5919440
MIME: A Dynamic Index Scheme for Multi-dimensional Query in Mobile P2P Networks
Nowadays, as the mobile services become widely used, there is a strong demand for mobile support in P2P search techniques. In this paper, we introduce a new cost model for searching multi-dimensional data in mobile P2P environment and propose a novel multi-dimensional MP2P search scheme MIME. MIME models the physical node layout in a two-dimensional plane and keeps records of the locations of the nodes to construct a proximity-aware P2P overlay. MIME also incorporates two adaptive features: an update algorithm that makes dynamic updates to the overlay, and a cache mechanism that reduces the load of data migration during the updates. Experiment results show that MIME achieves significant performance improvements in point/range queries compared to the conventional system.
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.
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
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
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
15925751
SpMT WaveCache: Exploiting Thread-Level Parallelism in WaveScalar
Speculative Multithreading (SpMT) increases the performance by means of executing multiple threads speculatively to exploit thread-level parallelism. By combining software and hardware approaches, we have improved the capabilities of previous WaveScalar ISA on the basis of Transactional Memory system for the WaveCache Architecture. Threads are extracted at the course of static compiling, and speculatively executed as a thread-level transaction that is supported by extra hardware components, such as Thread-Context-Table (TCT) and Thread-Memory-History (TMH). We have evaluated the SpMT WaveCache with 6 real benchmarks from SPEC, Mediabench and Mibench. On the whole, the SpMT WaveCache outperforms superscalar architecture ranging from 2X to 3X, and great performance gains are achieved over original WaveCache and Transactional WaveCache as well.
2378122
Self-organized manufacturing resource management: An ant-colony inspired approach
In a typical manufacturing system, jobs are released from a production planning stage to a shop floor, where they are allocated to resources like machines. An optimal or near optimal schedule is generally found for those jobs. In reality, the execution of this schedule is often interrupted by dynamic events like unexpected incoming new jobs, machine breakdowns, etc. A rapid recovery or a self-organized feature of the interrupted schedule is desirable for manufacturing resource management. This paper proposes an ant-colony inspired approach to recover the interrupted schedule through a self-organization mechanism. The experiments show that this mechanism significantly improves the quality of the recovered schedules.
13807032
Coordinating Agents in Shop Floor Environments From a Dynamic Systems Perspective
In this paper, we address agent coordination from a dynamic systems perspective and propose a dynamic coordination model, which is inspired by biological metabolic systems. A new coordination mechanism through dynamic local adjustment (CDLA) is presented, and coordination is achieved when every agent utilizes explicitly the global system dynamics and performs iteratively a dynamic local adjustment procedure. The CDLA mechanism is investigated in an example multiagent shop floor system. The results show that the example manufacturing process is well-coordinated and the coordination approach is practically applicable and effective
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.