Data Warehouse Designing: Dimensional Modelling and E-R Modelling

International Journal of Engineering Inventions
e-ISSN: 2278-7461, p-ISSN: 2319-6491
Volume 3, Issue 9 (April 2014) PP: 28-34
www.ijeijournal.com Page | 28
Data Warehouse Designing: Dimensional Modelling and E-R
Modelling
Geetika Saxena1
, Bharat Bhushan Agarwal2
COMPUTER SCIENCE DEPARTMENT, IFTM UNIVERSITY, MORADABAD
Abstract: The Data Warehouse (DW) is considered as a collection of integrated, detailed, historical data,
collected from different sources . DW is used to collect data designed to support management decision making.
There are so many approaches in designing a data warehouse both in conceptual and logical design phases.
The conceptual design approaches are dimensional fact model, multidimensional E/R model, starER model and
object-oriented multidimensional model. And the logical design approaches are flat schema, star schema, fact
constellation schema, galaxy schema and snowflake schema. In this paper we have focused on comparison of
Dimensional Modelling AND E-R modelling in the Data Warehouse. Dimensional Modelling (DM) is most
popular technique in data warehousing. In DM a model of tables and relations is used to optimize decision
support query performance in relational databases. And conventional E-R models are used to remove
redundancy in the data model, facilitate retrieval of individual records having certain critical identifiers, and
optimize On-line Transaction Processing (OLTP) performance.
Keywords: Data Warehouse, DM Models, E-R Models, flat schema, star schema, fact constellation schema,
galaxy schema, snowflake schema.
I. Introduction
Information is an asset for the organization or enterprise (resource like capital, first matters, plants and
people) which is used to provide benefit and competitive advantage to any organization. Hence, understanding
the value of information is important. Today, every organization have a relational database management system
that is used for organization‟s daily operations. The organization wants to increase the value of their
organizational data by making it accessible information.
Organizations usually complaints,
“We have tons of data but we cannot access them!”
“Show me only what is important!”
As the amount of the organizational data increases, it becomes harder to access and get the most information out
of it, because it is in different formats, exists on different platforms and
resides on different structures. Data warehousing provides an excellent approach in converting operational data
into useful, accessible and reliable information to support the decision making process and also provides the
basis for data analysis techniques like data mining and multidimensional analysis. Data mining is the process of
identifying valid, novel, useful and understandable patterns in data. Data Mining is also known as KDD
(Knowledge Discovery in Databases).
II. Data Warehouse Concepts
2.1. DEFINITION OF DATA WAREHOURE
The data warehouse always contains data and information, on which management decisions can be
reliably tested, analyzed, assessed and monitored using the data and information integration.
“A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of
management‟s decision-making process .”—W. H. Inmon [1, 2, 3, 6, 10, 11].
 Subject Oriented: Data that gives information about a particular subject instead of about a company's
ongoing operations.
 Integrated: Data that is gathered into the data warehouse from a variety of sources and merged into a
coherent whole.
 Time-variant: All data in the data warehouse is identified with a particular time period.
 Non-volatile: Data are not updated or changed in any way once they enter the data warehouse, but are
only loaded and accessed.

Data Warehouse Designing: Dimensional Modelling And E-R Modelling
III. Designing A Data Warehouse
3.1 CONCEPTUAL DESIGN MODELS
The conceptual design allows having closer ideas about the ways that a user can perceive an
application domain. In fact, it is considered as a key step that ensures the successful of the DW projects since it
defines the expressivity of the multidimensional schemata, and the result of this step is a graphical notation
which facilitates to the designer and the user different tasks such as writing, understanding and managing the
conceptual schemata.
The main goal of conceptual design modelling is developing a formal, complete, abstract design based on the
user requirements [34].
At this phase of a DW there is the need to:
Represent facts and their properties
Represent objects and capture their properties with the associations among them:
Record the associations between objects and facts:
Distinguish dimensions and categorize them into hierarchies
3.1.1. THE DIMENSIONAL FACT MODEL
This model is built from ER schemas [9, 15, 16, 17, 33]. The Dimensional Fact (DF) Model is a
collection of tree structured fact schemas whose elements are facts, attributes, dimensions and hierarchies. A
fact schema is structured as a tree whose root is a fact. The fact is represented by a box. Sub-trees rooted in
dimensions are hierarchies. The circles represent the attributes and the arcs represent relationship between
attribute pairs.
Fig.1 A dimensional fact schema
3.1.2. MULTIDIMENSIONAL E/R MODEL
Multidimensional E/R (ME/R) model includes some key points [14]:
 Specialization of the ER Model
 Minimal extension of the ER Model; this model should be easy to learn and use for an experienced ER
Modeler.
This model allows the generalization concepts. There are some specializations:
 A special entity set: dimension level
 Two special relationship sets connecting dimension levels:
 a special n-ary relationship set: the „fact‟ relationship set
 a special binary relationship set: the „roll-up to‟ relationship set
The “roll-up to” relationship set; it relates a dimension level A to a dimension level B representing concepts of a
higher level of abstraction (city roll-up to country).
Fig.2. notation of Multidimensional E/R elements

3.1.3. STARER
This model combines star structure with constructs of ER model [13]. The starER contains facts,
entities, relationships and attributes. This model has the following constructs:
 Fact set
 Entity set
 Relationship set
Fig.3. StarER Model
3.2 LOGICAL DESIGN MODELS
Dimensionality modelling uses the ER Modelling with some important restrictions. Dimensional model
composed of one table with a composite primary key, called fact table, and a set of smaller tables called
dimension tables. Each dimension table has a simple (non-composite) primary key that corresponds exactly to
one of the components of the composite key in the fact table. This characteristic structure is called star schema
or star join.
There are following models used in dimensional modelling.
3.2.1 DIMENSIONAL MODEL DESIGN
Dimensional Model is used by OLTP systems. It contains no redundancy, but high efficiency of
updates, shows all data and relationships between them. Simple queries require multiple table joins and complex
subqueries. It is suitable for technical specialist.
3.2.1.1 FLAT SCHEMA
This schema is the very simple schema. This is formed by collapsing all entities in the data model
down into the minimal entities. This minimizes the number of tables in the database and joins in the queries. We
end up with one table for each minimal entity in the original data model [12]. This structure does not lose
information from the original data model. It contains redundancy, in the form of transitive and partial
dependencies, but does not involve any aggregation. It contains some problems; first it may lead to aggregation
errors when there are hierarchical relationships between transaction entities. When we collapse numerical
amounts from higher level transaction entities in to other they will be repeated. Second this schema contains
large number of attributes. Therefore while the number of tables (system complexity) is minimized, the
complexity of each table (element complexity) is increased.

Fig. 4. Flat Schema
3.2.1.2 STAR SCHEMA
It is the basic structure for a dimensional model. A fact table in the middle connected to a set of
dimension tables
It contains:
 A large central table (fact table)
 A set of smaller attendant tables (dimension table), one for each dimension
Fig. 5. Star Schema
3.2.1.3 SNOWFLAKE SCHEMA
A refinement of star schema where some dimensional hierarchy is further splitting (normalized) into a
set of smaller dimension tables, forming a shape similar to snowflake
 However, the snowflake structure can reduce the effectiveness of browsing, since more joins will be
needed.
 Snowflake schema is an extension of star schema in a way; it separates itself from Star when it comes to
handling large dimension tables. A star schema focuses on a centralized design with a fact table in it
connecting to different dimension tables end to end.

Fig. 6. Snowflake Schema
3.2.1.4 FACT CONSTELLATION SCHEMA
Multiple fact tables share dimension tables, viewed as a collection of stars, therefore called galaxy
schema or fact constellation.
Fig. 7. Fact constellations
3.2.1.5 GALAXY SCHEMA
Galaxy schema is a schema where multiple fact tables share dimension tables. Unlike a fact
constellation schema, the fact tables in a galaxy do not need to be directly related [12]. The following figure, Fig
5, illustrates a sample of a galaxy schema.

Fig.8. Galaxy Schema
IV. Comparison Of Dimensional Models And Er Model
ER modelling
The main goal of ER modelling is to remove redundancy from data. To remove redundancy, designers
must use hundreds of entities and relations between entities, which makes ER model complex. There is no easy
way to enable end users navigate through the data in ER models.
ER modelling aims to optimize performance for transaction processing. It is also hard to query ER models
because of the complexity; many tables should be joined to obtain a result set. Therefore ER models are not
suitable for high performance retrieval of data.
Dimensional model
The dimensional model is a standard framework. End user tools can make strong assumptions about the
dimensional model. It helps, to make user interfaces more user friendly and processing more efficient [20].
Dimensional model is more useful to random changes in user behaviour and requirements. The logical design
can be made independent of expected query patterns. All dimensions can be thought as symmetrically equal
entry points into the fact table. Dimensional model is extensible to new design decisions and data elements. All
existing fact and dimension tables can be changed in place without having to reload data. End user query and
reporting tools are not affected by the change.
Dimensional model involves business rules but ER modelling does not involve business rules, it involves
data rules.
V. Conclusion
In this paper we focused on comparison of Dimensional Modelling AND E-R modelling in the Data
Warehouse. We have discussed Conceptual Design Models and all types of Logical Design Models. The
conceptual design approaches are dimensional fact model, multidimensional E/R model, starER model and
object-oriented multidimensional model. And the logical design approaches are flat schema, star schema, fact
constellation schema, galaxy schema and snowflake schema. We got that Dimensionality modelling uses the ER
Modelling with some important restrictions like Dimensional model composed of one table with a composite
primary key, called fact table, and a set of smaller tables called dimension tables.
According to my research, there is no complete study in literature on DW models providing a
mapping of models to development phases and giving a comparison of the models according to these phases.
Also, there are very few articles for covering all phases of data warehousing.

VI. Future Work
One future work may be implementing a more complex case study using real world application data,
perform performance tests using the three logical models compared to support the comparison on logical design
models.
Another future work may be by adding more quality factor in the comparison we can improve comparison of
logical design models.
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