27349578
On multilevel voting
SummaryIn a distributed system mutual exclusion is often used to maintain consistency when restricted operations are performed. Mechanisms guaranteeing mutual exclusions should be both resilient and efficient. Resiliency implies high resource availability in the face of failures, while efficiency implies low overhead incurred by performing restricted operations. In this paper, we propose and study a general paradigm, called multilevel voting, which provides a general framework to assist in the design of resilient and efficient mutual exclusion mechanisms. The proposed method uses multiple level quorum consensus. Unlike another method based on the use of multiple quorum consensus, the proposed model only contains one type of integrity constraints. This has the benefit of being conceptually simple and easy to reason about. The strong resemblance with the traditional quorum consensus makes it easy for the proposed paradigm to embed any technique based on traditional quorum schemes. We show that the proposed model represents the exact class of coteries. This means that not only does it have all the power of coteries, but also all schemes under the model are correct. Thus, should the need arise, we can interchange two schemes freely without using any extra mechanisms to ensure correctness. We study a number of issues that have impact on performance such as the degree of a multilevel scheme and the order of a coterie. We explain how the model can be extended also to model the schemes for the synchronization of read and write of replicated data. We provide algorithms for the design of multilevel schemes in the context of mutual exclusion and that of read and write of replicated data.
37720806
An efficient method for mutual exclusion in truly distributed systems
Many operations in a distributed system require mutual exclusion to guarantee correctness. Quorum methods have been widely proposed for implementing mutual exclusion. Majority voting is the best known quorum method. It has the merit of simplicity, but may incur high message overhead. The tree algorithm is an efficient structured quorum method for mutual exclusion problems. The quorums generated by a tree algorithm are smaller on average than those generated by a majority voting. However, the tree algorithm enforces a highly biased treatment to the nodes at different levels. This affects its performance in a distributed system where the nodes have similar characteristics. We propose a new structured quorum method which treats the nodes more evenly than the tree algorithm yet still preserves a satisfactory availability. We believe that this method is desirable for implementing mutual exclusion in a truly distributed system.<<ETX>>
444470
Handling uncertainties in workflow applications
Workflow techniques are currently seen as the key techniques to improve the effectiveness and productivity of business processes. Most workflow models take a static view toward workflow applications. The static view requires that all the components are known in advance and the structural information alone can uniquely determine an execution path. Some applications may not have either or both of these properties. In these applications, uncertainty exists. In this paper, we study the issues related to uncertainties. We characterize uncertainties into three categories, domain uncertainty, structural uncertainty and implementation uncertainty, and discuss the impact they have on workflow specification and implementation. We then propose approaches to coping with each of them.
10131271
Hierarchical Classification of Documents with Error Control
Classification is a function that matches a new object with one of the predefined classes. Document classification is characterized by the large number of attributes involved in the objects (documents). The traditional method of building a single classifier to do all the classification work would incur a high overhead. Hierarchical classification is a more efficient method -- instead of a single classifier, we use a set of classifiers distributed over a class taxonomy, one for each internal node. However, once a misclassification occurs at a high level class, it may result in a class that is far apart from the correct one. An existing approach to coping with this problem requires terms also to be arranged hierarchically. In this paper, instead of overhauling the classifier itself, we propose mechanisms to detect misclassification and take appropriate actions. We then discuss an alternative that masks the misclassification based on a well known software fault tolerance technique. Our experiments show our algorithms represent a good trade-off between speed and accuracy in most applications.
17319139
Unobtrusive but invasive: using screen recording to collect field data on computer-mediated interaction
We explored the use of computer screen plus audio recording as a methodological approach for collecting empirical data on how teams use their computers to coordinate work. Screen recording allowed unobtrusive collecting of a rich record of actual computer work activity in its natural work setting. The embedded nature of screen recording on laptops made it easy to follow the user's mobility among various work sites. However, the invasiveness of seeing all of the user's interactions with and through the computer raised privacy concerns that made it difficult to find people to agree to participate in this type of detailed study. We discuss measures needed to develop trust with the researchers to enable access to this rich, empirical data of computer usage in the field.
4300632
Keyword Extraction Using Support Vector Machine
This paper is concerned with keyword extraction. By keyword extraction, we mean extracting a subset of words/phrases from a document that can describe the ‘meaning' of the document. Keywords are of benefit to many text mining applications. However, a large number of documents do not have keywords and thus it is necessary to assign keywords before enjoying the benefit from it. Several research efforts have been done on keyword extraction. These methods make use of the ‘global context information', which makes the performance of extraction restricted. A thorough and systematic investigation on the issue is thus needed. In this paper, we propose to make use of not only ‘global context information', but also ‘local context information' for extracting keywords from documents. As far as we know, utilizing both ‘global context information' and ‘local context information' in keyword extraction has not been sufficiently investigated previously. Methods for performing the tasks on the basis of Support Vector Machines have also been proposed in this paper. Features in the model have been defined. Experimental results indicate that the proposed SVM based method can significantly outperform the baseline methods for keyword extraction. The proposed method has been applied to document classification, a typical text mining processing. Experimental results show that the accuracy of document classification can be significantly improved by using the keyword extraction method.
4332072
A Component Retrieval Method Based on Facet-Weight Self-learning
Component-based development method has been a new software development paradigm. How to get the needed components quickly and accurately is one of the basic problems about reusing software component automatically. In this paper, an intelligent component retrieval model – FWRM. is proposed. Facet presentation is used to model query and component. Multiple types of facets are defined which extends traditional keyword-based facet presentation. Genetic algorithm based facet weight self-learning algorithm can change the facet weight dynamically in order to improve retrieval accuracy. Corresponding similarity functions are defined also. In addition, risk minimization-based component sampling method is used to solve the insufficiency of training data. All these algorithms and methods are integrated into FWRM’s three main implementation parts: Facet-Weight Optimize System, Component Retrieve System and Resource. The experimental results prove that this method is feasible and can improve component retrieval effectively.
14427891
Phylogenetic reconstruction from arbitrary gene-order data
Phylogenetic reconstruction from gene-order data has attracted attention from both biologists and computer scientists over the last few years. So far, our software suite GRAPPA is the most accurate approach, but it requires that all genomes have identical gene content, with each gene appearing exactly once in each genome. Some progress has been made in handling genomes with unequal gene content, both in terms of computing pair-wise genomic distances and in terms of reconstruction. In this paper, we present a new approach for computing the median of three arbitrary genomes and apply it to the reconstruction of phylogenies from arbitrary gene-order data. We implemented these methods within GRAPPA and tested them on simulated datasets under various conditions as well as on a real dataset of chloroplast genomes; we report the results of our simulations and our analysis of the real dataset and compare them to reconstructions made by using neighbor-joining and using the original GRAPPA on the same genomes with equalized gene contents. Our new approach is remarkably accurate both in simulations and on the real dataset, in contrast to the distance-based approaches and to reconstructions using the original GRAPPA applied to equalized gene contents.
15896075
Effective bandwidth-based QoS provisioning for real-time audio/video streaming over MIMO-OFDM wireless networks
We propose a cross-layer design scheme to support the real-time quality of service (QoS) guarantees for heterogeneous mobile users over downlink MIMO-OFDM wireless networks. Inspired by the effective bandwidth theory originally established for wired networks, we develop the approach to apply the theory into wireless networks. Based on MIMO-OFDM infrastructure employed at the physical (PHY)-layer, we analytically derive the medium access control (MAC)-layer admission-control and time-slot allocation algorithms to guarantee the statistical delay-bound for different real-time users. Simulation results show that the proposed scheme can efficiently support the wireless QoS requirement for diverse real-time mobile users.