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1. The Composite Data Model: A Unified Approach for Combining and
Querying Multiple Data Models
Abstract:
In this paper, we combine the characteristics of three fundamental data
models in order to represent their semantics in a common framework.
These fundamental data models include the familiar concepts of modeling
(1) object classes (or entities), their properties (attributes) and relationships
between them, (2) multidimensional objects and attributes that can be
summarized over the dimensions, and (3) hierarchical structures. This
model, called the Composite Data Model, facilitates combinations of these
three model structures to be represented jointly in a single schema, thus
providing more expressive and natural queries over them. The main
advantage of the composite data model (CDM), and a composite query
language (CQL) over it, is that any combination of the three fundamental
models can be represented jointly based on explicit semantics of each of the
fundamental data models. This is unlike existing data models that
represent each data model individually or obscure the semantics of
additional features being modeled. In order to develop a query language
over the combined schemas, we introduce a new concept, referred to as
anchor, which is an object class that acts as the focus of the query. We
provide in the query language path structures relative to the anchor that

2. facilitate data navigation and data manipulation. We develop the syntax
and semantics of the proposed language, and illustrate its expressive
power through numerous query examples, and comparisons to three other
query languages: OQL, SPARQL, and XQuery.
Existing System:
The relational data model also supports these concepts in term of relations
representing object classes and their attributes. However, relationships are
not explicit, but are expressed through the query language by “join”
operators. The OLAP data model was introduced in order to manage
multidimensional summary data.
The OLAP data model, which is also referred to as the statistical data
model consists of three basic constructs: (i) Dimensions, where each
dimension can consist of a multi-level hierarchical structure; (ii) A
multidimensional object, also referred to as a “cross-product” object; (iii)
Summary attributes, where each is associated with the multidimensional
object.
Proposed System:
We are interested in any type of object hierarchies as a basic structure
supported by our model. As will be shown throughout the paper, these
hierarchy structures can exist independently, associated with attributes of
regular object classes, or with dimensions of multidimensional object
classes. We refer in this paper to any of these types of hierarchies as “object
hierarchies”.

3. Associating hierarchy structures with attributes makes it possible for
attributes to be organized into semantically meaningful hierarchies, such as
a product-type hierarchy for items sold in a store, or disease hierarchies
used in patient diagnosis. Associating hierarchy structures with
dimensions makes it possible to apply summary operations on summary
attributes over the hierarchy levels, such as summarizing population from
a city to the state level.
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• RAM : 256 Mb.
Software Requirements:
• Operating system : - Windows XP.
• Front End : - JSP
• Back End : - SQL Server
Software Requirements:
• Operating system : - Windows XP.

4. • Front End : - .Net
• Back End : - SQL Server