Running MapReduce programs in the public cloud introduces the important problem: how to optimize resource provisioning to minimize the financial charge for a specific job? In this paper, we study the whole process of ...
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Running MapReduce programs in the public cloud introduces the important problem: how to optimize resource provisioning to minimize the financial charge for a specific job? In this paper, we study the whole process of MapReduce processing and build up a cost function that explicitly models the relationship between the amount of input data, the available system resources (Map and Reduce slots), and the complexity of the Reduce function for the target MapReduce job. The model parameters can be learned from test runs with a small number of nodes. Based on this cost model, we can solve a number of decision problems, such as the optimal amount of resources that can minimize the financial cost with a time deadline or minimize the time under certain financial budget. Experimental results show that this cost model performs well on tested MapReduce programs.
lti-label learning aims at predicting a proper label set for each unseen *** instance in the dataset is associated with a set of predefined ***-label learning approaches frequently used choose identical feature set to...
lti-label learning aims at predicting a proper label set for each unseen *** instance in the dataset is associated with a set of predefined ***-label learning approaches frequently used choose identical feature set to determine the instance's membership of each label.
Federated policy systems are required to support the emergent complexity and organizational heterogeneity of modern Internet service delivery. This paper presents a distributed policy management approach which utilize...
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Federated policy systems are required to support the emergent complexity and organizational heterogeneity of modern Internet service delivery. This paper presents a distributed policy management approach which utilizes a flexible, tree-based capability authority model to partition and delegate federated capabilities or services. A trust management model and a delegation logic is defined which supports secure decentralized policy reasoning and addresses performance overheads due to distributed rule evaluation, threats from malformed or malicious federated principals and allows flexibility with respect to delegation chain reduction or capability authority re-partitioning. The system is evaluated through a security analysis and a prototype implementation of a federated policy engineering framework based on this logic is described. This framework is based on public key certificates and an extension to the Keynote Trust Management language. It provides practical management services such as key discovery and certificate revocation in addition to the core capability delegation function.
In this work, we use folksonomies for building user preference list (UPL) based on user's search history. A UPL is an indispensable source of knowledge which can be exploited by intelligent systems for query recom...
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In this work, we use folksonomies for building user preference list (UPL) based on user's search history. A UPL is an indispensable source of knowledge which can be exploited by intelligent systems for query recommendation, personalized search, and web search result ranking etc. A UPL consist of list of concepts, and their weights, clustered together using agglomerative clustering by employing Google Similarity Distance. We show how to design and implement such a system in practice and visualize the UPL which aids in finding interesting relationships between terms and detect outliers, if any. The experiment reveals that UPL not only captures user interests but also its context and results are very promising.
Nowadays PDF documents have become a dominating knowledge repository for both the academia and industry largely because they are very convenient to print and exchange. However, the methods of automated structure infor...
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Research on sensor network had been focused on security services that provide authentication, confidentiality, integrity and availability until recently, but now there is growing interest in tackling the problem of ac...
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Research on sensor network had been focused on security services that provide authentication, confidentiality, integrity and availability until recently, but now there is growing interest in tackling the problem of actual sensor IDs being exposed. Many techniques for providing anonymity to the source in an ad-hoc network have been proposed, but they are not suitable. Thus, a technique that is well-suited to the characteristics of sensor networks is needed. This paper, limiting the type of attack against a sensor network to that of eavesdropping, proposes a new technique for providing anonymity using Phantom ID and SMAC. The degree of anonymity provided by the proposed technique was analyzed using an entropy-based modeling technique. The results showed that the anonymity is high when the proposed technique is used. The key factor responsible for the improved anonymity had to do with disguising the sensor ID so that it cant be found easily.
Employing multi-level abstraction in modeling refers to representing objects at multiple levels of one or more abstraction hierarchies, mainly classification, aggregation and generalization. Multiple representation, h...
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作者：
Wei DuZhongbo CaoYan WangEnrico BlanzieriChen ZhangYanchun LiangCollege of Mathematics
Jilin University Changchun 130012 China Department of Information and Communication Technology University of Trento Povo 38050 Italy College of Computer Science and Technology
Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China
Based on analysis of relationships between solution qualities and number of initial dynamic points in elastic net algorithm, we propose an improved elastic network algorithm (IENA) introduced in a heuristic cloning st...
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Two-dimensional polyacrylamide gel electrophoresis of proteins is a robust and reproducible technique. It is the most widely used separation tool in proteomics. Current efforts in the field are directed at development...
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Two-dimensional polyacrylamide gel electrophoresis of proteins is a robust and reproducible technique. It is the most widely used separation tool in proteomics. Current efforts in the field are directed at development of tools for expanding the range of proteins accessible with two-dimensional gels. Proteomics was built around the two-dimensional gel. The idea that multiple proteins can be analyzed in parallel grew from two-dimensional gel maps. Proteomics researchers needed to identify interested protein spots by examining the gel. This is time consuming, labor extensive and error prone. It is desired that the computer can analyze the proteins automatically by first detecting then quantifying the protein spots in the 2-D gel images. In our previous work, we presented a new technique for segmentation of 2-D gel images using the fuzzy c-means algorithm using the notion of fuzzy relations. In this paper, we will describe the new relational fuzzy c-means algorithm (RFCM) and use it for automatic protein spots quantification. We will also use two methods to evaluate its performance: the unsupervised evaluation method and comparison with the expert spots quantification.
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