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Service-Oriented Architecture: Risks and Remedies
-- Deepa V Jose and Smitha Vinod
Service-Oriented Architecture (SOA) is not just an architecture of services seen
from a technological perspective, but the policies, practices and frameworks by
which we ensure that the right services are provided and consumed. This paper is
the outcome of the efforts to study SOA, its efficiency and drawbacks. Some methods
to tackle the problems faced by SOA are also briefed.
© 2011 IUP. All Rights Reserved.
Improvements to First-Come-First-Served
Multiprocessor Scheduling with Gang Scheduling
-- R Siyambalapitiya and M Sandirigama
This paper proposes an improved algorithm for the multiprocessor job scheduling
problem based on First-Come-First-Served (FCFS) strategy. Depending on the job processing
times, some jobs are divided into multithreads while others remain as single thread
jobs. Multithread jobs are processed based on the concept of gang scheduling. Backfilling
technique is used to improve the performance of the proposed algorithm. The results
of the proposed algorithm are presented using a percentage gap from a lower bound.
© 2011 IUP. All Rights Reserved.
Multi-Class Manifold Preserving Isomap
Using Sammon’s Projection
-- Shashwati Mishra and Chittaranjan Pradhan
Isometric feature Mapping (Isomap) gives promising results in preserving the original
manifold structure in the case of a highly twisted and curved manifold. Classical
Multi-Dimensional Scaling (MDS) uses Euclidean distance concept for obtaining low-dimensional
embedding. As a distance preserving dimensionality reduction technique, Isomap gives
emphasis on geodetic distances. Due to lack of any linear relationship between reduced
embedded information and original high-dimensional information, it is considered
a nonlinear dimensionality reduction technique. This paper concentrates on multi-class
manifold geometry preservation. Like MDS, Sammon’s mapping tries to preserve the
manifold geometry by minimizing the Sammon’s stress. Sammon’s projection gives better
result in preserving small distances. Sammon’s algorithm was applied instead of
MDS for embedding information in a lower dimension in the final step of Isomap and
a more clear output was obtained.
© 2011 IUP. All Rights Reserved.
An Optimizing Compiler for
Turing Machine Description Language
-- Pinaki Chakraborty, Shweta Taneja, Nandita Anand,
Anupama Jha, Diksha Malik and Ankit Nayar
Turing machines are an important concept in theoretical computer science. Several
simple languages have been developed till date for modeling and simulation of Turing
machines, often for pedagogical purposes. The Turing Machine Description Language
(TMDL) is one such language and it is best known for its textbook style descriptive
representation of Turing machines. This paper reports the development of a two-pass
optimizing compiler for the language. In an experiment, it was observed that the
optimizing compiler produces object programs that are up to 1.784 times shorter
and 1.032 times faster than those produced by an existing compiler that does not
employ code optimization.
© 2011 IUP. All Rights Reserved.
A Multivalued Dependency-Based Normalization
Approach for Symbolic Relational Databases
-- S Deepa
In today’s world, a huge quantity of data is being generated which requires effective
management in terms of storage, manipulation and retrieval. Real world data is often
ambiguous and uncertain in nature since it is closer to human intuitions. In a step
towards designing database systems to manage real world data, there arises a need
to develop an intelligent database model. A symbolic relational database aims at
handling such real world data. It is an extension of the classical relational data
model and is designed as a subspace of the fuzzy data base model. Design theory
of any database consists of finding out the data dependencies and normal forms that
enables us to represent data in a consistent and non-redundant fashion. This paper
aims at developing higher-level normal forms for the design of symbolic relational
databases. The level of normalization worked out in the paper is based on multivalued
dependency.
© 2011 IUP. All Rights Reserved.
Identifying Relevant Snippets from
Ranked Web Documents
-- Shanmugasundaram Hariharan
Thirunavukarasu Ramkumar and Selva Muthukumaran
Internet has brought a major shift or revolution in day-to-day life. It is quite
common that millions of documents are posted on the web on a daily basis. The major
concern is to identify the right information from the enormous data available. The
documents retrieved from commercial search engines are not relevant to the user
query. The major issue is to reproduce the right knowledge and deliver it to the
web surfer. This has provided the platform for reranking, optimization and several
other applications like text summarization, question answering and snippetization.
This paper focuses on identifying text snippets for the retrieved web results using
statistical approaches. Experiments based on Google search engine presented promising
results.
© 2011 IUP. All Rights Reserved.
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