22227630
An Index Scheme for XML Documents Based on Relationship Joins
XML is rapidly emerging as a standard for information storage, representation and exchange on the Web. How to rapidly search and query XML documents efficiently has received many attentions in resent research. However, current querying schemes of XML documents typically involve in both node content and tree structural information, which may limit efficiency when facing the application that the tree structural information is more complicated than the tree node itself. In this paper, we propose the node relationships joins algorithms that utilize available indexes mainly on tree structural information. The relationships join algorithms work perfectly especially for searching paths that are very long or whose lengths are unknown. Experimental results from our prototype system implementation highlight the correctness and efficiency of our solution
8051159
A Web-based Framework For On-line Collaborative Learning
This paper presents a framework for design of online courses and collaborative learning based on Web technologies. It provides the following features: construction of structured learning materials in a distributed environment; personalization of course contents in the run-time that adapts to the progress of individual students; it can supply relevant Web resources to the students by analyzing the learning history dynamically; students in a group could exchange their learning experiences and discover relevant knowledge via the communication channels provided by the framework. The proposed techniques could be deployed to a variety of online computer-assisted modules to improve the effectiveness of Web-based e-learning
116588251
On-line evolutionary exponential family mixture
This paper deals with evolutionary clustering, which refers to the problem of clustering data with distribution drifting along time. Starting from a density estimation view to clustering problems, we propose two general on-line frameworks. In the first framework, i.e., historical data dependent (HDD), current model distribution is designed to approximate both current and historical data distributions. In the second framework, i.e., historical model dependent (HMD), current model distribution is designed to approximate both current data distribution and historical model distribution. Both frameworks are based on the general exponential family mixture (EFM) model. As a result, all conventional clustering algorithms based on EFMs can be extended to evolutionary setting under the two frameworks. Empirical results validate the two frameworks.
26669443
Bottom-up discovery of frequent rooted unordered subtrees
In the past decade, XML has emerged as the standard language for information exchanging over the Internet. Due to its tree-structure paradigm, XML is superior for its capability of storing, querying, and manipulating complex data. Therefore, discovering frequent tree patterns over tree-structured data has become an interesting topic for XML data management. In this paper, we propose a tree mining algorithm, named BUXMiner, for finding a special class of frequent trees, called rooted unordered trees, from a tree-structured database. BUXMiner employs an efficient bottom-up approach to enumerate all candidate trees over a compact global tree guide and computes the frequent trees based on the tree guide. In addition to BUXMiner, we also propose a mining approach called BUMXMiner to discover the maximal frequent rooted unordered trees. We compare BUXMiner with previous tree-structure mining algorithms, namely XQPMinerTID and FastXMiner, which were also proposed to discover rooted unordered trees. The experimental results show that our algorithm outperforms XQPMinerTID and FastXMiner in terms of efficiency. The performance results from real-world applications also indicate the usefulness of our proposed tree mining algorithms in a variety of web applications, such as analysis of web page access patterns and mining frequent XML query patterns for caching.
8340253
UGE4B: An Universal Grid Environment for Bioinformatics Research
Recent years, grid technology has been used widely in the fields of scientific research. In this paper, we present a universal grid environment for bioinformatics research (UGE4B). Although there have been several similar projects successfully constructed in the world, we need to meet some unique requirements from bioinformatics research in China. UGE4B consists of 6 components, including portal, task management, data management, information server, security and computing node. As a national-wide bioinformatics research platform, UGE4B is built through interconnecting multiple sub grids provided by each bioinformatics institutes. Besides provides a universal portal to end users for bioinformatics computing and data accessing, UGE4B also supported some special bioinformatics applications
5747087
Efficient Processing of Warping Time Series Join of Motion Capture Data
Discovering non-trivial matching subsequences from two time series is very useful in synthesizing novel time series. This can be applied to applications such as motion synthesis where smooth and natural motion sequences are often required to be generated from existing motion sequences. We first address this problem by defining it as a problem of l-e-join over two time series. Given two time series, the goal of l-e-join is to find those non-trivial matching subsequences by detecting maximal l-connections from the e-matching matrix of the two time series. Given a querying motion sequence, the l-e-join can be applied to retrieve all connectable motion sequences from a database of motion sequences. To support efficient l-e-join of time series, we propose a two-step filter-and-refine algorithm, called Warping Time Series Join (WTSJ) algorithm. The filtering step serves to prune those sparse regions of the e-matching matrix where there are no maximal l-connections without incurring costly computation. The refinement step serves to detect closed l-connections within regions that cannot be pruned by the filtering step. To speed up the computation of e-matching matrix, we propose a block-based time series summarization method, based on which the block-wise e-matching matrix is first computed. Lots of pairwise distance computation of elements can then be avoided by applying the filtering algorithm on the block-wise e-matching matrix.Extensive experiments on l-e-join of motion capture sequences are conducted. The results confirm the efficiency and effectiveness of our proposed algorithm in processing l-e-join of motion capture time series.
7031383
Image retrieval based on Sugeno fuzzy integral
In this paper, we propose a fuzzy similarity measure based on the Sugeno fuzzy integral and apply it to the content-based image retrieval. Fuzzy measure is used to formulate the subjective feedback information. Experimental results show that the proposed method can improve the efficiency and reliability of image retrieval system. Our method of formulating the subjective feedback information performs better than the weighted average operator (WAO) and a Choquet-integral-based similarity measure.
15177546
Semantic enhanced rule driven workflow execution in Collaborative Virtual Enterprise
Globalization leads to an efficient new business paradigm generally known as collaborative virtual enterprise (CVE) which demands flexible service orchestration and robust workflow execution. As two major service orchestration strategies, OWL-S and BPEL have their own strength and deficiencies. To meet the challenge in CVE, in this paper, we propose a workflow execution system (WES) that takes advantage of the complementary strengths of these two technologies. On the one hand, having semantic support, OWL-S is used in dynamic service discovery and composition at high level. On the other hand, at the concrete level, industry-based BPEL is exploited in service execution. The description of each semantic Web service is enhanced with rule-based modeling of the essential business logic behind the service interface. In order to realize interoperability in OWL-S and BPEL without loss of semantic information, we further proposed an OWLS2BPEL Mapper to facilitate the workflow robustness and support rule evaluation to increase responsiveness to customers. A concrete scenario in PC manufacturing collaborative virtual enterprise is analyzed to demonstrate the effectiveness of our workflow execution system.
16819932
A Client Peer Adjustment Policy for Peer-to-Peer Media Streaming
Previous work indicates that more cooperation is needed between peers for peer-to-peer (P2P) media streaming application than files sharing. That implies it is important to select cooperators in P2P networks. However, most of peer selection algorithms are performed at requesting ones and focus on how to choose servers from which clients can benefit more while ignoring whether the scalability and service capacity of systems will be influenced by these results. In this paper, we pay more attention to heterogeneous network capacity of peers and propose a policy fulfilled by server peers to select and adjust clients according to their service capacity and contribution. Peers with higher service capacity may replace existing children. And experiments proved that our policy could improve system's overall utilization and contribution, as well as the depth of multicast tree when compared with delay-based algorithm
39222179
A New ART Neural Networks for Remote Sensing Image Classification
A new ART2A-C algorithm based on fuzzy operators to cluster the remote sensing images and aerials is proposed in this paper. By combining two ART ANNs with higher performance, the traditional ART2A-C is developed with the fuzzy operators introduced in matching rule. Then the proposed method is applied to the classification and the new network is implemented as well as two other existed ARTs respectively. Experimental results show that the new method outperforms the traditional ones.
17872919
Extremal search of decision policies for scalable distributed applications
The ongoing trend of constructing open, scalable distributed systems such as peer-to-peer (P2P) systems demands for effective tools to manage the interactions between constituent entities (or nodes). One such tool is through imposing decision policies at the network level. However very few techniques are available to allow computers autonomously identify good policies with limited human intervention. In this paper, we propose an Extremal Programming (EP) algorithm to achieve automatic policy identification. The algorithm is inspired by recent advances in understanding far from equilibrium phenomena in terms of self-organized criticality (SOC). The effectiveness of EP is evaluated through a P2P application called location-aware video streaming (LAVS). The simulation studies in LAVS demonstrate that with EP, the fast and effective sharing of video streams is achieved.