1502133
Accelerating Parent-Child Path Matching in XML
With the rapidly increasing popularity of XML as a data format, there is a large demand for efficient XML structural matching techniques. Normally, data in XML are stored in a tree-like structure where nodes (with data) are located using the path relations in the tree. This paper proposes a coding policy named path code to accelerate an important category of structure matching in XML, namely the Parent-Child paths. A Parent-Child path is the path expression that contains only parent-child relationships. The proposed path code employs a partial prefix Path of XML elements using a special compression technique. Based on the path code, we present a Boosting algorithm, which has low-linear time complexity and little I/O cost, to match Parent-Child paths. In addition, we propose two heuristic policies to optimize the Boosting algorithm further by reducing the memory consumption. Our experiments show that the Boosting algorithm considerably outperforms previous algorithms in run-time Parent-Child path matching.
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.
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.
1721891
Lempel–Ziv Factorization Using Less Time & Space
Abstract.For 30 years the Lempel–Ziv factorization LZx of a string x = x[1..n] has been a fundamental data structure of string processing, especially valuable for string compression and for computing all the repetitions (runs) in x. Traditionally the standard method for computing LZx was based on Θ(n)-time (or, depending on the measure used, O(n log n)-time) processing of the suffix tree STx of x. Recently Abouelhoda et al. proposed an efficient Lempel–Ziv factorization algorithm based on an “enhanced” suffix array – that is, a suffix array SAx together with supporting data structures, principally an “interval tree”. In this paper we introduce a collection of fast space-efficient algorithms for LZ factorization, also based on suffix arrays, that in theory as well as in many practical circumstances are superior to those previously proposed; one family out of this collection achieves true Θ(n)-time alphabet-independent processing in the worst case by avoiding tree structures altogether.
16911875
Study on Fast Fixed-Codebook Search Algorithm in G.729 Speech Coding
G.729 is called Conjugate Structured-Algebraic Code Excited Linear Prediction (CS-ACELP), its fixed-codebook adopts algebraic- codebook. The algebraic structure of the codebook allows for a fast search procedure, but it still requires large computational load using the nested-loop search method. The focused search used in G.729 and the depth-first tree search used in G.729a can reduce the searching complexity efficiently. However it still occupies a larger proportion of whole encoding complexity. In this paper, we study various methods and propose a fast Codebook Search method, it can largely reduce the complexity, and speech still maintain at almost the same quality.
15526741
A Novel Liver Perfusion Analysis Based on Active Contours and Chamfer Matching
Liver Perfusion gives important information about blood supply of liver. However, in daily clinical diagnosis, radiologists have to manually mark the perfusion position in time-sequence images due to the motion of liver caused by respiration. In this paper, we propose a novel hybrid method using a variation of active contours and modified chamfer matching to automatically detect the liver perfusion position and measure the intensity with a single shape prior. The experiment is taken on abdomen MRI series and the result reveals that after extracting liver’s rough boundary by active contours, precise perfusion positions can be detected by the modified chamfer matching algorithm, and finally a refined intensity curve without respiration affection can be achieved.
15892947
Torsional Pile Subjected to Transient Loading in Viscoelastic Poroelastic Medium
Considering viscoelastic saturated soil, The transient dynamic response of an elastic pile is studied. The pile-soil system is divided into thin layers, the control equations of the soil are solved respectively by using Laplace transform. Considering the mixed boundary-value conditions at the interface of pile and soil, expression is derived to describe the relationship between the inner force and the displacement of the pile segment. Then the expressions of all pile segments are integrated to form the stiffness matrix of pile. Considering the transient torsional loading at the pile head, the pile displacement in time domain is calculated by numerical method, with the result showing the influence of various pile parameters.
42691815
Global stability of a class of Cohen-Grossberg neural networks with delays
This paper is concerned with the Global Asymptotic Stability (GAS) of a general class of Cohen-Grossberg neural networks with both multiple time varying delays and distributed delays. Criteria are established to ensure the GAS of the concerned neural networks, which can be expressed in the form of Linear Matrix Inequality and independent of amplification functions. Furthermore, a sufficient condition guaranteeing the global robust stability is established for the general class of Cohen-Grossberg neural networks with both multiple time varying delays and distributed delays in the case of parameter uncertainties.