Work Data warehouse cube structure.

Figure 5: Chart View of Internet Sales Amount by Date
Figure 6 show grid view of Internet Sales amount by customer wise according to date. Here
different Internet sales amount are listed according to different countries by employee.
Figure 6: Grid View of Internet Sales Amount by Customer by Employee
Figure 7: Chart Views of Internet Sales Amount and Internet Gross Profit by Geography by Date

Chart View and Grid View of Internet Sales Amount and Internet Gross Profit by Geography
(i.e., different country wise) depicted in figure 7 and figure 8. Here you can see the details of
Average unit price, gross profit and sales amount of Internet according to different time slot of a
year.
Figure 8: Grid Views of Internet Average Unit Price, Internet Gross Profit and Internet Sales Amount by
Date
6. CONCLUSION
Create a complete web Business Intelligence tool based on an organization using Data Warehouse
and maintain a strong ETL process which will be dynamic and must more efficient. Hope this
web tool is very useful for an organization’s financial department. Data warehouses are still an
expensive solution and typically found in large firms. The development of a central warehouse is
a huge undertaking and capital intensive with large, potentially unmanageable risks.
Data warehousing is the leading and most reliable technology used today by companies for
planning, forecasting, and management. After the evolution of the concept of data warehousing
during the early 90’s it was thought that this technology will grow at a very rapid pace but
unfortunately it’s not the reality. A major reason for data warehouse project failures is poor
maintenance. Without proper maintenance desired results are nearly impossible to attain from a
data warehouse. Unlike operational systems data warehouses need a lot more maintenance and a
support team of qualified professionals is needed to take care of the issues that arise after its
deployment including data extraction, data loading, network management, training and
communication, query management and some other related tasks.
REFERENCES
[1] S. Sai Satyanarayana Reddy, A.Lavanya, Dr.V.Khanna, Dr.L.S.S.Reddy “RESEARCH ISSUES ON
DATA WAREHOUSE MAINTENANCE” International Conference on Advanced Computer Control
2008 IEEE
[2] Huang, L. & Ulrich, T. & Hemmje, M.& Neuhold, E.J.,"Adaptively Constructing the Query Interface
for Meta-Search Engines." GMD-IPSI Dovlivostr.15, Darmstadt, 64293 Gemany, 2001.
[3] Nilakanta. Sree, Scheibe. Kevin, “Dimensional issues in agricultural data warehouse designs”,
Computers and electronics in agriculture, Vol.60, No.2, 2008, pp.263-278
[4] Jan Chmiel, Tadeusz Morzy, Robert Wrembel, “Multiversion Join Index For Multiversion Data
Warehouse”, Information and Software Technology Vol.51 No.1, 2009, pp.98-108.
A. Simitsis, “Modeling and Managing ETL Processes,” Proceedings of the VLDB PhD Workshop,
Germany, 2003.

[5] Than Than Wai, Sint Sint Aung, “Metadata Based Student Data Extraction from Universities Data
Warehouse” National Inst of Tech Teachers Training and Res 2009 IEEE.
[6] Hanson, Joseph H. An Alternative Data Warehouse Structure for Performing Updates. December
1996, UMI Press.
[7] Labio, W.J. and H. Garcia-Molina, Eficient Snapshot Diferential Algorithms for Data Warehousing,
Technical Report. 1996, Stanford Univ: Palo Alto.
[8] McElreath, J., Data Warehouses: An Architectural Perspective. Perspectives, November 1995.
Computer Sciences Corporation, El Segundo, CA: p. 13.
[9] Meta Software Corp., Using DesigdIDEF to Simulate Workflow Models with ServiceMOdel, 1995,
Meta Software Corp.: Cambridge MA. p. 1-25.
[10] Zhuge, Y.H.G.-M., Jachim Hammer, Jennifer Widom, View Maintenance in a Warehousing
Environment. Technical Report. 1994. Stanford: Palo Alto.
[11] CHEN Xi-ping; ZHANG Liang; LI Chun-xiang; CAO Jie, “Data warehouse and its application to
decision supporting system of public traffic”, Journal of Gansu University of Technology, 2001(04).
AUTHORS
Sukanta Singh received B.Tech in Computer Science & Engineering from Govt.
College of Engineering & Ceramic Technology, WBUT, India in 2008 and M.Tech in
Computer Science & Engineering from Kalyani Govt. Engineering College, WBUT,
and India in 2010. He has more than 3.5 years of teaching experience and currently
working as an Assistant Professor in Global Institute of Management & Technology,
West Bengal, India. His fields of interest are soft computing, theory of computation
and data warehouse.

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SALES BASED DATA EXTRACTION FOR BUSINESS INTELLIGENCE