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.
54104798
Data allocation algorithms for distributed video servers
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.
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.
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.
1791876
A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval
This paper proposes a fuzzy logic approach, UFM (unified feature matching), for region-based image retrieval. In our retrieval system, an image is represented by a set of segmented regions, each of which is characterized by a fuzzy feature (fuzzy set) reflecting color, texture, and shape properties. As a result, an image is associated with a family of fuzzy features corresponding to regions. Fuzzy features naturally characterize the gradual transition between regions (blurry boundaries) within an image and incorporate the segmentation-related uncertainties into the retrieval algorithm. The resemblance of two images is then defined as the overall similarity between two families of fuzzy features and quantified by a similarity measure, UFM measure, which integrates properties of all the regions in the images. Compared with similarity measures based on individual regions and on all regions with crisp-valued feature representations, the UFM measure greatly reduces the influence of inaccurate segmentation and provides a very intuitive quantification. The UFM has been implemented as a part of our experimental SIMPLIcity image retrieval system. The performance of the system is illustrated using examples from an image database of about 60,000 general-purpose images.
2380687
Packet classification using multidimensional cutting
This paper introduces a classification algorithm called phHyperCuts. Like the previously best known algorithm, HiCuts, HyperCuts is based on a decision tree structure. Unlike HiCuts, however, in which each node in the decision tree represents a hyperplane, each node in the HyperCuts decision tree represents a k--dimensional hypercube. Using this extra degree of freedom and a new set of heuristics to find optimal hypercubes for a given amount of storage, HyperCuts can provide an order of magnitude improvement over existing classification algorithms. HyperCuts uses 2 to 10 times less memory than HiCuts optimized for memory, while the worst case search time of HyperCuts is 50--500% better than that of HiCuts optimized for speed. Compared with another recent scheme, EGT-PC, HyperCuts uses 1.8--7 times less memory space while the worst case search time is up to 5 times smaller. More importantly, unlike EGT-PC, HyperCuts can be fully pipelined to provide one classification result every packet arrival time, and also allows fast updates.
39144227
Cursive word reference line detection
This paper proposes a novel method in detecting the four reference lines of a cursive word at any skew angle. The proposed method is rotation independent because the detection is based on points determined by their relative positions between the outer contour of a cursive word and its convex hull. Features like bounding box, contour area, convex hull and furthest points are used in detecting and grouping these points. The reference lines are detected by searching for suitable peaks in the Hough transformation space of these points. Encouraging experimental results have been obtained.
16046090
Nonlinear blind source separation using higher order statistics and a genetic algorithm
This paper presents a novel method for blindly separating unobservable independent source signals from their nonlinear mixtures. The demixing system is modeled using a parameterized neural network whose parameters can be determined under the criterion of independence of its outputs. Two cost functions based on higher order statistics are established to measure the statistical dependence of the outputs of the demixing system. The proposed method utilizes a genetic algorithm (GA) to minimize the highly nonlinear and nonconvex cost functions. The GA-based global optimization technique is able to obtain superior separation solutions to the nonlinear blind separation problem from any random initial values. Compared to conventional gradient-based approaches, the GA-based approach for blind source separation is characterized by high accuracy, robustness, and convergence rate. In particular, it is very suitable for the case of limited available data. Simulation results are discussed to demonstrate that the proposed GA-based approach is capable of separating independent sources from their nonlinear mixtures generated by a parametric separation model.
21957858
Algorithms for Approximate Graph Matching
Abstract Labeled graphs are graphs in which each node and edge has a label. The distance between two labeled graphs is considered to be the weighted sum of the costs of edit operations (insert, delete, and relabel the nodes and edges) to transform one graph to the other. The paper considers two variants of the approximate graph matching problem (AGM): Given a pattern graph P and a data graph D : 1. 1. What is the distance between P and D ? 2. 2. What is the minimum distance between P and D when subgraphs can be freely removed from D ? We first show that no efficient algorithm can solve either variant of the AGM unless P = NP. Then we present several heuristic algorithms leading to approximate solutions. The heuristics are based on probabilistic hill climbing and maximum flow techniques. Our experimental results involving the comparison of real and simulated data demonstrate the good performance of the algorithms presented.