KNOWLEDGE DISCOVERY IN SERVICES AGGREGATING
SOFTWARE SERVICES TO DISCOVER ENTERPRISE
MASHUPS
ABSTRACT
Service
mashup is the act of integrating the resulting data of two complementary
software services into a common picture. Such an approach is promising with
respect to the discovery of new types of knowledge. However, before service
mashup routines can be executed, it is necessary to predict which services (of
an open repository) are viable candidates.
Similar
to Knowledge Discovery in Databases (KDD), to introduce the Knowledge Discovery
in Services (KDS) process that identifies mashup candidates. In this, the KDS
process is specialized to address a repository of open services that do not
contain semantic annotations. In these situations, specialized techniques are
required to determine equivalences among open services with reasonable precision.
This
project introduces a bottom-up process for KDS that adapts to the environment
of services for which it operates. Detailed experiments are discussed that
evaluate KDS techniques on an open repository of services from the Internet and
on a repository of services created in a controlled environment.
The
resulting mashup could visualize the path of parcels that get lost in the
delivery process. This integration of web services outputs is the general idea behind
service mashup. While composition involves linking services into a higher level
workflow or business-oriented process, service mashup is closer related to
running services in parallel and integrating the resulting data.
SYSTEM
ANALYSIS
2.1 EXISTING SYSTEM
Service mashup is the
act of integrating the resulting data of two complementary software services into
a common picture. Such an approach is promising with respect to the discovery
of new types of knowledge. Before service mashup routines can be executed, it
is necessary to predict which services (of an open repository) are viable
candidates. Similar to Knowledge Discovery in Databases (KDD) .The four phases
of KDD , cleaning and integration, selection and transformation, data mining,
and evaluation and presentation.
Existing system is time consuming why because person who
are interested in taking service they need to present at courier branch then
there will be lot of formalities like filling forums submitting forms, waiting
for time so this entire process take some time. In present existing system data
is entered through manual process and there are chances of data loss, security
is very less and any person can access important data.
DISADVANTAGE
Ø Store
the duplicate information
Ø Wastage
of time
Ø No
clustering information
Ø No
categorization information
2.2
PROPOSED SYSTEM
The Knowledge Discovery in Services (KDS) process
that identifies mashup candidates. In this, the KDS process is specialized to
address a repository of open services that do not contain semantic annotations.
In these situations, specialized techniques are required to determine
equivalences among open services with reasonable precision. This project
introduces a bottom-up process for KDS that adapts to the environment of
services for which it operates.
Detailed experiments are discussed that evaluate KDS
techniques on an open repository of services from the Internet and on a
repository of services created in a controlled environment. The KDS phases of
discovery, equivalence processing, clustering, categorization, filtering, and
presentation.
In
order to overcome existing system problems new system is developed using this
system any system can be easily searched with better security features.
Database is implemented for maintaining files. So this can help in increasing
secure data storage. Database details can be easily retrieved by just knowing
ID’s. Amount calculation can be performed with in less time. More features are
provided in the project document
A service mashup is the simultaneous execution of
two or more services to create an integrated data provision with a more
complete description about some object or task. For example, web services from
a web search company such as Google Corporation that provides mapping
capability can be integrated with capabilities from a shipping business such as
the United Parcel Service (UPS).
ADVANTAGE
Ø Avoid
the duplicate information
Ø Time save
Ø Clustering
information
Ø Categorization
information
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