16003770
A Caching System for XML Queries Using Frequent Query Patterns
In this paper, we present an optimization framework for querying XML data by caching frequent query patterns. In this framework, frequent query patterns are mined online from user queries, and these query results are cached for future use. The mining process of frequent query patterns is launched automatically when user queries meet predefined requirements. To deal with queries that are similar to but not exactly same as the cached ones, a novel technique named query rewriting is adopted. This technique is able to handle four kinds of similar queries namely exact matching, exact containment, semantic matching and semantic containment. A cache replacement scheme that utilizes both the query pattern support and query pattern accessing time is employed to perform a fine-grained region purging. Experiments are carried out on the XMARK datasets. The results show that the proposed methods are both effective and efficient in improving the performance of XML queries.
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.
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.
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.
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
295516
A fuzzy metric for image quality assessment
Image quality assessment is an important issue addressed in various image processing applications such as image/video compression and image reconstruction. The peak signal-to-noise ratio (PSNR) with the L/sup 2/-metric is commonly used in objective image quality assessment. However, the measure does not agree very well with human visual perception in many cases. In this paper, a fuzzy image metric (FIM) is defined based on M. Sugeno's (1974) fuzzy integral. This new objective image metric, which is to some extent a proper evaluation from the viewpoint of the judgement procedure, is closely approximates the subjective mean opinion score (MOS) with a correlation coefficient of about 0.94, as compared to 0.82 obtained using PSNR.
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.
11514442
A test pattern ordering algorithm for diagnosis with truncated fail data
In this paper, we propose a test pattern ordering algorithm for fault diagnosis. Test pattern ordering is effective in situations where the fail log is truncated and contains a limited number of fail data. In such cases, higher diagnostic resolution can be achieved with the test set appropriately ordered. Test pattern ordering is independent of the diagnosis algorithm used. The higher resolution achieved by test pattern ordering is obtained at no additional cost once the test patterns have been appropriately ordered. Experimental results on two industrial designs are presented to demonstrate the effectiveness of the proposed method
17195732
Tactical Communication Equipment Simulation Training Platform Based on VR
A tactical communication equipment simulation training platform aiming at the difficult problem of training is presented. Firstly, platform is built based on HLA and its framework is designed to a hierarchical structure. Secondly, models of the training equipments are created with software of GL Studio and Multigen Creator. The key technology of the platform including single equipment 3D modeling and integrated equipment 3D modeling is detailed. Each step is also listed and some skills are told combined with examples. Finally, the flow named military requirement drive designing and developing is put forward considering the traits of the platform, which is adopted to implement the platform. The simulation examples are introduced to prove the feasibility of the platform. The development of the platform provides beneficial reference for constructing the similar large-scale equipment operation simulation training system.
8846257
A Noise Insensitive Cluster Validity Measure for Pattern Classification
When trying to discover knowledge on a collection of data, one of the first arising tasks is to identify groups of similar objects, that is, to carry out cluster analysis for obtaining data partitions. Thus, a decision must be taken for choosing the clustering result that produces the best data partition for a given data collection. In order to support such a decision, indexes for measuring the quality of a data partitioning must be constructed. So far, several cluster validity indexes have been formulated in the literatures. Each of those indexes has strengths and drawbacks when compared with the others. In the present study, an alternative cluster validity index is formulated. The proposed validity index not only takes the contribution of each pattern into consideration, but also relies on information of intra-cluster and inter-cluster distance. The main advantage of the presented index is that is insensitive to noise by introducing the Gaussian kernel into the proposed validity index. An experimental design was devised in order to determine the comparative performance of the proposed cluster validity index against DB index previously formulated in the literature. Experimental results show that the proposed index is insensitive to noise and adaptive to produce good clustering solution.