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Unit 7: Applications Cases of Integration

7.3 summary notes

l z There are many types of EAI software on the market (such as Sun Microsystems SeeBeyond),
each approaching the problem of integration from a different angle and presenting a
different solution. However, there are four overarching purposes for which EAI software
can be used to improve efficiency.
l z The integration problems many enterprises face today are due to the fact that until relatively
recently there was no expectation that applications should be able to ‘talk’ to each other.

l z Until the advent of networks, computer applications were designed to perform a specific
purpose, and were often written in a range of different programming languages and used
different data structures than each other, with no thought to integration.
7.4 keywords

Auditing: Auditing is an evaluation of a person, organization, system, process, enterprise, project
or product. Audits are performed to ascertain the validity and reliability of information; also to
provide an assessment of a system’s internal control.
Database: Database is a set of computer programs that controls the creation, maintenance, and
the use of the database in a computer platform or of an organization and its end users.

EAI: Enterprise Application Integration is the term used to describe the integration of the
computer applications of an enterprise so as to maximise their utility throughout the enterprise.
Operating System: An operating system (OS) is an interface between hardware and user which is
responsible for the management and coordination of activities and the sharing of the resources of
the computer that acts as a host for computing applications run on the machine.

7.5 review Questions

1. What do you mean by enterprise application integration?
2. What are the purposes of EAI uses?
3. Describe the industry specific implementations of EAI.
4. What are the advantages and disadvantages of Integration?

7.6 further readings

Books A. K. Jain and R. C. Dubes, Algorithms for Clustering Data, Prentice Hall, 1988.

Alex Berson, Data Warehousing Data Mining and OLAP, Tata Mcgraw Hill, 1997
Alex Berson, Stephen J. Smith, Data warehousing, Data Mining & OLAP, Tata
McGraw Hill, Publications, 2004.
Alex Freitas and Simon Lavington, Mining Very Large Databases with Parallel
Processing, Kluwer Academic Publishers, 1998.

J. Ross Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers,
1993.
Jiawei Han, Micheline Kamber, Data Mining – Concepts and Techniques, Morgan
Kaufmann Publishers, First Edition, 2003.

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