Contemporary object-oriented programming seeks to enable distributed computing by accessing remote objects using blocking remote procedure calls. this technique, however, suffers from several drawbacks because it reli...
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ISBN:
(纸本)9783540728818
Contemporary object-oriented programming seeks to enable distributed computing by accessing remote objects using blocking remote procedure calls. this technique, however, suffers from several drawbacks because it relies on the assumption of stable network connections and synchronous method invocations. In this paper we present an approach to support distributed programming, which rely on local object replicas keeping themselves synchronized using an underlying peer-to-peer infrastructure. We have termed our approach Peer-to-peer Distributed Shared Objects (PDSO). this PDSO approach has been implemented in the DOLCLAN framework. An evaluation demonstrates that DOLCLAN can be utilized to create a real distributed collaborative system for ad-hoc collaboration in hospitals, which demonstrates that the approach can support the creation of non-trivial distributed applications for pervasive computing.
We describe a test development method, named OTK1, that is aimed at optimizing compiler testing. the OTK method is based on constructing a model of optimizer's input data. the method allows developing tests target...
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ISBN:
(纸本)9783540730651
We describe a test development method, named OTK1, that is aimed at optimizing compiler testing. the OTK method is based on constructing a model of optimizer's input data. the method allows developing tests targeted to testing a chosen optimizer. A formal data model is constructed on the basis of an abstract informal description of an algorithm of the optimizer under test. In the paper, we consider in detail the process of analyzing an optimization algorithm and building a formal model. We also consider in outline the other part of the method, test selection and test running. the OTK method has been successfully applied in several case studies, including test development for several different optimizing compilers for modern architectures.
Consensus clustering has emerged as one of the principal clustering problems in the data mining community. In recent years the theoretical computer science community has generated a number of approximation algorithms ...
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ISBN:
(纸本)9780898716306
Consensus clustering has emerged as one of the principal clustering problems in the data mining community. In recent years the theoretical computer science community has generated a number of approximation algorithms for consensus clustering and similar problems. these algorithms run in polynomial time, with performance guaranteed to be at most a certain factor worse than optimal. We investigate the feasibility of the approximation algorithms, in an attempt to link data-mining and theoretical research. On realistic data sets, algorithms with quadratic running times are impractical. Unfortunately these and even worse running times are typical of approximation algorithms. To circumvent this, we sample from the data, run the "slow" algorithms on the sample, and then build a consensus clustering from the seed sample clustering, using a range of techniques. these unsampling techniques are in fact almost as good at creating consensus partitionings as the approximation and data-mining algorithms themselves. We find that one of the latest approximation algorithms is not only fast and effective, but also easy to describe, making it an ideal choice.
One limitation of the well-known family polymorphism approach is that each "family" will be a large monolithic program. In this paper, we introduce a minimal lightweight set of language features that treat e...
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We consider the problem of aligning multiple protein sequences withthe goal of maximizing the SP (Sum-of-Pairs) score, when the number of sequences is large. the QOMA (Quasi-Optimal Multiple Alignment) algorithm addr...
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this paper deals withthe realization of retrieval and feature description systems for clusters by using logical rule generations based on the Genetic programming (GP). At first, whole data is divided into several clu...
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ISBN:
(纸本)9783540725893
this paper deals withthe realization of retrieval and feature description systems for clusters by using logical rule generations based on the Genetic programming (GP). At first, whole data is divided into several clusters and the rules are improved based the GP. the fitness of individuals is defined in proportion to the hits of corresponding logical expression to the samples in targeted cluster c, but also in inversely proportion to the hits outside the cluster c. the GP method is applied to various real world data by showing effective performance compared to conventional methods.
the proceedings contain 56 papers. the topics discussed include: an opportunistic algorithm for scheduling workflows on grids;a service oriented system for on demand dynamic structural analysis over computational grid...
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ISBN:
(纸本)9783540713500
the proceedings contain 56 papers. the topics discussed include: an opportunistic algorithm for scheduling workflows on grids;a service oriented system for on demand dynamic structural analysis over computational grids;scalable desktop grid system;analyzing overheads and scalability characteristics of OpenMP applications;parallel fuzzy c-means cluster analysis;peer-to-peer models for resource discovery in large-scale grids: a scalable architecture;JaceV: a programming and execution environment for asynchronous iterative computations on volatile nodes;aspect oriented pluggable support for parallel computing;model for simulation of heterogeneous high-performance computing environments;on evaluating decentralized parallel I/O scheduling strategies for parallel file systems;and distributed security constrained optimal power flow integrated to a DSM based energy management system for real time power systems security protocol.
this paper aims to provide a scheme for effectively and efficiently finding an approximately optimal example size with respect to a given dataset when using Multiple-Criteria Linear programming (MCLP) classification m...
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ISBN:
(纸本)9783540725855
this paper aims to provide a scheme for effectively and efficiently finding an approximately optimal example size with respect to a given dataset when using Multiple-Criteria Linear programming (MCLP) classification method. By integrating techniques of both progressive sampling and classification committee, it designs a dynamic classification committee scheme for MCLP. the experimental results have shown that our idea is feasible and the scheme is effective and efficient for exploring an approximately optimal sample size. the empirical results also help us to further investigate some general specialties of MCLP, such as the more general function expressions reflecting the relationship between accuracy and sample size, and between computing cost and sample size.
In the recent years, classification models based on mathematical programming have been widely used in business intelligence. In this paper, a new Multi-Criteria Quadratic-programming Linear Classification (MQLC) model...
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ISBN:
(纸本)9783540725855
In the recent years, classification models based on mathematical programming have been widely used in business intelligence. In this paper, a new Multi-Criteria Quadratic-programming Linear Classification (MQLC) model is proposed and tested with VIP E-Mail dataset. this experimental study uses a variance of K-fold Cross-Validation to demonstrate the accuracy and stability of the model. Finally, we compare our model withthe decision tree by using commercial software C5.0. the result indicates that the proposed MQLC model performs better than decision tree on small samples.
Aspect Oriented programming (AOP) was introduced 10 years ago and many research projects have focused on broadening AOP and its target areas. However, few applications in the Grid computing world adopt AOP in comparis...
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ISBN:
(纸本)9783540725893
Aspect Oriented programming (AOP) was introduced 10 years ago and many research projects have focused on broadening AOP and its target areas. However, few applications in the Grid computing world adopt AOP in comparison with very vigorous research of AOP. therefore, we present a case study that covers a general networking problem in the Grid computing. AOP provides a novel solution of the problem without modifying existing source code. Aspects that we define are simple, intuitive and reusable. We believe that our implementation is very useful in developing other Grid computing software platforms, and AOP can be a powerful method in modularizing source codes and solving problems of software architectures.
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