40834787
Petri-net-based context-related access control in workflow environment
Access control, which is one of the most important aspects of security services, is becoming increasingly an important challenge in workflow management systems. However, the traditional models of access control, such as the role-based access control (RBAC), are static models without recognition of the dynamic environments in which the workflows are rooted. These context-independent models rarely can meet the requirements that the highly dynamic environments raise. In this paper, we regard authorization as a dynamic process instead of the traditional static view and take two contextual factors into account: (1) the state of related authorization processes; and (2) the state of related process instances. Then, we propose a Petri-net-based model as a practical solution to the context-sensitive access control on this basis.
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
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.
8202363
Coordinated Services Provision in Peer-to-Peer Environments
In recent years, inspired by the emerging Web services standard and peer-to-peer technology, a new federated service providing (FSP) system paradigm has attracted increasing research interests. Many existing systems have either explicitly or implicitly followed this paradigm. Instead of exchanging files, peers in FSP systems share their computation resources in order to offer domain-specific services. In this paper, we focused on the coordination problem of how to self-organize the service group structures in response to the varying service demand. We presented our solution in the form of a coordination mechanism, which includes a labor-market model, a recruiting protocol, and a policy-driven decision architecture. Peers make their service providing decisions based on their local policies, which can be added, removed, or modified by users. A general methodology is introduced in this paper to facilitate policy design. Specifically, a heuristic inspired by the extremal optimization technique is utilized to handle potential inconsistencies among policies. A stimulus-response mechanism was further applied to make the decision process adjustable. Experiments under five application scenarios verified our ideas and demonstrated the effectiveness of our coordination mechanism.
33950497
An Effective Method for Chinese Grammar Rule Learning
A new method of Chinese grammar rules learning is put forward in this paper. The key point of this method is to use part-of-speech (POS), semantic and contextual information together in learning and expressing Chinese grammar rules. In this way, not only Context Free Grammar (CFG) rules can be learnt, but also the ambiguous structures in POS can be identified automatically. Furthermore, non-ambiguous semantic rules and forbidden rules can be produced from ambiguous rules by using semantic and contextual information. Experimental results demonstrate that the complexity of Chinese parsing is greatly reduced using the different rules learnt by our method.
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.
15177546
Semantic enhanced rule driven workflow execution in Collaborative Virtual Enterprise
Globalization leads to an efficient new business paradigm generally known as collaborative virtual enterprise (CVE) which demands flexible service orchestration and robust workflow execution. As two major service orchestration strategies, OWL-S and BPEL have their own strength and deficiencies. To meet the challenge in CVE, in this paper, we propose a workflow execution system (WES) that takes advantage of the complementary strengths of these two technologies. On the one hand, having semantic support, OWL-S is used in dynamic service discovery and composition at high level. On the other hand, at the concrete level, industry-based BPEL is exploited in service execution. The description of each semantic Web service is enhanced with rule-based modeling of the essential business logic behind the service interface. In order to realize interoperability in OWL-S and BPEL without loss of semantic information, we further proposed an OWLS2BPEL Mapper to facilitate the workflow robustness and support rule evaluation to increase responsiveness to customers. A concrete scenario in PC manufacturing collaborative virtual enterprise is analyzed to demonstrate the effectiveness of our workflow execution system.
37291002
Subdivision Depth Computation for Extra-Ordinary Catmull-Clark Subdivision Surface Patches
A second order forward differences based subdivision depth computation technique for extra-ordinary Catmull-Clark subdivision surface (CCSS) patches is presented. The new technique improves a previous technique in that the computation of the subdivision depth is based on the patch's curvature distribution, instead of its dimension. Hence, with the new technique, no excessive subdivision is needed for extra-ordinary CCSS patches to meet the precision requirement and, consequently, one can make trimming, finite element mesh generation, boolean operations, and tessellation of CCSSs more efficient.