1043193
Dependability Prediction of High Availability OSCAR Cluster Server
High availability (HA) computing has recently gained much attention, especially in enterprise and mission critical systems. The HA is now a necessity that is no longer regarded as a luxury feature. Thus, we, conjunctively with the open source community, are in process of enhancing the HA feature to Open Source Cluster Application Resources (OSCAR), a widely adopted Linux PC cluster system. Server redundancy will be our initial key aspect of the next generation HA OSCAR cluster system. In this paper, we introduce a HA server for OSCAR cluster system. Its architecture and mechanism is discussed, and then we model and predict the dependability of the system by a Petri net-based model, Stochastic Reword Net (SRN). The reliability and instantaneous availability of the system are presented as a result.
31684732
Achieving high availability and performance computing with an HA-OSCAR cluster
High availability (HA) computing has long gained much attention in enterprise and mission critical systems. HA goals are to maximize the uptime, thus undoubtedly complementing high-performance computing (HPC) objectives. HA-OSCAR is a project that aims to improve HA in commercial-off-the-shelf (COTS)-based HPC environments. In this paper, we introduce a multi-head HPC cluster architecture. Server redundancy is an initial key aspect aiming toward downtime reduction. Two HA-OSCAR types, active-standby and active-active, are studied. We evaluate system dependability for given two models. Stochastic Reward Nets (SRN) are used to model the system availability. We describe our SRN modeling using Stochastic Petri Net Package, and compute several interesting results that characterize HA-OSCAR availability.
13929087
Availability prediction and modeling of high mobility OSCAR cluster
Since the initial introduction of open source cluster application resources (OSCAR), this software package has been a well-accepted choice for building high performance computing systems. As it continues to be applied to mission-critical environments, high availability (HA) features therefore are needed to be included in OSCAR cluster. In this paper, we provide a HA solution for OSCAR cluster. As a widely used technique in HA solutions, component redundancy is adopted to improve the system availability. Based on the proposed architecture, we develop a detailed failure-repair model for predicting the availability of HA OSCAR cluster. stochastic reward nets (SRN) are used to model the behavior of the system. We specify our SRN model to stochastic Petri net package, describe and compute several interesting output measures that characterize availability features of HA OSCAR cluster.
19118237
A machine learning approach for the curation of biomedical literature: KDD Cup 2002 (task 1)
In this paper, we present an automated text classification system for the classification of biomedical papers. This classification is based on whether there is experimental evidence for the expression of molecular gene products for specified genes within a given paper. The system performs pre-processing and data cleaning, followed by feature extraction from the raw text. It subsequently classifies the paper using the extracted features with a Naïve Bayes Classifier. Our approach has made it possible to classify (and curate) biomedical papers automatically, thus potentially saving considerable time and resources.
6400431
Applying rough sets to market timing decisions
A lot of research has been done to predict economic development. The problem studied here is about the stock prediction for use of investors. More specifically, the stock market's movements will be analyzed and predicted. We wish to retrieve knowledge that could guide investors on when to buy and sell. 
 
Through a detailed case study on trading S&P 500 index, rough sets is shown to be an applicable and effective tool to achieve this goal. Some problems concerning time series transformation, indicator selection, trading system building in real implementation are also discussed.
8343773
A spatio-temporal multi-modal data management and analysis environment for tracking MS lesions
We describe the development of a system that automates data collection, metadata extraction and analysis of spatio-temporal multi-modal data, combining data management and data analysis to provide an efficient resource for clinicians. Though the system is extensible to many applications, the current focus is on managing Multiple Sclerosis (MS) lesion data, which are disparate streams of image, numeric, and text data. In order to discover patterns of MS pathology and plan early and effective treatment, multispectral magnetic resonance (MR) image streams collected over time need to be correlated efficiently with each other and with patient performance and clinical data streams.
6411349
A Modified Chi2 Algorithm for Discretization
Since the ChiMerge algorithm was first proposed by Kerber (1992), it has become a widely used and discussed discretization method. The Chi2 algorithm is a modification to the ChiMerge method. It automates the discretization process by introducing an inconsistency rate as the stopping criterion and it automatically selects the significance value. In addition, it adds a finer phase aimed at feature selection to broaden the applications of the ChiMerge algorithm. However, the Chi2 algorithm does not consider the inaccuracy inherent in ChiMerge's merging criterion. The user-defined inconsistency rate also brings about inaccuracy to the discretization process. These two drawbacks are first discussed in this paper and modifications to overcome them are then proposed. By comparison, results with the original Chi2 algorithm using C4.5, the modified Chi2 algorithm, performs better than the original Chi2 algorithm. It becomes a completely automatic discretization method.
2847866
A Machine Learning Approach for the Curation of Biomedical Literature
In the field of the biomedical sciences there exists a vast repository of information located within large quantities of research papers. Very often, researchers need to spend considerable amounts of time reading through entire papers before being able to determine whether or not they should be curated (archived). In this paper, we present an automated text classification system for the classification of biomedical papers. This classification is based on whether there is experimental evidence for the expression of molecular gene products for specified genes within a given paper. The system performs pre-processing and data cleaning, followed by feature extraction from the raw text. It subsequently classifies the paper using the extracted features with a Naive Bayes Classifier. Our approach has made it possible to classify (and curate) biomedical papers automatically, thus potentially saving considerable time and resources. The system proved to be highly accurate, and won honourable mention in the KDD Cup 2002 task 1.