14012459
MSFSS: A Storage System for Mass Small Files
We designed and implemented MSFSS, a scalable and flexible distributed file system for storage and retrieval of mass small files. MSFSS is a platform built upon the existing commodity file systems. It automatically stores files onto the best-fit commodity file systems according their access patterns. To avoid central bottleneck, it optimizes metadata size, separates metadata operations from file data transfer, and implements batch metadata operations. The system provides data migration, hot file caching, and replication, which are essential for large scale, reliable storage systems. It has successfully been deployed as storage system for our Web application which has about 50 TB of small files. Experimental results show that MSFSS provides high scalability and throughput in file operation services.
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
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
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.
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.
22493357
Procedures for identifying untestable and redundant transition faults in synchronous sequential circuits
Due to their simplicity transition faults are often used as targets for test generation to detect delay defects. However, one concern documented in the literature is that of overtesting. One of the reasons for overtesting is that DFT approaches, such as scan, change sequentially untestable faults into testable faults. One approach to reducing overtesting is to identify sequentially untestable and redundant faults and not target them during test generation for the circuit with scan. Another application of identifying untestable transition faults is its use in logic optimization. We investigate efficient procedures to identify untestable and redundant transition faults in nonscan synchronous sequential circuits. Experimental results for ISCAS-89 benchmark circuits are presented.
15486362
Simulation Training Platform for Large Tactical Communication Equipment Based on VR
A simulation training platform aiming at the difficult problem of training is presented. Platform is built based on HLA and its framework is designed to hierarchical structure. Models of training equipment are created with software named GL Studio and Multigen Creator. Key technologies of modeling process are detailed. Equipment operations are layered and its rules are described with XML document. Driving framework programming with Vega Prime API can integrate 3D models, operation rules, special effect and whole scene to a platform system based on VR. Finally, plug-in soft framework is adopted to implement the system and simulation examples are introduced to prove the feasibility of the platform.
29726375
Collaborative Education Model and Its Application in E-learning
This paper presents a collaborative education model and its application in e-learning. This work is distinguished by three contributions. One is the proposition of three goals of e-learning in "source, transmit and quality" views. The second one is the proposition of collaborative education model including model of studying process, model of studying environment and six collaborative scenarios. The last one is the proposition of a collaborative e-learning implementation architecture.
15935457
A Design of Data Rebuilding for Decision Support in E-Government System
The E-Government System is composed of three networks and one database. The business network within government offices (the Internal Net), the resource network in central and local administrations and departments (the External Net), the public information network (PIN) relying on the Internet. It is necessary to rebuild data in the three networks of the system in order to construct an effective electronic information database. In data rebuilding, a data exchange platform is set up to enable data sharing between the three networks. The data exchange platform is implemented in a structure that combines the intelligent decision support system (IDSS) and data warehouse (DW).
1048322
Soundness of Coercion in the Calculus of Constructions
A coercive subtyping system for the Calculus of Constructions is presented. The proposed system λC≤co is obtained essentially by adding coercions and η-conversion to λC≤, which is a subtyping extension to the Calculus of Constructions without coercions. We proved that a coercion c:A ≤ B is semantically sound. That is, (1) c is a term of type A → B; (2) c behaves like an identity function. This development follows the spirit of semantical interpretation of subtyping.