1859019
A parallel algorithm for clustering protein-protein interaction networks
The increasing availability of interaction graphs requires new resource-efficient tools capable of extracting valuable biological knowledge from these networks. In this paper we report on a novel parallel implementation of Girvan and Newman's clustering algorithm that is capable of running on clusters of computers. Our parallel implementation achieves almost linear speed-up up to 32 processors and allows us to run this computationally intensive algorithm on large protein-protein interaction networks. Preliminary experiments show that the algorithm has very high accuracy in identifying functional related protein modules.
18229979
A Compression-Boosting Transform for Two-Dimensional Data
We introduce a novel invertible transform for two- dimensional data which has the objective of reordering the matrix so it will improve its (lossless) compression at later stages. The transform requires to solve a computationally hard problem for which a randomized algorithm is used. The inverse transform is fast and can be implemented in linear time in the size of the matrix. Preliminary experimental results show that the reordering improves the compressibility of digital images.
32009068
Finding biclusters by random projections
Given a matrix X composed of symbols, a bicluster is a submatrix of X obtained by removing some of the rows and some of the columns of X in such a way that each row of what is left reads the same string. In this paper, we are concerned with the problem of finding the bicluster with the largest area in a large matrix X. The problem is first proved to be NP-complete. We present a fast and efficient randomized algorithm that discovers the largest bicluster by random projections. A detailed probabilistic analysis of the algorithm and an asymptotic study of the statistical significance of the solutions are given. We report results of extensive simulations on synthetic data.
7388186
Mining Web Logs to Improve Web Caching and Prefetching
Caching and prefetching are well known strategies for improving the performance of Internet systems. The heart of a caching system is its page replacement policy, which selects the pages to be replaced in a proxy cache when a request arrives. By the same token, the essence of a prefetching algorithm lies in its ability to accurately predict future request. In this paper, we present a method for caching variable-sized web objects using an n-gram based prediction of future web requests. Our method aims at mining a prediction model from the web logs for document access patterns and using the model to extend the well-known GDSF caching policy. In addition, we present a new method to integrate this caching algorithm with a prediction-based prefetching algorithm. We empirically show that the system performance is greatly improved using the integrated approach.
2823399
Reducing the Calibration Effort for Location Estimation Using Unlabeled Samples
WLAN location estimation based on 802.11 signal strength is becoming increasingly prevalent in today's pervasive computing applications. As an alternative to the well-established deterministic approaches, probabilistic location determination techniques show good performance and thus become increasingly popular. For these techniques to achieve a high level of accuracy, however, adequate training samples should be collected offline for calibration. As a result, a great amount of manual effort is incurred. In this paper, we aim to solve the problem by reducing both the sampling time and the number of locations sampled in constructing the radio map. A learning algorithm is proposed to build location estimation systems based on a small fraction of the calibration data that traditional techniques require and a collection of user traces that can be cheaply obtained. Our experiments show that unlabeled user traces can be used to compensate for the effects of reducing calibration effort and can even improve the system performance. Consequently, manual effort can be significantly reduced while a high level of accuracy is still achieved
36641724
A new and efficient FFT algorithm for distributed memory systems
This paper presents a new and optimal parallel implementation of multidimensional fast Fourier transform algorithm on distributed memory multiprocessors. Its optimality is obtained by minimizing the number of message passings necessary, at the cost of increase in message length. This distinctive feature of the new algorithm effectively utilizes the important architectural property of most of today's distributed memory multiprocessors-wormhole routing for interprocessor communications. By using the algebra of stride permutations and tenser products as a mathematical tool, we are able to derive and formulate an efficient data partition and communication scheme that reduces communication cost from O(N/sup 2/) required for the best known FFT to O(N) on an N/sup 2/-processor machine. Our data partition scheme is natural and efficient for solving discretized boundary value problems such as partial differential equations and finite element analysis. To evaluate the actual performance of our new algorithm in comparison with other existing parallel FFT algorithms, we have carried out implementation experiments on the Intel's Touchstone Delta machine.
15776610
Delaying Variable Binding Commitments in Planning
One of the problems with many partial-order planners is their eager commitment to variable bindings. This is contrary to their control decision in delayed-commitment of operator orderings. In this paper we present an extension to the classical partial-order planners by associating every variable with a finite domain of values. The extended planner applies constraint satisfaction routines to check for the consistency of variable bindings. With the extended planner, we perform a set of experiments to show that it is able to reduce the amount of backtracking in domains where few variable instantiations can lead to final solutions. We also investigate the frequency of CSP application to determine how often should constraint checking be performed in order to yield the best performance.
1777247
Web-page classification through summarization
Web-page classification is much more difficult than pure-text classification due to a large variety of noisy information embedded in Web pages. In this paper, we propose a new Web-page classification algorithm based on Web summarization for improving the accuracy. We first give empirical evidence that ideal Web-page summaries generated by human editors can indeed improve the performance of Web-page classification algorithms. We then propose a new Web summarization-based classification algorithm and evaluate it along with several other state-of-the-art text summarization algorithms on the LookSmart Web directory. Experimental results show that our proposed summarization-based classification algorithm achieves an approximately 8.8% improvement as compared to pure-text-based classification algorithm. We further introduce an ensemble classifier using the improved summarization algorithm and show that it achieves about 12.9% improvement over pure-text based methods.