344886
From Association to Classification: Inference Using Weight of Evidence
Association and classification are two important tasks in data mining and knowledge discovery. Intensive studies have been carried out in both areas. But, how to apply discovered event associations to classification is still seldom found in current publications. Trying to bridge this gap, this paper extends our previous paper on significant event association discovery to classification. We propose to use weight of evidence to evaluate the evidence of a significant event association in support of, or against, a certain class membership. Traditional weight of evidence in information theory is extended here to measure the event associations of different orders with respect to a certain class. After the discovery of significant event associations inherent in a data set, it is easy and efficient to apply the weight of evidence measure for classifying an observation according to any attribute. With this approach, we achieve flexible prediction.
2919977
High-Order Pattern Discovery from Discrete-Valued Data
To uncover qualitative and quantitative patterns in a data set is a challenging task for research in the area of machine learning and data analysis. Due to the complexity of real-world data, high-order (polythetic) patterns or event associations, in addition to first-order class-dependent relationships, have to be acquired. Once the patterns of different orders are found, they should be represented in a form appropriate for further analysis and interpretation. The authors propose a novel method to discover qualitative and quantitative patterns (or event associations) inherent in a data set. It uses the adjusted residual analysis in statistics to test the significance of the occurrence of a pattern candidate against its expectation. To avoid exhaustive search of all possible combinations of primary events, techniques of eliminating the impossible pattern candidates are developed. The detected patterns of different orders are then represented in an attributed hypergraph which is lucid for pattern interpretation and analysis. Test results on artificial and real-world data are discussed toward the end of the paper.
2498388
Scheduling fixed-priority tasks with preemption threshold
In the context of fixed-priority scheduling, feasibility of a task set with non-preemptive scheduling does not imply the feasibility with preemptive scheduling and vice versa. We use the notion of preemption threshold, first introduced by Express Logic, in their ThreadX real-time operating system, to develop a scheduling model that subsumes both preemptive and non-preemptive fixed priority scheduling. Preemption threshold allows a task to only disable preemption of tasks up to a specified threshold priority. Tasks having priorities higher than the threshold are still allowed to preempt. With this new scheduling model, we show that schedulability is improved as compared to both the preemptive and nonpreemptive scheduling models. We develop the equations for computing the worst-case response times, using the concept of level-i busy period. Some useful results about the generalized model are presented and an algorithm for optimal assignment of priority and preemption threshold is designed based on these results.
3009217
Design and Implementation of an Out-of-Band Virtualization System on Solaris 10
In out-of-band virtualization systems, it is typical that the virtual storage manager (VSM) maintains the metadata for all Agents and performs the address mapping. The Agent performs I/O access according to the mapped address. High network latency, poor performance and low scalability are the main problems of this method. This article introduces an improved design and implementation of an out-of-band virtualization system, in which each Agent maintains a copy of the metadata and performs the address mapping by itself, so as to improve performance, reliability and scalability. This article discusses the on-line extension of a logical volume and the related problems such as synchronization. This function improves the ability of uninterrupted service. The system was tested on the UFS of Solaris 10 and the results showed that the performance of a stripe volume consisting of four disks exceeded that of a linear volume by an average of 103.65%.
14757772
Convergence of a Convolution-Filtering-Based Algorithm for Empirical Mode Decomposition
Lin et al. propose the iterative Toeplitz filters algorithm as an alternative iterative algorithm for Empirical Mode Decomposition (EMD). In this alternative algorithm, the average of the upper and lower envelopes is replaced by certain "moving average" obtained through a low-pass filter. Performing the traditional sifting algorithm with such moving averages is equivalent to iterating certain convolution filters (finite length Toeplitz filters). This paper studies the convergence of this algorithm for signals of continuous variables, and proves that the limit function of this iterative algorithm is an ideal high-pass filtering process.
14617194
Effects of Suffix Repetition Rates of a String on the Performance of String Matching Algorithms
The highly efficient Boyer-Moore’s string matching algorithm utilizes information on multi-occurrences of string suffixes in a pattern string to avoid backtracks in searching the pattern string. One hypothesis is that Boyer-Moore’s algorithm even benefits more from highly self-repetitive patterns. In this paper, the author studies how multi-occurrences of string suffixes affect the performance of the Boyer-Moore’s algorithm as well as some other well known string search algorithms. The paper introduces a new concept of suffix repetition rate (SRR) to measure how frequently the suffixes of a string occur inside of the string. Using this measurement, experiments with several thousands patterns over the entire range of SRRs have been carried out, and the results show that increasing of SRR on pattern strings does not improve the performance of a searching algorithm in terms of efficiency.
3235615
Application of optical proximity correction technology
As process technology scales down to very deep sub-micron (VDSM) in semiconductor manufacturing technology, intrinsic size becomes close to or even shorter than the wavelength used for optical lithography. Thus, some distortions and deformations are introduced by optical proximity effects (OPE) mainly caused by the diffraction and interference of exposure light when layout patterns on a mask are transcribed to a wafer, which influence on the yield and performance of IC circuit. In order to compensate for the deformations, optical proximity correction (OPC) is the most commonly used methodology. Presently, the OPC method is to use a unitary toleration on the whole chip layer, which makes the run time of OPC algorithm longer, causes the size of GDSII files to follow exponential growth, and results in the cost of making mask grow immensely. Firstly, this paper proposes a self-adaptation OPC method with preprocessing function of patterns classification. According to the need of the correction precision, the OPC system divides patterns corrected into two groups with different toleration: critical patterns and general patterns, which enhance the efficiency of the OPC approach. Secondly, a model-based OPC method is presented based on pattern subsection and classification, which keeps the precision of the correction as well as enhances the efficiency. We also propose a rule-based OPC method with general, concise and complete correction rules, and achieve automatic-built rules-based and its looking-up. Thirdly, we also implement an OPC system, called MR-OPC; the MR-OPC system integrates both rule-based OPC and model-based OPC into a whole, so it can solve the confliction between the efficiency and precision. Experimental results show that the MR-OPC system we suggested has advantages of the efficiency and expansibility.