os research paper 2013

Topics: Relational model, Data modeling, Database Pages: 33 (6803 words) Published: February 25, 2014
Journal of Computing and Information Technology - CIT 10, 2002, 2, 69–84


A Procedure of Conversion
of Relational into Multidimensional
Database Schema
Mladen Varga
Faculty of Economics, University of Zagreb, Croatia

It is universally recognized that operational information
systems lean on the relational model and data warehouses
on the multidimensional model. The phrase On-Line
Analytical Processing (OLAP) means summarizing, consolidating, viewing, and synthesizing data according to multiple dimensions. The process of modeling data
warehouse may start from operational system’s database.
It may be helpful to convert a relational database schema
into a multidimensional database schema in order to discover dimensions that are hidden in a relational database. However, only a few efforts have been done investigating the conversion of relational into multidimensional database schema. This paper proposes the general procedure of such a conversion. The procedure can be partly automated because some decisions of attribute type must

be made during conversion.
Keywords: relational database, relational schema, multidimensional database, multidimensional schema, conversion.

1. Introduction
Operational information systems that lean on
the relational model may be a starting point for
the process of modeling data warehouse. Most
data warehouses rely on the multidimensional
model and it may be helpful to convert a relational database schema into a multidimensional database schema.
In this section, some general facts of relational
and dimensional model are mentioned. In Section 2 the procedure of conversion is presented and in Section 3 a small example of the conversion is shown.

1.1. Relational Model
A relational database consists of a set of relations. A relational schema which is used to describe a relation r, denoted by R(A1 A2 : : : An ) is made up of a relation named R and a list
of attributes A1 , A2 ,: : : , An . Each attribute Ai
is the name of a role played by some domain
D in the relation R. A relation r of the relational schema R(A1 A2 : : : An ) is a set of tuples r = ft 1 t 2 : : : t m g. A relational database is described by a relational database schema that
consists of a set of relational schemas.
If for any two distinct tuples t1 and t2 in the
relation r of R there exists attribute (set of at6
tributes) K such that t1 K ] = t2 K ], then such
attribute (set of attributes) is called key. It is
common to designate one of possible keys as
the primary key, which is used to identify tuples
in the relation.
A set of attributes FK in the relational schema R
is a foreign key of R1 if two rules are satisfied:
The attributes in FK have the same domain
as the primary key PK of another relational
schema attributes FK are said to reference
relation R2 ,
A value of FK in a tuple t1 of R1 either occurs as a value of PK for some tuple t2 in R2 or is null.
1.2. Dimensional Model
Let us suppose that a hospital database contains
admission data (such as number of days and


A Procedure of Conversion of Relational into Multidimensional Database Schema

value) of the patient, the date of admission, and
the diagnosis. The admission data is determined
by three attributes Patient, Diagnosis and Time.
They are referred to as dimensions while the
determined admission attributes are referred to
as measures or facts or fact attributes. The facts
are mostly numerical, preferably continuously
valued and additive. They vary over time. In the
statistical database field (Shosani, 1997) the dimension corresponds to category attribute, and the measure to summary attribute. The cube
model, shown in Fig. 1, is very useful in presenting the concept of the multidimensional space. There is no a priori distinction between dimensions and measures while any attribute can play either role (Gyssens, 1996). There is no formal

way to decide which attributes are dimensions
and which attributes...

References: ACM Workshop on Data Warehousing and OLAP,
(1998), Washington.
of the Hawaii Int. Conf. on System Science, (1998a),
Conference, (1996), Mumbai (Bombay).
4 ] R. KIMBALL, The Data Warehouse Toolkit. John
Wiley, (1996), New York.
10th Int. Conf. on Scient. and Statistical Database
Management, (1998), pp
Systems, (1997), pp. 185-196.
Received: November, 1999
Revised: March, 2002
Accepted: May, 2002
Contact address:
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