Uchang Park
Duksung Women’s University, Dobonggu Ssangmoondong 419, Seoul, Korea
Ramon Lawrence
University of British Columbia Okanagan, 3333 University Way, Kelowna, B.C., Canada
Keywords: Database, integration, view, heterogeneity.
Abstract: Database integration is a common and growing challenge with the proliferation of database systems, data
warehouses, data marts, and other OLAP systems in organizations. Although there are many methods of
sharing data between databases, true interoperability of database systems requires capturing, comparing, and
merging the semantics of each system. In this work, we present a database integration system that improves
on the database federation architecture by allowing domain administrators to simply and efficiently capture
database semantics. The semantic information is combined using a tool for producing a global view.
Building the global view is the bottleneck in integration because there are few tools that support its
construction, and these tools often require sophisticated knowledge and experience to operate properly. The
technique and tool presented is simple and powerful enough to be used by all database administrators, yet
expressive enough to support the majority of integration queries.
Research on how to integrate heterogeneous and
autonomous database systems has been performed
since the 1980’s (Data Integration Projects, 2005).
However, the early approaches, including federated
databases (Sheth and Larson, 1990) and distributed
databases did not solve the entire problem. In most
cases, commercial databases like Oracle, SQL
Server, and DB2 support proprietary integration of
databases from the same vendor (Chawathe et al.,
1994), (Collet et al., 1991), (IBM, 2005), (Josifovski
et al., 2002), (MICROSOFT, 2005).
Mediator and wrapper architectures preserve
database autonomy and support distributed queries.
However, to be effective, a global view of the data
sources must be created. Global view construction
involves schema matching and merging techniques
(Rahm and Bernstein, 2004). Although many
prototype systems exist, the tools are not production-
ready or easy to use by database administrators and
designers. Further, there is no standard
infrastructure, protocols, and implementation of the
mediator and wrapper components. In this paper, we
describe a system for helping database
administrators to integrate databases and aids in the
construction of a global view. Using the global view,
we exploit the UnityJDBC integration system
(Mason and Lawrence, 2005), (UNITYJDBC, 2006)
for execution of the queries on the data sources. The
UnityJDBC driver can integrate any number of
JDBC-accessible data sources including SQL
Server, MySQL, and Oracle. Our work uses
techniques similar to those used in schema matching
systems such as Clio (Haas et al., 2005) and
COMA++ (Aumueller et al., 2005) and is related to
the area of schema merging (Dragut and Lawrence,
The contributions of this work are:
A global view construction technique that requires
only knowledge of SQL.
A query interface that allows users to write
queries on the global view without understanding
the underlying databases.
A query execution system that automatically
combines data from various sources according to
the specified global view.
Park U. and Lawrence R. (2007).
In Proceedings of the Ninth International Conference on Enterprise Information Systems - DISI, pages 453-456
DOI: 10.5220/0002351404530456
We overview the system architecture in Section
2. In Section 3, we discuss the basic constructs for
building a global view. Section 4 contains related
work, and the paper closes with future work and
The overall system architecture is in Figure 1.
The architecture provides a complete system for
global view construction, data source querying and
result integration.
Before the global view is constructed, the system
collects each local database schema information and
converts it to an XML file (Steps 1 and 2). The
global schema editor is used to build the global
view. Once constructed, the global view and
mapping information is stored in a XML schema
mapping document (Step 3). The mapping between
local and global schemas is at the entity-level. That
is, each table and attribute in a local view is mapped
to a table or attribute in the global view. During
query processing, the user specifies queries on the
global view using a GUI. Global queries are
converted into federation (source) queries. These
source queries are executed by the federation engine
(UnityJDBC) and then combined into a single result
using the global view (Steps 4 and 5).
3.1 Global View Construction
Our system connects to each local database and
extracts the schema information into XML files. As
an example, Figure 2 reports two simple Employee
database schemas.
Our global view editor is shown in Figure 3. A
global view is constructed by mapping the tables
from the local databases to the global view. Once a
set of global view relations are constructed using
this bottom-up approach, the administrator uses the
editor to construct local mappings. This mapping
task is performed similar to schema matching
approaches (Halevy, 2001), (Rahm and Bernstein,
2004) except that it allows more complex matching,
including matching of relations as well as attributes.
Figure 3: Global View Editor.
There are three types of attribute matchings:
Direct match – When attribute B of the local
schema matches exactly to attribute A of the
global view.
No match – Attribute B of a local schema has
no matching in the global view.
Functional match – Attribute A of the global
view is matched by a functional expression
(such as string concatenation) of one or more
attributes in a single local schema.
For each relation in the local and global views, the
primary keys of the relations are used to determine
how to match relations. There are three ways to
match relations between the local and global view:
Union – The tuples of relation R in a local view
are combined into relation S in the global view
using UNION. There can be multiple relations
Figure 1: System Architecture.
Figure 2: Site1 and Site2 Database Schema.
ICEIS 2007 - International Conference on Enterprise Information Systems
that are unioned together to produce a global
view relation instance.
Single – Single is a special case of union where
a global relation maps to only one local relation.
Join – The tuples of a global relation S are
produced by a join condition connecting
relations R and U which may be in different
As an example, Table 1 shows the mappings
between a constructed global view and the schemas
of the site 1 and site 2.
Table 1: Global to Local Mapping.
Site 1 Site 2 Mapping
emp db1.myemp db2.youremp UNION
empid empid empid PK, Direct
name name name Direct
salary salary salary Direct
deptno deptno deptno Direct
dept db2.dept SINGLE
deptno deptno PK, Direct
deptname deptname Direct
city city Direct
empspecial db1.specialemp db2.dept JOIN
empno empid PK, Direct
deptno deptno deptno Direct
age age Direct
deptcity city Direct
By constructing a global view, users are relieved
of the burden of building distributed queries
themselves which is typical in a database federation.
The global schema is in Figure 4.
Figure 4: Global Schema for Employee Database.
3.2 Global View Querying
Queries on the integrated schema are built using a
GUI interface (see Figure 5). Users can view the
global schema while keying in queries over the
screen. SQL queries are processed by a query
processor that uses the XML mapping file and the
execution engine. Data manipulation operations are
not allowed on the GUI interface.
Depending on the relationship between a global
schema table and a local schema table, the query
handling rules are grouped into the following
Type 1: Query for single source table that
comes from single local site.
Type 2: Query for union table that comes from
union of 2 or more local sites tables.
Type 3: Query for join table that comes from
join of 2 local sites tables.
Type 4: Join query when joining 2 tables in the
global view.
The rewriting rules for each type are as follows:
Type 1: Map global relation and field names to
local relation and field names.
Type 2: Create a local source query for each
source containing a table to be combined using
union. Union each source query result. Union is
applied only on the key attributes if the source
tables have different numbers and types of
Type 3: Create a federated query that joins
relations in the two databases on key attributes
specified in the mapping.
Type 4: A join in the global view may map to a
UNION (Type 2) and a federated JOIN (Type 3)
depending on the mapping for the table(s) in the
global view. Multiple global joins may result in
many UNIONs and federated joins in the
federated query.
Figure 5: GUI Query Interface.
Once a query has been re-written from a global view
query to a federated query, it is given to the
federated query engine for execution.
The following are examples for each type based on
our running example.
Type 1: Single Table
Global Query:
SELECT deptname
FROM dept
Federated Query:
SELECT db2.dept.deptname
FROM db2.dept
Type 2: Union
Global Query:
FROM emp
Federated Query:
SELECT db1.myemp.name
FROM db1.myemp
SELECT db2.youremp.name
FROM db2.youremp
Type 3: Federated Join
Global Query:
SELECT empspecial.empno,
FROM empspecial
Federated Query:
SELECT db1.specialemp.empid,
FROM db1.specialemp, db2.dept
WHERE db1.specialemp.deptno =
Type 4: Global Join
Global Query:
SELECT emp.name, dept.deptname
FROM emp, dept
WHERE emp.deptno = dept.deptno
Federated Query:
SELECT db1.myemp.name,
FROM db1.myemp, db2.dept
WHERE db1.myemp.deptno =
SELECT db2.youremp.name,
FROM db2.youremp, db2.dept
WHERE db2.youremp.deptno =
Database integration offers benefits in three main
areas: simplified system administration and
maintenance, rapid development of integrated
applications, and the ability for end-users to access
all information in a domain. In this paper, we
presented a database integration system that layers a
global view on top of the federation architecture.
This global view is simple to construct and maintain
and allows federated queries to be automatically
built by querying the global view. Thus, the
approach captures the benefits of database
federation, while avoiding its major shortcoming,
the challenge of building federated queries to
integrate data. Further, the approach, unlike
commercial implementations, is not bound to a
particular database management system. Overall,
this makes the benefits of database integration easier
and more cost-effective to realize in all
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