Title: A Security Based Data Mining Approach in Data Grid
ArXiv ID: 1003.4066
Date: 2010-03-23
Authors: Researchers from original ArXiv paper
📝 Abstract
Grid computing is the next logical step to distributed computing. Main objective of grid computing is an innovative approach to share resources such as CPU usage; memory sharing and software sharing. Data Grids provide transparent access to semantically related data resources in a heterogeneous system. The system incorporates both data mining and grid computing techniques where Grid application reduces the time for sending results to several clients at the same time and Data mining application on computational grids gives fast and sophisticated results to users. In this work, grid based data mining technique is used to do automatic allocation based on probabilistic mining frequent sequence algorithm. It finds frequent sequences for many users at a time with accurate result. It also includes the trust management architecture for trust enhanced security.
💡 Deep Analysis
Deep Dive into A Security Based Data Mining Approach in Data Grid.
Grid computing is the next logical step to distributed computing. Main objective of grid computing is an innovative approach to share resources such as CPU usage; memory sharing and software sharing. Data Grids provide transparent access to semantically related data resources in a heterogeneous system. The system incorporates both data mining and grid computing techniques where Grid application reduces the time for sending results to several clients at the same time and Data mining application on computational grids gives fast and sophisticated results to users. In this work, grid based data mining technique is used to do automatic allocation based on probabilistic mining frequent sequence algorithm. It finds frequent sequences for many users at a time with accurate result. It also includes the trust management architecture for trust enhanced security.
📄 Full Content
JOURNAL OF COMPUTING, VOLUME 2, ISSUE 3, MARCH 2010, ISSN 2151-9617
HTTPS://SITES.GOOGLE.COM/SITE/JOURNALOFCOMPUTING/
45
A SECURITY BASED DATA MINING
APPROACH IN DATA GRID
S.Vidhya, S.Karthikeyan
Abstract - Grid computing is the next logical step to distributed computing. Main objective of grid computing is an
innovative approach to share resources such as CPU usage; memory sharing and software sharing. Data Grids provide
transparent access to semantically related data resources in a heterogeneous system. The system incorporates both data
mining and grid computing techniques where Grid application reduces the time for sending results to several clients at the
same time and Data mining application on computational grids gives fast and sophisticated results to users. In this work,
grid based data mining technique is used to do automatic allocation based on probabilistic mining frequent sequence
algorithm. It finds frequent sequences for many users at a time with accurate result. It also includes the trust management
architecture for trust enhanced security. Keywords: trust enhanced security, Data Grids, computational grids.
INTRODUCTION
1.1. Grid Computing
A parallel processing architecture in
which CPU resources are shared across a
network, and all machines function as one
large supercomputer, it allows unused CPU
capacity in all participating machines to be
allocated to one application that is extremely
computation intensive and programmed for
parallel processing. Grid computing is also
called
ʺpeer
to
peer
computingʺ
and
ʺdistributed computing.ʺ
‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐
S.Karthikeyan is with Department of Computer
Science, SNS College of Technology, Coimbatore, India
S.Vidhya is with Department of Computer Science,
SNS College of Technology, Coimbatore, India
The grid computing gives us yet
another way of sharing the computer resource
and yields us the maximum benefit at the time
and speed efficiency. Grid computing enables
multiple applications to share computing
infrastructure, resulting in much greater
flexibility, cost, power efficiency, performance,
scalability and availability at the same time.
1.2. Data Grid
A data grid is a grid computing system that
deals with the data controlled sharing and
management of large amount of distributed
data. A Data Grid can include and provide
transparent access to semantically related data
resources that are different managed by
different software systems and are accessible
through different protocols and interfaces.
JOURNAL OF COMPUTING, VOLUME 2, ISSUE 3, MARCH 2010, ISSN 2151-9617
HTTPS://SITES.GOOGLE.COM/SITE/JOURNALOFCOMPUTING/
46
1.3. Distributed Data Mining
Distributed data mining deals with
the problem of data analysis in environments
of distributed computing nodes, and users
peer to peer computing is emerging as a new
distributed
computing
for
many
novel
applications
that
involve
exchange
of
information among a large n umber of peers
with little centralized coordination.
1.4. Data Mining Rule
SPRINT algorithm for searching the
data. Finally it finds result and sends to the
server.
In this work, a unified view is
provided in which it allows user to use a
single query to retrieve all the information
transparently from different data sources. The
technologies used in this work includes
standard for data access over grid, high level
data access and semantic data integration
Where the high level data access is provided
by OGSA‐DQP system and semantic data
integration by XMAP framework.
Integrating OGSADQP system and
XMAP
framework
has
developed
the
prototype shown in this work.
DESIGN GOAL
The proposed grid based data mining
technique is used to do automatic allocation
based on the algorithm Probabilistic mining
frequent sequences. This algorithm is used to
find frequent sequences in complex databases.
It finds frequent sequences for many users at a
time with accurate result. Grid application for
this application reduces the time for sending
results to several clients at the same time. Data
mining application on computational grids
gives fast and sophisticated results to users.
In
this
system,
data
integration
architecture needs to combine both the query
reformulation
and
the
query
processing
services.
This
system
offers
a
wrapper/mediator‐based approach to integrate
data
sources,
and
adopts
the
XMAP
decentralized mediator approach to handle
semantic heterogeneity over data