The approach of User-Aware Tenancy integrates the high configurability of multi-tenant applications with the flexibility and the functional variability of Rich-Variant Component use. Multi-tenancy concept consists in ...
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ISBN:
(纸本)9781467381499
The approach of User-Aware Tenancy integrates the high configurability of multi-tenant applications with the flexibility and the functional variability of Rich-Variant Component use. Multi-tenancy concept consists in sharing instances among a large group of customers, called tenants. Multi-tenancy is a tool to exploit economies of scale widely promoted by Software as a Service (SaaS) models. However, the ability of a SaaS application to be adapted to individual tenant's needs seem to be a major requirement. Thus, our approach focuses on more flexibility and more reusability for Multi-tenant SaaS application using the multiview notion of Rich-Variant Components. The approach consists in a user-aware tenancy for SaaS. In this paper, we provide an application of an algorithm deriving the necessary instances of Rich-Variant Components building the application in a scalable and performing manner. The algorithm is based on fundamental concepts from the graph theory, and is accompanied by a reduced school management application as an illustrating example.
Ultra dense networks are a promising technology enabling high power and spectrum efficiencies in future wireless systems. It is well-known that for ultra dense networks inter-cell interference is one of the main bottl...
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ISBN:
(纸本)9781479987955
Ultra dense networks are a promising technology enabling high power and spectrum efficiencies in future wireless systems. It is well-known that for ultra dense networks inter-cell interference is one of the main bottlenecks prohibiting achieving the promised performance gains. In order to effectively coordinate or mitigate interference in paper, we propose a graph-based low complexity dynamic clustering algorithm. The key idea behind the proposed algorithm is that dividing the whole network into a number of clusters under size constraint and the maximum intra-cluster interference and minimum inter-cluster interference. The logic is maximum intra-cluster can be effectively controlled by the coordination within each cluster. Meanwhile, graph-based algorithm is exploited to further reduce implementation complexity and make the proposed algorithm suitable for practical implementation. Finally, simulation results numerically demonstrate that the proposed low complexity algorithm has almost the same performance compared to the existing high performance algorithm but the complexity is much lower.
Outlier detection has a large variety of applications ranging from detecting intrusion in a computer network, to forecasting hurricanes and tornados in weather data, to identifying indicators of potential crisis in st...
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Outlier detection has a large variety of applications ranging from detecting intrusion in a computer network, to forecasting hurricanes and tornados in weather data, to identifying indicators of potential crisis in stock market data, etc. The problem of finding outliers in sequential data has been widely studied in the data mining literature and many techniques have been developed to tackle the problem in various application domains. However, many of these techniques rely on the peculiar characteristics of a specific type of data to detect the outliers. As a result, they cannot be easily applied to different types of data in other application domains;they should at least be tuned and customized to adapt to the new domain. They also may need certain amount of training data to build their models. This makes them hard to apply especially when only a limited amount of data is available. The work described in this paper tackle the problem by proposing a graph-based approach for the discovery of contextual outliers in sequential data. The developed algorithm offers a higher degree of flexibility and requires less amount of information about the nature of the analyzed data compared to the previous approaches described in the literature. In order to validate our approach, we conducted experiments on stock market and weather data;we compared the results with the results from our previous work. Our analysis of the results demonstrate that the algorithm proposed in this paper is successful and effective in detecting outliers in data from different domains, one financial and the other meteorological. (C) 2014 Elsevier B.V. All rights reserved.
A stable and accurate point matching algorithm named Bidirectional Weight graph Transformation Matching (BWGTM) is proposed in this paper. The algorithm starts with a set of correspondences which contain a variable nu...
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ISBN:
(纸本)9781479939039
A stable and accurate point matching algorithm named Bidirectional Weight graph Transformation Matching (BWGTM) is proposed in this paper. The algorithm starts with a set of correspondences which contain a variable number of erroneous correspondences, or outliers, in addition to a fixed number of true correspondences (inliers). For each feature point and its K nearest neighbors (KNN), there are two set of graphs to be generated. Depending on the co-angular distances between edges that connect a feature point to its KNN in graphs, the vertices with maximum distance will be founded and deemed as candidate outliers. Considering that some inliers whose KNN consist of outliers are regarded as candidate outliers, a recovery strategy utilizes the addition of fresh vertices to regain these inliers. Experimental results demonstrate the superior performance of this algorithm under various conditions for images.
A stable and accurate point matching algorithm named Bidirectional Weight graph Transformation Matching (BWGTM) is proposed in this paper. The algorithm starts with a set of correspondences which contain a variable nu...
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ISBN:
(纸本)9781479939046
A stable and accurate point matching algorithm named Bidirectional Weight graph Transformation Matching (BWGTM) is proposed in this paper. The algorithm starts with a set of correspondences which contain a variable number of erroneous correspondences, or outliers, in addition to a fixed number of true correspondences (inliers). For each feature point and its K nearest neighbors (KNN), there are two set of graphs to be generated. Depending on the co-angular distances between edges that connect a feature point to its KNN in graphs, the vertices with maximum distance will be founded and deemed as candidate outliers. Considering that some inliers whose KNN consist of outliers are regarded as candidate outliers, a recovery strategy utilizes the addition of fresh vertices to regain these inliers. Experimental results demonstrate the superior performance of this algorithm under various conditions for images.
This paper presents an automatic point matching algorithm for establishing accurate match correspondences in two or more images. The proposed algorithm utilizes a group of feature points to explore their geometrical r...
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This paper presents an automatic point matching algorithm for establishing accurate match correspondences in two or more images. The proposed algorithm utilizes a group of feature points to explore their geometrical relationship in a graph arrangement. The algorithm starts with a set of matches (including outliers) between the two images. A set of nondirectional graphs is then generated for each feature and its K nearest matches (chosen from the initial set). Using the angular distances between edges that connect a feature point to its K nearest neighbors in the graph, the algorithm finds a graph in the second image that is similar to the first graph. In the case of a graph including outliers, the algorithm removes such outliers (one by one, according to their strength) from the graph and re-evaluates the angles until the two graphs are matched or discarded. This is a simple intuitive and robust algorithm that is inspired by a previous work. Experimental results demonstrate the superior performance of this algorithm under various conditions, such as rigid and nonrigid transformations, ambiguity due to partial occlusions or match correspondence multiplicity, scale, and larger view variation.
Manifold Ranking (MR), a graph-based ranking algorithm, has been widely applied in information retrieval and shown to have excellent performance and feasibility on a variety of data types. Particularly, it has been su...
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ISBN:
(纸本)9781450309349
Manifold Ranking (MR), a graph-based ranking algorithm, has been widely applied in information retrieval and shown to have excellent performance and feasibility on a variety of data types. Particularly, it has been successfully applied to content-based image retrieval, because of its outstanding ability to discover underlying geometrical structure of the given image database. However, manifold ranking is computationally very expensive, both in graph construction and ranking computation stages, which significantly limits its applicability to very large data sets. In this paper, we extend the original manifold ranking algorithm and propose a new framework named Efficient Manifold Ranking (EMR). We aim to address the shortcomings of MR from two perspectives: scalable graph construction and efficient computation. Specifically, we build an anchor graph on the data set instead of the traditional k-nearest neighbor graph, and design a new form of adjacency matrix utilized to speed up the ranking computation. The experimental results on a real world image database demonstrate the effectiveness and efficiency of our proposed method. With a comparable performance to the original manifold ranking, our method significantly reduces the computational time, makes it a promising method to large scale real world retrieval problems.
The detection of an over-represented sub-sequence in a set of (carefully chosen) DNA sequences is often the main clue leading to the investigation of a possible functional role for such a subsequence. Over-represented...
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ISBN:
(纸本)9781424447350
The detection of an over-represented sub-sequence in a set of (carefully chosen) DNA sequences is often the main clue leading to the investigation of a possible functional role for such a subsequence. Over-represented substrings (with possibly local mutations) in a biological string are termed motifs. A typical functional unit that can be modeled by a motif is a Transcription Factor Binding Site (TFBS), a portion of the DNA sequence apt to the binding of a protein that participates in complex transcriptomic biochemical reactions. In the literature it has been proposed a simplified combinatorial problem called the planted (l-d)-motif problem (known also as the (l-d) Challenge Problem) that captures the essential combinatorial nature of the motif finding problem. In this paper we propose a novel graph-based algorithm for solving a refinement of the (l-d) Challenge Problem. Experimental results show that instances of the (l-d) Challenge Problem considered difficult for competing state of the art methods in literature can be solved efficiently in our framework.
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