INTEGRATING RELATED XML DATA INTO MULTIPLE DATA WAREHOUSE SCHEMAS

Natarajan Meghanathan, et al. (Eds): ITCS, SIP, JSE-2012, CS & IT 04, pp. 357–367, 2012.
© CS & IT-CSCP 2012 DOI : 10.5121/csit.2012.2133
INTEGRATING RELATED XML DATA INTO
MULTIPLE DATA WAREHOUSE SCHEMAS
Soumya Sen1
, Ranak Ghosh2
, Debanjali Paul3
, Nabendu Chaki4
1,4
University of Calcutta Kolkata -700009 West Bengal, India
1
iamsoumyasen@gmail.com
4
nabendu@ieee.org
2,3
Barrackpore Rastraguru Surendranath College, Kolkata - 700120 West Bengal,
India
2
ranakghoshmail@gmail.com
3
debanjali.rimi@gmail.com
ABSTRACT
XML is a worldwide standard to represent data in web based system. Numbers of organizations
use XML for e-commerce and internet based applications. On the other hand, data warehouse is
used by most of the organizations for making decisions on their business by analyzing
operational data. Integration of business logic based on data warehouse and XML based system
has therefore emerged as a demanding area of research interest. This paper illustrates an
approach to integrate XML data based on multiple related XML Schemas, to its compatible data
warehouse schemas based on relational online analytical processing (ROLAP). The novelty of
the work is that more than one type of data warehouse schemas could be identified from a single
related XML schema. A new data structure, Schema Graph has been proposed in the process.
KEYWORDS
XML, XML Schema, Data Warehouse, Fact Constellation Schema, Star Schema, Snowflake
Schema, Schema Graph
1. INTRODUCTION
Data warehouse [9] is subject-oriented, non-volatile, integrated, time variant collection of data
which helps in developing strategic decisions. The popular way of describing data warehouse is
through multi-dimensional model [9]. Multidimensional model is based on some central theme
which is represented by fact table [9]. A fact table is constructed from multiple dimension tables
[9]. The association between these fact table and dimension tables are generally represented
through three data warehouse schemas namely star schema, snowflake schema and fact
constellation.
XML is well established standard for semi-structured data and also poses several benefits in web
environment. Many organizations thus prefer the use of XML extensively. Data could come from
different heterogeneous sources [5], some of them be may be semi-structured. In order to
integrate these heterogeneous data they could be converted to XML to take the advantages of
XML standard. XML data is associated with DTD [10] or XML schema [10]. XML provides

358 Computer Science & Information Technology (CS & IT)
Document Type Definition (DTD), which explains precisely what elements could appear as
document and what the contents of the elements and attributes are. Some of the approaches show
how XML data based on DTD [6] [7] [8] have been converted to data warehouse schema.
However DTD have some limitations. DTD do not have any built-in data types; also do not
support user-derived data types and allow only limited control over cardinality. Whereas XML
schemas are more powerful to represent XML document structure and overcome the limitations
of XML DTD. An XML schema could works as a fact repository [12]. The increasing use of Web
and the requirement of integrating data from different data sources expedite the research in
converting XML schema to equivalent data warehouse system. Retrieving multi-dimensional data
model [11] from XML sources became important over the years. XML document could be
summarized [13] and represented in the form of data warehouse. Moreover due to the
transmission of data over public domain like web, secure XML data warehouses [14] are need to
be formed. Several researches have been done for integrating XML data in data warehouse [1] [2]
[3] [4].
The paper [1] converts XML schema either to star schema or snowflake schema. Moreover this
paper works only with single XML schema. The paper [2] proposes a method to design multiple
cubes of multidimensional model from XML schema. The paper [3] proposes a method to convert
the contents of the XML schema to multiple schemas of the multi dimensional model. However
all these generated schemas are converted to star schema only. The paper [4] proposes XML
schema conversion to OLAP cube. This paper focuses on converting XML schema to
corresponding data warehouse schema by identifying fact and dimension tables.
However, the existing methods studied and cited here are only capable to identify a single data
warehouse schema from a given XML schema. In this paper, a framework is proposed to detect
more than one data warehouse schema from the given XML schema and support the identification
of all three types of data warehouse schemas.
The process of obtaining the data warehouse schema from the XML schema has been proposed
here. At first XML schema is converted to a schema graph (described in Section 3.2). Schema
graph is a new data structure proposed here for the conversion process. In next stage the fact and
dimension tables are being identified from the schema graph (described in Section 3.3). Once
these tables have been identified, the measure of fact table is taken from the user. Depending on
the relationship between dimension tables and fact tables one or more star schemas, snowflake
schemas or fact constellation schema are being identified (described in Section 3.4).
2. PROPOSED DATA STRUCTURE
Schema Graph: A schema graph is a representation of entities found in the XML Schema. The
graph has the following properties:
a. It is a level wise separable graph.
b. The entities encountered in the XML are represented through vertices of the graph.
c. The name of the vertices of the Graph would be same as the name of the entities.
Holder Element (HE): The elements that have no predecessor are in the Schema Graph are
called Holder Elements. They are placed in Level-1 of the graph
Contained Element (CE): The elements that are directly connected to the HE’s are called
Contained Element. They are placed in Level-2 of the graph.

Computer Science & Information Technology (CS & IT) 359
SE
CE
Level-2
HE
Level-1 Level-3 Level-4
Secondary Elements (SE): The elements that are directly connected to the CE’s are called
Secondary Elements. They are placed in Level-3 of the graph.
If elements in the graph appear as connected to SE, they would be placed in level-4. The new
vertices that would be connected to the vertices of level-4 would be placed in level-5.
Subsequently new level could be created if the new entities appear in the graph connected to the
previous level.
All the entities are represented through rectangular vertex in Schema Graph. The attributes are
represented in the Schema Graph in an Oval shaped vertex. They are connected to the
corresponding entities. In Figure 1 entities are shown only.
Figure 1. Schema Graph Along with HE, CE and SE
3. CONVERSION OF RELATED XML SCHEMA TO WAREHOUSE
SCHEMA
3.1. Overview of The Proposed Framework
The modeling of Warehouse Schema from a related XML Schema is performed in two steps. At
first Schema graph is formed from the XML schema (described in section 3.2). The elements of
the schema graph are identified as HE, CE and SE. In the next stage fact tables and dimension
tables are identified. Moreover if any element doesn’t have any primary key then a key attribute
is added to it (described in section 3.3). Each HE would correspond to a fact table and would
make an entry in the fact table, the key attribute of the CE’s that are connected to the HE are
entered in the corresponding fact table for that HE. CE’s would be the dimension tables. If SE’s
are found connected with CE the primary keys of SE’s are placed in CE. If SE’s are present even
after level-3, the primary keys of the higher level are placed in the table corresponding to the SE
of immediate lower level. Next the warehouse schema structure is identified (section 3.4). There
are three separate methodologies for three data warehouses schemas. Identification of star
schema, snowflake schema and fact constellation are described in section 3.4.1, 3.4.2 & 3.4.3
respectively.
3.2. Procedure to Build Schema Graph From Related XML Schema
In order to build the schema graph from the XML schema at first the entities are identified. In
XML schema those elements which are not nested within some elements are termed as HE and

are placed at level-1 of the schema graph. After that if some element is being found to be declared
within the body of HE they are being classified as CE and placed at level-2 of the schema graph.
Again if new elements are declared within body of CE they are being placed at level-3 of the
schema graph and are termed as SE. If the nested elements are further found for SE they are
placed in the next level of the schema graph and are also termed as SE. This scanning would be
continued till all the elements which are being nested are identified. Though new levels are
created in the schema graph based on the higher level of nesting all of them are termed as SE, that
is, elements in level-3 or more than level-3, all are being termed as SE. This algorithm is shown
in Figure 2.
Figure 2. Algorithm to Construct Schema Graph from XML Schema
3.3. Identifying Fact and Dimension Tables
While drawing the Schema Graph three entities have been specified: HE, CE, SE. The CE’s are
chosen as the dimension table and each HE would prompt towards a fact-table. If there exists any
SE, then the primary keys of them would be included in the corresponding CE’s. Similarly, if
there are entities beyond level-3, then the primary keys of entities of level (i+1) appear as foreign
key in the connected entity of level i. If any entity is found without having a primary key, then
primary key is added to it as: Name of entity + “_id”
3.4. Identification of the Schema Structure of Data Warehouse
Once the fact and dimension tables are identified, the corresponding DW schema need to be
a) Find out those entities in XML schema that have no predecessor and
denote them as the starting vertices or holders for the entire graph. These
entities would be known as HE. They would placed in the Level-1 of the
graph.
b) For all HE (i=1 to n) perform following : (n is the total number of
HE)
i. Find the sequence of elements under i-th HE :
If it is an element then create a vertex for it into the graph and
connect it with i-th HE. These elements (vertices) would be denoted
as CE. CE would be placed in the level-2 of the graph.
Else if it is an attribute it would be considered as an attribute of the
corresponding HE.
ii. For all CE (j=1 to m) perform following: (m is the number of CE in
the HE)
iii. Scan the XML Schema for j-th CE:
If it is an element then place it into the graph and connect it with its
CE. These elements would be known as SE. SE would be placed in
the level-3 of the graph.
Else if it is an attribute place it would be considered as an attribute of
CE.
iv. For every SE (k=1 to p) : (p is the total number of SE at that level)
Repeat the steps to include the entities and attributes as they
encountered. Whenever a new entity is added new level is created for
it.
End For /* SE */
End For /* CE */
End For /* HE */

build. Here three popular data warehouse schemas are identified namely star schema, snowflake
schema and fact constellation schema. This is done by checking the nature of connection among
elements within the schema graph. If the schema is found as disconnected, more than one schema
is identified. The numbers of distinct components in the schema is same as the number of DW
schema identified.
3.4.1 Procedure Star Schema
A data warehouse schema is identified as star schema if the schema graph consists of HE and
CE’s only. Every fact table is named as the name of HE + “Fact”. The primary keys of each of the
connected CE are placed in fact table and the CE’s are act as dimension tables in the star schema.
This algorithm is shown in Figure 3.
Figure 3. Algorithm to Construct Star Schema
3.4.2 Procedure Snowflake Schema
A data warehouse schema is identified as snowflake schema if the schema graph consists of HE,
CE and SE provided that HE’s are not connected. At least one SE should exist for some CE in the
schema graph to be identified as snowflake schema. Every fact table is named as the name of HE
+ “Fact”. The primary key of the connected CE are placed in fact table and the CEs act as
dimension tables. Similarly the dimension tables would contain the primary key of the tables
represented by SE. If further levels of SE are found they are represented in the schema by placing
the primary key in the immediate previous level of SE tables. The algorithm is shown in Figure
4.
Figure 4. Algorithm to Construct Snowflake Schema
Partition the Schema Graph Level wise.
Identify HE
For HE:
a. Form a Fact-Table with the name of HE + ”Fact” and Primary key of
the HE
b. Specify the CE connected with this HE; include the primary keys of
each CE into the Fact-Table.
End For /*HE*/
Partition the Schema Graph Level wise.
Identify the HE
For each HE:
a. Form a Fact-Table with the name of HE + ”Fact” and primary key of the
HE
b. Specify the CE connected with this HE, include the primary keys of each
CE into the Fact-Table.
c. For each CE find SEs, if any.
If found Connect it with its CE using the primary key of the SE.
d. For each SE:
Check if there are any SE:
If further level of SE is found the primary key of the new SE of
the immediate higher level is placed in the SE of current level;
End For /* SE */
End For /* CE */
End For /* HE */

3.4.3 Procedure Fact-Constellation
A data warehouse schema is identified as fact constellation if the fact tables are found connected.
The number of HE’s in a component of the schema is same as the number of fact tables for the
component. Every fact table is named as the name of HE + “Fact”. The primary keys of the
connected CEs are placed in fact table. If further levels of SEs are found, these are represented by
placing their primary keys in the immediate previous level of SEs. This algorithm is shown in
Figure 5.
Figure 5. Algorithm to Construct Fact Constellation
4. EXAMPLE
In this section an illustrative example is presented to explain the conversion methodology of an
XML Schema to its equivalent data warehouse schema
Partition the Schema graph level wise;
Identify the HE
For each HE:
a. Form a Fact-Table with the HE +”Fact” and Primary key of the HE
b. Specify the CE connected with this HE, include the primary keys of
each CE into the Fact-Table.
c. For each CE check if there are any SE:
If Found
Connect SE with its CE including the primary key of the SE in the
CE;
d. For each SE:
Check if there are any SE:
If further level of SE is found the primary key of the new SE of
the immediate higher level is placed in the SE of current level
End For /* SE */
End For /* CE */
End For /* HE */

Figure 6. Example of XML Schema
<?xml version="1.0"?>
<xsd:schema xmlns:xsd="http??www.w3.org/2001/xmlschema">
<xsd:elementname="sales">
<xsd:complexType>
<xsd:sequence>
<xsd:elementname="Item" type="ItemType">
<xsd:sequence>
<xsd:elementname="name" type="xsd:string" use="required"/>
<xsd:elementname="Title" type="xsd:string" use="required"/>
</xsd:sequence>
<xsd:elementname="branch" type="BranchType">
<xsd:sequence>
<xsd:elementname="type" type="xsd:string" use="required"/>
<xsd:elementname="Branch_id" type="xsd:string" use="required"/>
</xsd:sequence>
<xsd:elementname="Location" type="LocationType"/>
<xsd:elementname="Sales_id" type="xsd:string" use="required"/>
<xsd:attributename="unit" type="xsd:positiveinteger"
use="required"/>
</xsd:sequence>
</xsd:complextype>
</xsd:element>
<xsd:elementname="shipping">
<xsd:complexType>
<xsd:sequence>
<xsd:elementname="shipper" type="ShipperType">
<xsd:sequence>
<xsd:elementname="name" type="xsd:string"
use="required"/>
<xsd:elementname="Shipper_id" type=locationType
use="required"/>
</xsd:sequence>
<xsd:elementname="Location" type="LocationType"/>
<xsd:attributename="Shipping_id" type=ShipperType
use="required"/>
<xsd:attributename="location_to" type=LocationType
use="required"/>
<xsd:attributename="unit" type="xsd:string"
use="required"/>
</xsd:sequence>
</xsd:complextype>
</xsd:element>
<xsd:complexTypename="LocationType">
<xsd:sequence>
<xsd:elementname="street" type="xsd:string"
use="required"/>
<xsd:elementname="city" type="xsd:string"
use="required"/>
</xsd:sequence>

In the XML schema (Figure 6 ) by applying the proposed methodology described in section 3.2
schema graph is drawn. It is found that the elements Sales and Shipping have no predecessors.
Thus they would be identified as HE. For the HE Sales three CE’s are identified namely they are
Item, Branch and Location. All these CE’s have no SE. For the HE Shipping two CE’s are
identified. They are Shipper and Location. Both of these CE don’t have any SE. The attributes of
every entity is connected with the corresponding entity. The schema graph obtained is shown in
Figure 7.
Figure 7. Schema Graph of XML Schema
Now fact and dimension tables are identified using the procedure described in section 3.3. As the
schema graph contains two HE, two fact tables would be constructed namely Sales_Fact and
Shipping_Fact. The dimension tables for Sales_fact would be Sales, Item, Branch, Location. The
dimension tables for Shipping_Fact would be Shipping, Shipper, Location. Now primary keys are
required to be identified.
We assume that the entity Sales, Shipping, Branch, Shipper have the primary key hence we need
to add primary key for the entity Item and Location. The primary keys of the entity Item and
Location are named as Item_id and Location_id (described in section 3.3). These new primary
key attributes are shown in Figure 8.
The next step is to identify the proper data warehouse schema from the schema graph. As the HE
Sales and Shipping are connected through the CE Location, procedure Fact-Constellation
(described in section 3.4.3) is applied to build the data warehouse schema. In both of the fact
tables there is an attribute user_measures which would be given by the users. This is shown in
Figure 8.
Unit
Type
City
Unit
Title
Shipping_id
Item
Location
Branch
Shipping
Sales
Location_to
Name
Street
Name
Shipper
Sales_id
Shipper_id
Branch_id

Figure 8. Fact Constellaion Schema from the Schema Graph of Figure-7
5. CONCLUSION
This paper provides a framework to convert a related XML schema to data warehouse schema
which includes star schema, snowflake schema and also the fact constellation. Moreover it is
capable to identify multiple schemas from a XML schema if they are related. This is an
interesting and emerging area of research as a number of business organizations use data
warehouse for their business analysis and use XML to handle semi-structured data and also to
take the advantage of using web environment. Converting different types of semi-structured data
to XML is another important research interest. This work could be extended further to construct
data warehouse from different semi-structured data by adding an intermediate step to transform
semi-structured data to XML. Thus the organizations looking to incorporate business intelligence
can use this type of framework to build data warehouse architecture from heterogeneous data
sources.
REFERENCES
[1] Sarbani Dasgupta, Soumya Sen, Nabendu Chaki; "A Framework To Convert XML Schema to
ROLAP"; Proc. of the Second International Conference on Emerging Applications of Information
Technology (EAIT 2011), Kolkata, India, 2011 ISBN : 978-1-4244-9683-9
[2] From XML schema to cube Parimala N and Payel pahwa International Journal of Computer Theory
and Engineering Vol 1 No 3 August 2009.
Shipping_Fact
User_measures
Location_id
Shipper_id
Shipping_idSales_id
Sales
Unit
Branch
Type
Branch_id
Shipper
Name
Shipper_id
Location
City
Street
Location_i
d
Shipping
Unit
Location_
to
Shipping_id
Sales_Fact
User_measure
s
Location_id
Branch_id
Item_id
Sales_id
Title
* User_measures jn fact tables are supplied by users as their
measuring unit
Item
Item_id
Name

[3] Conceptual design of data warehouses from xml schemas Payel pahwa and Parimala N 2nd
International Conference on Intellectual Capital, knowledge management & Organizational Learning
21-22 Nov,2005 American University of Dubai, United Arab Emirates
[4] M. Jensen, T. Møller, and T.B. Pedersen, .Specifying OLAP Cubes On XML Data., Journal of
Intelligent Information Systems, 2001.
[5] Frank S.C. Tseng, Chia Wei Chen: Integrating heterogeneous data warehouses using XML
technologies, Journal of Information Science Volume-31, Issue:3 (June 2005) Page-209-229
[6] Boris Vrdoljak, Marko Banek, and Stefano Rizzi: Designing Web Warehouses from XMLSchemasY.
Kambayashi, M. Mohania, W. Wöß (Eds.): DaWaK 2003, LNCS 2737, pp. 89-98, 2003. Springer-
Verlag Berlin Heidelberg 2003
[7] Wolfgang Hummer, Andreas Bauer, Gunnar Harde: XCube – XML For Data Warehouses,
DOLAP’03, November 7, 2003, USA.
[8] M. Golfarelli, S. Rizzi, and B. Vrdoljak, .Data warehouse design from XML sources., Proc.
DOLAP’01, Atlanta, pp. 40-47, 2001.
[9] Data Mining Concepts and Technique,2nd Edition, Jiawei Han and Micheline Kamber, Morgan
Kaufmann Publisher
[10] Tim Bray, Jean Paoli, C. M. Sperberg-McQueen, Eve Maler, François Yergeau; “Extensible Markup
Language (XML) 1.0 (Fifth Edition)”; W3C Recommendation 26 November 2008
www.w3.org/TR/REC-xml
[11] Yuan Sun; Hexin Chen; Mianshu Chen; Xinying Wang; Aijun Sang; “Multi-dimension Multimedia
Retrieval Model Implementation Based on XML Database” International Conference on Signal
Processing Syatems, 2009
[12] Rajugan, R.; Chang, E.; Dillon, T.S.; “Conceptual Design of an XML FACT Repository for
Dispersed XML Document Warehouses and XML Marts”, 5th International Conference on Computer
and Information Technology, 2005
[13] Ramanath, M.; Kumar, K.S.; “A rank-rewrite framework for summarizing XML documents” 24th
International Conference on Data Engineering Workshop, ICDEW 2008
[14] Belen Vela; Carlos Blanco; Eduardo Fernandez; E.Marcos “Model Driven Development of Secure
XML Data Warehouses: A Case Study”. EDBT 2010, Lausanne, Switzerland.
Authors
Soumya Sen is a faculty member in A. K. Choudhury School of Information
Technology under University of Calcutta since March, 2009. He obtained his M. Tech.
Degree in Computer science & Engineering from the University of Calcutta in 2007.
His area of research is Data Warehouse and OLAP Tool. He is currently pursuing his
PhD work as a part-time scholar.
Ranak Ghosh completed his M.Sc. degree in Computer Science in 2011 from
Barrackpore Rastraguru Surendranath College under West Bengal State University. This
work is part of his Masters dissertation work under the guidance of Soumya Sen

Debanjali Paul completed her M.Sc. degree in Computer Science in 2011 from
Barrackpore Rastraguru Surendranath College under West Bengal State University. This
work is part of her Masters dissertation work under the guidance of Soumya Sen
Nabendu Chaki is an Associate Professor in the Department Computer Science &
Engineering, University of Calcutta, India. Besides editing several volumes in Springer
proceedings, Nabendu has authored 2 text books and close to 100 refereed research
papers in Journals and International conferences. His areas of research interests include
distributed systems and software engineering.

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