14581620
Network cache model for wireless proxy caching
We propose a wireless network cache model to facilitate the cooperative proxy caching in wireless base stations. A self-configured and adaptive virtual proxy graph (VPG) is used to achieve efficient data search, data cache and data replication. Based on demand, the aggregate effect of data caching, searching and replicating actions by individual proxies automatically distributes cached Web documents closer to the interested mobile clients. Our performance study demonstrates that the proposed individual-based wireless proxy caching scheme outperforms existing caching schemes in terms of different performance metrics.
7304743
Proxy ecology - cooperative proxies with artificial life
In this paper, we propose a novel P2P cooperative proxy cache system using an individual-based model. We borrow the idea from an ecological system as well as economic systems to manage the cooperative proxies through data and information exchange among individual proxies. The data flow among proxy nodes creates artificial life for the cooperative proxies. The proxy servers with artificial life can automatically configure themselves into a virtual proxy graph. The aggregate effect of caching actions by individual peer proxies automatically distributes the Web document closer to the clients and balances the workload. Our simulation results show that the proposed proxy caching scheme tremendously improves the system performance. In addition, the individual-based design model ensures the simplicity and scalability of the cache system.
54104798
Data allocation algorithms for distributed video servers
9138036
Earthworm: a network memory management technique for large-scale distributed multimedia applications
The two main operating constraints of today's multimedia servers are the I/O bandwidth and communication bandwidth limitations. Both of these problems are addressed in this paper using a novel technique called Earthworm. In this scheme, the network memory is used as a huge cache for buffering multimedia data. Dramatic reduction in the demand on the I/O bandwidth, therefore, can be achieved. This scheme also chains display stations to allow them to forward video streams. This strategy eliminates the congestion at the communication port of the server. Removing this bottleneck allows our technique to operate on the vast aggregate bandwidth of the WAN rather than being constrained by the very limited local bandwidth available to the server. A unique feature of the Earthworm approach is that every display station using the server attempts to make some contribution to the caching space and communication bandwidth. The arrival of a new request, therefore, can be seen as a contributor, rather than just a burden to the server. This characteristic ensures the scalability of our design to support very large multimedia applications.
14997765
2PSM: an efficient framework for searching video information in a limited-bandwidth environment
Abstract. We present a novel technique, called 2-Phase Service Model, for streaming videos to home users in a limited-bandwidth environment. This scheme first delivers some number of non-adjacent data fragments to the client in Phase 1. The missing fragments are then transmitted in Phase 2 as the client is playing back the video. This approach offers many benefits. The isochronous bandwidth required for Phase 2 can be controlled within the capability of the transport medium. The data fragments received during Phase 1 can be used to provide an excellent preview of the video. They can also be used to facilitate VCR-style operations such as fast-forward and fast-reverse. Systems designed based on this method are less expensive because the fast-forward and fast-reverse versions of the video files are no longer needed. Eliminating these files also improves system performance because mapping between the regular files and their fast-forward and fast-reverse versions is no longer part of the VCR operations. Furthermore, since each client machine handles its own VCR-style interaction, this technique is very scalable. We provide simulation results to show that 2-Phase Service Model is able to handle VCR functions efficiently. We also implement a video player called {\em FRVplayer}. With this prototype, we are able to judge that the visual quality of the previews and VCR-style operations is excellent. These features are essential to many important applications. We discuss the application of FRVplayer in the design of a video management system, called VideoCenter. This system is intended for Internet applications such as digital video libraries.
42282919
Exploring Local Community Structures in Large Networks
In this paper, we extend the concept of degree from single vertex to sub-graph, and present a formal definition of module/community in a network based on this extension. A new locally optimized algorithm is designed to find the module for a given source vertex in a network. Our analysis shows that the complexity of this algorithm is O(K2d), where K is the number of vertices to be explored in the sub-graph and d is the average degree of the vertices in the sub-graph. Based on this algorithm, we implement a JAVA tool, MoNet, for exploring local community structures in large networks. Using this tool to analyze a co-purchase network from Amazon shows that there are local community structures in this network. Further analyses on these local community structures demonstrate that media items are much easier to form compact local modules than book items do, indicating that recommending digital media items to customers based on co-purchasing information in the online store will be more efficient than recommending books.
2749844
Image and Video Segmentation by Anisotropic Kernel Mean Shift
Mean shift is a nonparametric estimator of density which has been applied to image and video segmentation. Traditional mean shift based segmentation uses a radially symmetric kernel to estimate local density, which is not optimal in view of the often structured nature of image and more particularly video data. In this paper we present an anisotropic kernel mean shift in which the shape, scale, and orientation of the kernels adapt to the local structure of the image or video. We decompose the anisotropic kernel to provide handles for modifying the segmentation based on simple heuristics. Experimental results show that the anisotropic kernel mean shift outperforms the original mean shift on image and video segmentation in the following aspects: 1) it gets better results on general images and video in a smoothness sense; 2) the segmented results are more consistent with human visual saliency; 3) the algorithm is robust to initial parameters.
766846
Application of neural networks to biological data mining: a case study in protein sequence classification
Biological data mining aims to extract signi cant information from DNA, RNA and proteins. The signi cant information may refer to motifs, functional sites, clustering and classi cation rules. This paper presents an example of biological data mining: the classi cation of protein sequences using neural netw orks. We proposenew tec hniques to extract features from protein data and use them in combination with the Ba yesianneural network to classify protein sequences obtained from the PIR protein database maintained at the National Biomedical Research Foundation. T o evaluate the performance of the proposed approach, we compare it with other protein classi ers built based on sequence alignment and machine learning methods. Experimental results sho w the high precision of the proposed classi er and the complementarity of the tools studied in the paper.
1305119
Minimizing roundoff errors of prefix sums via dynamic construction of Huffman trees
The prefix-sum operation, which returns all prefix sums on a sequence of numbers, plays an important role in many applications. We study how to efficiently evaluate prefix sums on positive floating-point numbers such that the worst-case roundoff error of each sum is minimized. A direct approach to this problem builds a Huffman tree for each prefix subsequence from scratch, requiring exactly quadratic time for every input X. We can do better by taking advantage of the current Huffman tree to build the next Huffman tree, using dynamic insertions and deletions on Huffman trees. Consequently, subquadratic time suffices for various input patterns. We also provide experimental comparisons of all the algorithms studied in this paper on inputs that are randomly and uniformly generated.
8535317
On cubic s-arc transitive Cayley graphs of finite simple groups
For a positive integer s, a graph Γ is called s-arc transitive if its full automorphism group AutΓ acts transitively on the set of s-arcs of Γ. Given a group G and a subset S of G with S = S-1 and 1 ∉ S, let Γ = Cay(G, S) be the Cayley graph of G with respect to S and GR the set of right translations of G on G. Then GR forms a regular subgroup of AutΓ. A Cayley graph Γ = Cay(G, S) is called normal if GR is normal in AutΓ. In this paper we investigate connected cubic s-arc transitive Cayley graphs Γ of finite non-Abelian simple groups. Based on Li's work (Ph.D. Thesis (1996)), we prove that either Γ is normal with s ≤ 2 or G = A47 with s = 5 and AutΓ ≃ A48. Further, a connected 5-arc transitive cubic Cayley graph of A47 is constructed.
19014339
Biological Data Mining Using Bayesian Neural Networks: A Case Study
Biological data mining is the activity of finding significant information in biomolecular data. The significant information may refer to motifs, clusters, genes, and protein signatures. This paper presents an example of biological data mining: the recognition of promoters in DNA. We propose a two-level ensemble of classifiers to recognize E. Coli promoter sequences. The first-level classifiers include three Bayesian neural networks that learn from three different feature sets. The outputs of the first-level classifiers are combined in the second-level to give the final result. Empirical study shows that a precision rate of 92.2% is achieved, indicating an excellent performance of the proposed approach.