A nonblocking transaction data flow graph based protocol forreplicated databases

Replicated data management systems adopt the 1-copy serializability criteria for processing transactions. In order to achieve this goal, many approaches rely on obtaining votes from other sites for processing update requests. In the proposed approach, a technique for generation of precedence graphs for each transaction execution is analyzed. The transaction data flow graph approach is a fully distributed approach. The proposed technique, is free from deadlocks, and avoids resubmission of transactions     

Performance assessment of foraging algorithms vs. evolutionary algorithms

The class of foraging algorithms is a relatively new field based on mimicking the foraging behavior of animals, insects, birds or fish in order to develop efficient optimization algorithms. The artificial bee colony (ABC), the bees algorithm (BA), ant colony optimization (ACO), and bacterial foraging optimization algorithms (BFOA) are examples of this class to name a few. This work provides a complete performance assessment of the four mentioned algorithms in comparison to the widely known differential evolution (DE), genetic algorithms (GAs), harmony search (HS), and particle swarm optimization (PSO) algorithms when applied to the problem of unconstrained nonlinear continuous function optimization. To the best of our knowledge, most of the work conducted so far using foraging algorithms has been tested on classical functions. This work provides the comparison using the well-known CEC05 benchmark functions based on the solution reached, the success rate, and the performance rate.     

Performance of garbage collection algorithms for flash-based solid state drives with hot/cold data

To avoid a poor random write performance, flash-based solid state drives typically rely on an internal log-structure. This log-structure reduces the write amplification and thereby improves the write throughput and extends the drive’s lifespan. In this paper, we analyze the performance of the log-structure combined with the d$d$-choices garbage collection algorithm, which repeatedly selects the block with the fewest number of valid pages out of a set of d$d$ randomly chosen blocks, and consider non-uniform random write workloads. Using a mean field model, we show that the write amplification worsens as the hot data gets hotter.     

Performance analysis of three text-join algorithms

When a multidatabase system contains textual database systems (i.e., information retrieval systems), queries against the global schema of the multidatabase system may contain a new type of joins-joins between attributes of textual type. Three algorithms for processing such a type of joins are presented and their I/O costs are analyzed in this paper. Since such a type of joins often involves document collections of very large size, it is very important to find efficient algorithms to process them. The three algorithms differ on whether the documents themselves or the inverted files on the documents are used to process the join. Our analysis and the simulation results indicate that the relative performance of these algorithms depends on the input document collections, system characteristics, and the input query. For each algorithm, the type of input document collections with which the algorithm is likely to perform well is identified. An integrated algorithm that automatically selects the best algorithm to use is also proposed     

Performance analysis of general tracking algorithms

A general family of tracking algorithms for linear regression models is studied. It includes the familiar least mean square gradient approach, recursive least squares, and Kalman filter based estimators. The exact expressions for the quality of the obtained estimates are complicated. Approximate, and easy-to-use, expressions for the covariance matrix of the parameter tracking error are developed. These are applicable over the whole time interval, including the transient, and the approximation error can be explicitly calculated     

Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data

Data mining is the process of discovering meaningful new correlation, patterns and trends by sifting through large amounts of data, using pattern recognition technologies as well as statistical and mathematical techniques. Cluster analysis is often used as one of the major data analysis technique widely applied for many practical applications in emerging areas of data mining. Two of the most delegated, partition based clustering algorithms namely k-Means and Fuzzy C-Means are analyzed in this research work. These algorithms are implemented by means of practical approach to analyze its performance, based on their computational time. The telecommunication data is the source data for this analysis. The connection oriented broad band data is used to find the performance of the chosen algorithms. The distance (Euclidian distance) between the server locations and their connections are rearranged after processing the data. The computational complexity (execution time) of each algorithm is analyzed and the results are compared with one another. By comparing the result of this practical approach, it was found that the results obtained are more accurate, easy to understand and above all the time taken to process the data was substantially high in Fuzzy C-Means algorithm than the k-Means.     

Missing data imputation in multivariate data by evolutionary algorithms

This paper presents a proposal based on an evolutionary algorithm to impute missing observations in multivariate data. A genetic algorithm based on the minimization of an error function derived from their covariance matrix and vector of means is presented.All methodological aspects of the genetic structure are presented. An extended explanation of the design of the fitness function is provided. An application example is solved by the proposed method.     

Sequential algorithms on concrete data structures

We provide a sequential denotational semantics for sequential programming languages, based on a new notion of sequential algorithm on the Kahn-Plotkin concrete data structures. Intuitively an algorithm may be seen either as a concrete object—a “program” in a simple output-driven language — or as an abstract object — the pair of a sequential function and of a computation strategy for it. The concrete and abstract presentations are equivalent, as shown by a representation theorem. The algorithms form a cartesian closed category with straightforward solutions to recursive domain equations. Hence they may replace functions in the denotational semantics of any sequential language. An applicative programming language based on sequential algorithms is presented in a companion paper.     

数据挖掘算法研究与综述

数据挖掘方法结合了机器学习、模式识别、统计学、数据库和人工智能等众多领域的知识,是解决从大量信息中获取有用知识、提供决策支持的有效途径,具有广泛的应用前景.以关联、分类、聚类归类,对当前数据挖掘的多种方法进行了研究,并指出其现存的问题.这些方法都有局限性,多方法融合、有机组合互补将成为数据挖掘的发展趋势.     

Performance evaluation of iterative geometric fitting algorithms

The convergence performance of typical numerical schemes for geometric fitting for computer vision applications is compared. First, the problem and the associated KCR lower bound are stated. Then, three well-known fitting algorithms are described: FNS, HEIV, and renormalization. To these, we add a special variant of Gauss-Newton iterations. For initialization of iterations, random choice, least squares, and Taubin's method are tested. Simulation is conducted for fundamental matrix computation and ellipse fitting, which reveals different characteristics of each method.     

高维数据聚类方法综述*

总结了高维数据聚类算法的研究现状,分析比较了算法性能的主要差异,并指出其今后的发展趋势,即在子空间聚类过程中融入其他传统聚类方法的思想,以提高聚类性能。     

面向大数据的K-means算法综述

聚类作为一种重要的数据挖掘方式,如何在海量数据下更快获得一个有理论保证的K-means的近似解则是一个关键问题。首先,定义K-means问题并介绍相关背景;然后,从理论保证和加速两个方面分别介绍国内外先进研究成果;最后,总结现有的成果并对未来的面向大数据的K-means研究方向予以展望和预测。     

Performance of synchronous parallel algorithms with regularstructures

New methods are presented for bounding and approximating the mean execution time of partitioning algorithm, and these methods are compared to previous approaches. Distribution-driven and program-driven simulations show that two of the methods are usually accurate to within 10% and give good estimates even when certain independence assumptions are violated. Asymptotic approximations and upper bounds are derived for the average execution time of multiphase algorithms when there is no contention for processes in the parallel phase. In addition, the authors bound the average execution time under static and dynamic scheduling policies and determine the optimum number of parallel tasks to be created to minimize the execution time bounds with constant scheduling overhead     

Performance of phase-based algorithms for disparity estimation

Stereoscopic depth analysis by means of disparity estimation has been a classical topic of computer vision, from the biological models of stereopsis [1] to the widely used techniques based on correlation or sum of squared differences [2]. Most of the recent work on this topic has been devoted to the phase-based techniques, developed because of their superior performance and better theoretical grounding [3, 4]. In this article we characterize the performance of phase-based disparity estimators, giving quantitative measures of their precision and their limits, and how changes in contrast, imbalance, and noise in the two stereo images modify the attainable accuracy. We find that the theoretical range of measurable disparities, one period of the modulation of the filter, is not attainable: the actual range is approx. two-thirds of this value. We show that the phase-based disparity estimators are robust to changes in contrast of 100% or more and well tolerate imbalances of luminosity of 400% between the images composing the stereo pair. Clearing the Gabor filter of its DC component has been often advocated as a means to improve the accuracy of the results. We give a quantitative measure of this improvement and show that using a DC-free Gabor filter leads to disparity estimators nearly insensitive to contrast and imbalance. Our tests show that the most critical source of error is noise: the error increases linearly with the increase in noise level. We conclude by studying the influence of the spectra and the luminosity of the input images on the error surface, for both artificial and natural images, showing that the spectral structure of the images has little influence on the results, changing only the form of the error surface near the limits of the detectable disparity range. In conclusion, this study allows estimation of the expected accuracy of custom-designed phase-based stereo analyzers for a combination of the most common error sources.     

图像边缘检测算法的对比分析

对一些常用的图像边缘检测算法进行简单的介绍,并通过实验对各种算法进行比较,分析了这些算子边缘检测的优缺点,以及导致它们差异的具体原因.这对于进一步学习和寻找更好的数字图像边缘检测方法具有实用意义.     

Performance analysis of distributed deadlock detection algorithms

The paper presents a probabilistic performance analysis of a deadlock detection algorithm in distributed systems. Although there has been extensive study on deadlock detection algorithms in distributed systems, little attention has been paid to the study of the performance of these algorithms. Most work on performance study has been achieved through simulation but not through an analytic model. Min (1990), to the best of our knowledge, made the sole attempt to evaluate the performance of distributed deadlock detection algorithms analytically. Being different from Min's, our analytic approach takes the time-dependent behavior of each process into consideration rather than simply taking the mean-value estimation. Furthermore, the relation among the times when deadlocked processes become blocked is studied, which enhances the accuracy of the analysis. We measure performance metrics such as duration of deadlock, the number of algorithm invocations, and the mean waiting time of a blocked process. It is shown that the analytic estimates are nearly consistent with simulation results