206581379
An event and service interacting model and event detection based on the broker/service model
In distributed systems based on B/SM, events and event-condition-action rules are the fundamental metaphors for defining and enforcing the system. Processing entities execute behavior by reacting to and generating new events. It is important that event semantics is explicit. Although a lot of research has concentrated on event semantics, there still exists potential semantic ambiguity which is unacceptable in some circumstances. We propose an event model and incorporate an interaction group identifier into it. We emphasize that the semantics is a key to capture the actual meaning of events in real life, in which the same composite event patterns may imply totally different semantic meanings. Based on this observation, a graph-ESIG (Event and Service Interacting Graph) and ESIC (Event and Service Interacting Chain), which are constructed according to ECA-Rules, are proposed to trace events. ESIG greatly enhanced the expressive power to allow event detection correctly, which matches situations in reality. Finally, we develop a event detection algorithm that uses ESIG and ESIC.
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.
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
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.
26667538
Research on Service Management Data Modeling Based on SID
In order to resolve problems related to information integration in service management system (SMS), this paper introduces Meta Object Facility (MOF) into SMS to propose the concept of universal metamodel in service management domain. And then, Service Management Data Model (SMDM) is established to complete instantiation of metamodel by studying Share Information /Data (SID) Model. In this methodology, firstly, universal metamodel is established to ensure data sharing and reuse in SMS. Secondly, based on analysis of all kinds of information and process in SMS, core classes are defined to instantiate metadata classes and guarantee extendibility of SMDM. Each operational process can establish corresponding data model by definition of sub-class inheriting core class. Finally, data model of service quality monitoring is established to validate modeling methodology's validity. Therefore, consistency between SMDM and functional requirements is achieved and extension of business entities in SID is realized.
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.
7987221
Using VRML and JAVA to Build Virtual Game-Based Learning Environment for Addition and Subtraction Operation
How to make elementary mathematics education vivid and interesting? In this paper, we describe how to use VRML and JAVA to build a virtual game-based learning environment (VGBLE), in which children can learn addition and subtraction operation through games and hence children’s interesting in learning and effects will be improved.
15543221
A Research on Chaos Forecast with the Capital Market Structure Model
To study the process of pricing in capital market and look for a new way for decision support in financial market, according to the mechanism of artificial neural network, Capital Market Structure Model is developed in this case due to the influence between bargainer and price of asset in market. During proof-test, result of chaos forecast under the capital market structure model shows that chaos forecast capability of the Capital Market Structure Model is good for decision support. In financial market, the bargainer always concentrated in same place and the information spread quickly, meanwhile, bargainer and price are affected by each other. This means that the bargainer will influence the asset price in financial market when they trading. As we know, the decision of trading always after the changing of asset price in market. The new price also will influence the new decision of trading. So we can conclude that the bargainer and the price of asset in financial market are also influenced by each other continually. It is very important to study the principle of the influence before predict the tendency of asset price in financial market, because we need to know the process of price changing to support our decision. According to related papers in recent years, most of the researches about the asset forecast make use of the Artificial Neural Network (ANN). In the cases of positive research about forecast by using of the Artificial Neural Network, theories of chaos and fuzzy are apply to improve the data processing. In addition, wavelet theory is applies in forecast. According to the fundamental concepts of dynamical reconstruction theory and multi resolution analysis, YangYiwen,LiuGuizhong and ZhangZongping [1] take the reconstructed state vectors as multiple inputs to neural network to predict stock index trend of Shanghai Stock Exchange (SSE) in future multiple trading days. We can get high accuracy in imitating nonlinear curve through ANN, but we don’t know the relationship among input variables in ANN. So when ANN is applies to forecast in capital market, we can’t explain the process of pricing, which means we get the result without reason. The way of price forecast can’t explain the process of pricing and will not be variable in economics, and also will not make people know much more about the market. This is the key problem people faced in price forecast when ANN is applies to it. People can’t explain the meaning of weights and biases of ANN in real market. Until now, except researches of forecast by using ANN or wavelet theory directly, no research of forecast due to the principle of the influence among bargainers and prices in financial market has been reported. The purpose of this study is to fund a structure model of capital market due to principle of influence among bargainers and prices in capital market according to the idea by which ANN is founded. This structure model applies to forecast the future price in capital market. To insure the validity of forecast, chaos theory is applied to account the available length of non-periodic cycle of stock price.