Databases Reference
In-Depth Information
Chapter 2
Distributed Approach for Pattern
Recognition
Implementing pattern recognition in a distributed manner may be a solution
for the Internet-scale data generation and application problems. Distributed
pattern recognition (DPR), the formal term for this type of recognition ap-
proach, can be defined as the extension of existing pattern recognition schemes
to include the delegation of the recognition process across a distributed system.
Most of the past initiatives in DPR have focused on providing a distributed
architecture for pattern recognition [18, 19, 20, 21, 22]. However, this type
of solution creates a high dependency on the hardware implementation. Be-
cause the implementation of these approaches across different architectural
platforms and network environments is limited by their inflexibility, the issue
of scalability in this context has yet to be solved.
A DPR scheme that is based solely on an algorithmic approach, independent
of any hardware implementation, has yet to be fully realized. Though there
are some recent studies on the implementation of a distributed approach for
existing pattern recognition schemes [2, 23, 24, 25], these studies manipulated
the methods of a particular algorithm to perform the recognition function
(from sequential to parallel mechanisms). Furthermore, existing distributed
approaches have been unable to reduce the computational complexity of their
respective algorithms, a necessity for deployment in a distributed environ-
ment. In addition, these studies have not considered the communication costs
incurred by the highly iterative features of the existing pattern recognition
schemes.
The deployment of pattern recognition applications for large-scale data sets
is an open issue that needs to be addressed. Several approaches have been pro-
posed, including data reduction, active learning and distributed approaches
in large-scale recognition. Nevertheless, a common denominator of these tech-
niques is the algorithmic complexity of the recognition schemes. Because the
distributed approach for pattern recognition can provide extensive support for
resource availability in response to the increasing size, complexity and amount
of data, it offers a significant advantage for large-scale data analysis. The ul-
timate goal for any DPR approach is to be able to extract useful information
from a large-scale analysis of a huge collection of data.
Because pattern recognition is considered to be highly problem specific and
has little prospect as a generic commodity application, DPR remains a rela-
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