34970822
Mining Frequent Patterns in an Arbitrary Sliding Window over Data Streams
This paper proposes a method for mining the frequent patterns in an arbitrary sliding window of data streams. As streams flow, the contents of which are captured with SWP-tree by scanning the stream only once, and the obsolete and infrequent patterns are deleted by periodically pruning the tree. To differentiate the patterns of recently generated transactions from those of historic transactions, a time decaying model is also applied. The experimental results show that the proposed method is efficient and scalable, and it is superior to other analogous algorithms.
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.
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.
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
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
43336842
Transfer, processing and distribution of cosmic ray data from Tibet
The YBJ-ARGO experiment uses cosmic ray data collected at the International Cosmic Ray Observatory in Tibet. Because of the observatory's location, transferring those data at a speed of 0.5 TB per day is challenging. Furthermore, the stream of raw data needs to be processed in a timely fashion before the resulting, reconstructed data are made available to the physicists-users. These processes demand large capacity, computing and storage resources. In this paper, we introduce the processes of transfer, processing and distribution of cosmic ray data from Tibet. A working demonstration was presented at the iGrid 2005 workshop.
37799288
On Building and Updating Distributed LSI for P2P Systems
Recently published studies have shown that Latent Semantic Indexing (LSI) plays an important role in content-based full text information retrieval of P2P system. However, it is a challenging problem to generate global consistent LSI structures in P2P systems because their nodes are self-organizing and their corpora are large, dynamic and distributed on different nodes. In this paper we propose a method for building LSI structures from distributed corpora. Our method is consisted with a network model for semantic information sampling and exchanging and a Reduced-Dimension-Representation (RDR)s merging algorithm. By the signal and noise subspace model, we also provide a theoretical justification that the RDR merging algorithm is sound. A simple numerical experiment shows that our RDR merging algorithm can keep query precision on an acceptable level.
17959493
A Fast Document Classification Algorithm Based on Improved KNN
A novel KNN classification algorithm combining model and evidence theory is proposed in this paper. The new method not only overcomes the main shortage of lazy learning in traditional KNN, but also takes the distances between samples to be recognized and samples in k-neighbors into account. At the same time the method resolves the unrecognizable cases of unknown samples. Applying the classification algorithm into the document recognition, experimental results show its satisfied recognition rate and fast categorization speed
6824407
Defect aware test patterns
A method to generate test patterns referred to as defect aware test patterns is proposed. Defect aware test patterns increase the ability to detect unmodeled defects. The proposed method can be used with any test generation procedure to improve the effectiveness of the tests in detecting unmodeled defects. Experimental results on several industrial designs show the effectiveness of defect aware tests. We also propose a measure to estimate the effectiveness of given test sets in detecting unmodeled defects.
11514235
Nonequilibrium electron and phonon transport and energy conversion in heterostructures
Abstract We establish a unified model dealing with the transport of electrons and phonons in double heterojunction structures with the coexistence of three nonequilibrium processes: (1) nonequilibrium among electrons, (2) nonequilibrium among phonons, and (3) nonequilibrium between electrons and phonons. Using this model, we investigate the energy conversion efficiency based on concurrent thermoelectric and thermionic effects on electrons and size effects on electrons and phonons. It is found that heterostructures can have an equivalent figure of merit higher than the corresponding bulk materials.