12807394
A novel data caching scheme for multimedia servers
Abstract In this paper, we propose a Bi-directional Fragmental Pipelining (BFP) technique and its variable buffer size data-caching scheme BFPV to reduce the disk I/O bandwidth requirement for multimedia servers. Our mathematical analysis shows that the BFP technique is superior to the traditional unidirectional pipelining technique in terms of memory buffer space requirement. We further demonstrate that the memory buffer management using BFPV is better than that of using the fixed buffer size approach BFPF. We have mathematically proved that BFPV saves more disk I/O bandwidth than BFPF does using the same memory buffer space. Our simulation results have quantitatively confirmed our analysis.
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.
5981810
An efficient ontology comparison tool for semantic Web applications
With the growing access to heterogeneous and independent data repositories, determining the semantic difference of two ontologies is critical in information retrieval, information integration and semantic web applications. In this paper, we propose an ontology comparison tool based on a novel senses refinement algorithm, which builds a senses set to accurately represent the semantics of the input ontology. The senses refinement algorithm automatically extracts senses from the electronic lexical database WordNet (locally installed or online), removes unnecessary senses based on the relationship among the entity classes of the ontology, and specifies relations and constraints of the concepts in the refined senses set. The senses refinement converts the measurement of ontology difference into simple set operations based on set theory, thus ensures the efficiency and accuracy of the ontology comparison. Our experimental studies show that the proposed senses refinement algorithm outperforms the naive senses set construction algorithm in terms of efficiency and accuracy.
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.
54104798
Data allocation algorithms for distributed video servers
1151748
Scalable and Efficient Data Streaming Algorithms for Detecting Common Content in Internet Traffic
Recent research on data streaming algorithms has provided powerful tools to efficiently monitor various characteristics of traffic passing through a single network link or node. However, it is often desirable to perform data streaming analysis on the traffic aggregated over hundreds or even thousands of links/nodes, which will provide network operators with a holistic view of the network operation. Shipping raw traffic data to a centralized location (i.e., “raw aggregation”) for streaming analysis is clearly not a feasible approach for a large network. In this paper, we propose a set of novel distributed data streaming algorithms that allow scalable and efficient monitoring of aggregated traffic without the need for raw aggregation. Our algorithms target the specific network monitoring problem of finding common content in the Internet traffic traversing several nodes/links, which has applications in network-wide intrusion detection, early warning for fast propagating worms, and detection of hot objects and spam traffic. We evaluate our algorithms through extensive simulations and experiments on traffic traces collected from a tier-1 ISP. The experimental results demonstrate that our algorithms can effectively detect common content in the traffic traversing across a large network.
2808817
BGP routing stability of popular destinations
The Border Gateway Protocol (BGP) plays a crucial role in the delivery of traffic in the Internet. Fluctuations in BGP routes cause degradation in user performance, increased processing load on routers, and changes in the distribution of traffic load over the network. Although earlier studies have raised concern that BGP routes change quite often, previous work has not considered whether these routing fluctuations affect a significant portion of the traffic. This paper shows that the small number of popular destinations responsible for the bulk of Internet traffic have remarkably stable BGP routes. The vast majority of BGP instability stems from a small number of unpopular destinations. We draw these conclusions from a joint analysis of BGP update messages and flow-level traffic measurements from AT&T's IP backbone. In addition, we analyze the routing stability of destination prefixes corresponding to the NetRating's list of popular Web sites using the update messages collected by the RouteViews and RIPE-NCC servers. Our results suggest that operators can engineer their networks under the assumption that the BGP advertisements associated with most of the traffic are reasonably stable.
1269749
Complexity-adaptive search algorithm for block motion estimation
We propose a complexity-adaptive fast block matching algorithm design strategy that allows the user to terminate the algorithm at any target computational complexity. Two complexity-adaptive techniques, frame level complexity allocation and block level complexity allocation, are developed to approach the global complexity-distortion optimization A buffer control strategy is proposed to dynamically adjust the target complexity of one frame. The predictive complexity-distortion benefit list (PCDB list) technique is employed to allocate the target complexity into each block. By these techniques, we modify the full-search block matching, three-step search, new three-step search, and four-step search to the complexity-adaptive algorithms. Experimental results show that the complexity-adaptive algorithms could achieve better performance than traditional fixed algorithms from the viewpoint of complexity-distoftion optimization.
12513185
Classifying Objectionable Websites Based on Image Content
This paper describes IBCOW (Image-based Classification of Objectionable Websites), a system capable of classifying a website as objectionable or benign based on image content. The system uses WIPEℳ (Wavelet Image Pornography Elimination) and statistics to provide robust classification of on-line objectionable World Wide Web sites. Semantically-meaningful feature vector matching is carried out so that comparisons between a given on-line image and images marked as ”objectionable” and ”benign” in a training set can be performed efficiently and effectively in the WIPE module. If more than a certain number of images sampled from a site is found to be objectionable, then the site is considered to be objectionable. The statistical analysis for determining the size of the image sample and the threshold number of objectionable images is given in this paper. The system is practical for real-world applications, classifying a Web site at a speed of less than 2 minutes each, including the time to compute the feature vector for the images downloaded from the site, on a Pentium Pro PC. Besides its exceptional speed, it has demonstrated 97% sensitivity and 97% specificity in classifying a Web site based solely on images. Both the sensitivity and the specificity in real-world applications is expected to be higher because our performance evaluation is relatively conservative and surrounding text can be used to assist the classification process.
12107864
Kernel Trick Embedded Gaussian Mixture Model
In this paper, we present a kernel trick embedded Gaussian Mixture Model (GMM), called kernel GMM. The basic idea is to embed kernel trick into EM algorithm and deduce a parameter estimation algorithm for GMM in feature space. Kernel GMM could be viewed as a Bayesian Kernel Method. Compared with most classical kernel methods, the proposed method can solve problems in probabilistic framework. Moreover, it can tackle nonlinear problems better than the traditional GMM. To avoid great computational cost problem existing in most kernel methods upon large scale data set, we also employ a Monte Carlo sampling technique to speed up kernel GMM so that it is more practical and efficient. Experimental results on synthetic and real-world data set demonstrate that the proposed approach has satisfing performance.