Browsing by Author "Tseng, Shian-Shyong"

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A Collaborative Approach for Minimal-Cost Monitor Deployment in Cloud Environment
(2015-08-01)
Tung, Yuan-Hsin
;
Tseng, Shian-Shyong
;
Tsai, Wei-Tek
;
資訊工程學系
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Department of Computer Science
Monitoring is widely applied in problem diagnosis, fault localization, and system maintenance. And since the cloud infrastructure is complex, the applications on the cloud are therefore complex, which makes monitoring in cloud more difficult. Rich monitors that contain composite and heterogeneous probes are often used in service-oriented system monitoring. These rich monitors often involve multiple entities, and the interpretation may require expert opinions from multiple domains. This paper proposes a knowledge-based collaborative monitoring approach to find out minimal cost monitor deployment in a cloud environment. The approach contains two main phases. In the knowledge acquisition phase, three acquisition tables, monitor-probe relationship matrix, cost of monitoring, and probe-problem dependence matrix, are generated according to diagnosis ontology and monitor ontology acquired from domain experts. And then based upon the three acquisition tables and three consensus building strategies, we formulate the problem of optimizing the cost of monitoring as an Integer Linear Programming (ILP) problem, which is NP-Complete. In the monitor deployment phase, the proposed algorithm applies two heuristic rules to address the problem. Three experiments are conducted to evaluate the performance of the proposed approach. The results from the experiments show that our approach is effective and produce quality approximate solutions in monitor deployment.
A Collaborative Approach for Minimal-Cost Monitor Deployment in Cloud Environment
(2015-08)
Tung, Yuan-Hsin
;
Tseng, Shian-Shyong
;
Tsai, Wei-Tek
;
資訊工程學系
;
Department of Computer Science
Monitoring is widely applied in problem diagnosis, fault localization, and system maintenance. And since the cloud infrastructure is complex, the applications on the cloud are therefore complex, which makes monitoring in cloud more difficult. Rich monitors that contain composite and heterogeneous probes are often used in service-oriented system monitoring. These rich monitors often involve multiple entities, and the interpretation may require expert opinions from multiple domains. This paper proposes a knowledge-based collaborative monitoring approach to find out minimal cost monitor deployment in a cloud environment. The approach contains two main phases. In the knowledge acquisition phase, three acquisition tables, monitor-probe relationship matrix, cost of monitoring, and probe-problem dependence matrix, are generated according to diagnosis ontology and monitor ontology acquired from domain experts. And then based upon the three acquisition tables and three consensus building strategies, we formulate the problem of optimizing the cost of monitoring as an Integer Linear Programming (ILP) problem, which is NP-Complete. In the monitor deployment phase, the proposed algorithm applies two heuristic rules to address the problem. Three experiments are conducted to evaluate the performance of the proposed approach. The results from the experiments show that our approach is effective and produce quality approximate solutions in monitor deployment.
A Cost-effective Approach to evaluating Security Vulnerability Scanner
(2013)
Tung, Yuan-Hsin
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Tseng, Shian-Shyong
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Shih, Jen-Feng
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Shan, Hwai-Ling
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資訊工程學系
Web applications are exposed to various threats and attacks, and therefore numerous tools are developed for detecting web application vulnerabilities. Many studies have focused on evaluating vulnerability scanners. An efficient evaluation approach for detection tools is essential and can be extremely helpful to the users. In this paper, we propose a cost-effective approach to evaluating vulnerability scanners by considering redundant vulnerability alert problem. We define the redundant alert problem in scanner evaluation with our motivational example and propose the advanced confusion matrix by extending two defined attributes, true duplication (TD) and false duplication (FD). Then we apply our proposed cost-effective evaluation approach and build up the web Vulnerability Scanner Testbed.
A CROSS-TEXT ANNOTATION SHARING MECHANISM FOR ENHANCING STUDENTS' COMPREHENSION OF POETRY
(2012-10-01)
Yang, Che-Ching
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Tseng, Shian-Shyong
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Shih, Wen-Chung
;
Liang, Tyne
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資訊工程學系
Poems are usually expressed with elaborate rhetoric techniques, which make them hard to be understood. In this study, a cross-text annotation sharing mechanism is proposed to help students comprehend poetry by creating and sharing individual annotations. Furthermore, relevant annotations among various texts can be retrieved to stimulate and help students make connections while reading. A quasi-experiment has been conducted to evaluate the effectiveness of the proposed approach. The experimental results show that a cross-text annotation sharing approach is significantly more helpful to the students for poetry comprehension than a conventional annotation sharing approach in terms of learning achievements; moreover, the students highly accepted the developed learning system in terms of "ease of use" and "usefulness."
A decision support system for constructing an alert classification model
(2009-10-01)
Jan, Nien-Yi
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Lin, Shun-Chieh
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Tseng, Shian-Shyong
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Lin, Nancy P.
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資訊工程學系
As the rapid growth of network attacking tools, patterns of network intrusion events change gradually. Although many researches had been proposed to analyze network intrusion behaviors in accordance with low-level network data, they still suffer a large mount of false alerts and result in difficulties for network administrators to discover useful information from these alerts. To reduce the load of administrators, by collecting and analyzing unknown attack sequences systematically, administrators can do the duty of fixing the root causes. Due to the different characteristics of each intrusion, none of analysis methods can correlate IDS alerts precisely and discover all kinds of real intrusion patterns. Therefore, an alert-based decision support system is proposed in this paper to construct an alert classification model for on-line network behavior monitoring. The architecture of decision support system consists of three phases: Alert Preprocessing Phase, Model Constructing Phase and Rule Refining Phase. The Alert Processing Phase is used to transform IDS alerts into alert transactions with specific data format as alert subsequences, where an alert sequence is a kind of well-aggregated alert transaction format to discover intrusion behaviors. Besides, the Model Constructing Phase is used to construct three kinds of rule classes: normal rule classes, intrusion rule classes and suspicious rule classes, to filter false alert patterns and analyze each existing or unknown alert patterns; each rule class represents a set of classification rules. Normal rule class, a set of false alert classification rules, can be trained by using sequential pattern mining approach in an attack-free environment. Intrusion rule classes, a set of known intrusion classification rules, and suspicious rule classes, a set of novel intrusion classification rules, can be trained in a simulated attacking environment using several well-known rootkits and labeling by experts. Finally, the Rule Refining Phase is used to change the classification flags of alert sequence across different time intervals. According to the urgent situations of different levels, Network administrators can do event protecting or vulnerability repairing, even or cause tracing of attacks. Therefore, the decision support system can prevent attacks effectively, find novel attack patterns exactly and reduce the load of administrators efficiently. Crown Copyright (C) 2009 Published by Elsevier Ltd. All rights reserved.
A dynamic partitioning self-scheduling scheme for parallel loops on heterogeneous clusters
(2006)
Yang, Chao-Tung
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Shih, Wen-Chung
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Tseng, Shian-Shyong
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資訊工程學系
;
Department of Computer Science
Loop partitioning on parallel and distributed systems has been an important problem. Furthermore, it becomes more difficult to deal with on the emerging heterogeneous PC cluster environments. In this paper, we propose a performance-based scheme, which dynamically partitions loop iterations according to the performance ratio of cluster nodes. To verify our approach, a heterogeneous cluster is built, and two kinds of application programs are implemented to be executed in this testbed. Experimental results show that our approach performs better than traditional schemes.
A Folksonomy-based Guidance Mechanism for Context-aware Ubiquitous Learning: A Case Study of Chinese Scenic Poetry Appreciation Activities
(2012-01-01)
Shih, Wen-Chung
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Tseng, Shian-Shyong
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Yang, Che-Ching
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Lin, Chih-Yu
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Liang, Tyne
In this work ubiquitous learning technologies are applied to Chinese scenic poetry appreciation. A folksonomy-based approach is proposed to accumulate knowledge about poems and their corresponding scenic spots. A user can use a "Tagging" operation by a smart phone to associate a concept (a word, a phrase or a sentence) with a scenic spot. These associations will be recorded in a folksonomy, which is then transformed into a Learning Guidance Tree to support personalized guidance in u-learning environments. The purpose of this work is to propose a folksonomy-based guidance mechanism. Also, we attempt to find students' and teachers' attitudes towards the innovative application through a case study. To show the effectiveness of the innovative approach, a ubiquitous learning system was developed to conduct Chinese scenic poetry appreciation activities for a fifth-grade Chinese course in Taiwan, in which forty-eight students participated. The survey results show that the system had a positive impact on students' learning, especially on the affective domain including participation, motivation and interaction.
A GA-based method to reduce material handling: the case of TFT-LCD array Fabs in Taiwan
(2011)
Tseng, Shian-Shyong
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Chang, Fengming M.
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Chu, Yian-Shu
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Chi, Pi-Ju
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資訊工程學系
This study proposes a genetic algorithm (GA)-based Material Handling Strategy Function (MHSF) method to increase the productivity of a TFT-LCD array fabrication to improve the traditional FIFO method in TFT-LCD fabrication by the reduction of the material handling for a Rail Guided Vehicle (RGV), because costs of material handling count up to about 50% of production costs. In this article, the MHSF is provided to help material handling commands choice, where a novel GA approach is used to adapt the weights of affecting factors in the function. Finally, to evaluate the performance of the proposed method, experiments using the data from a major TFT-LCD manufacturing plant in Taiwan have been carried out, and the results indicate that it can indeed reduce fabrication time and increase productivity.
A Heuristic Data Distribution Scheme for Data Mining Applications on Grid Environments
(2008-01-01)
Yang, Chao-Tung
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Shih, Wen-Chung
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Tseng, Shian-Shyong
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資訊工程學系
;
Department of Computer Science
Effective data distribution techniques can significantly reduce the total execution time of a program on grid computing environments, especially for data mining applications. In this paper, we describe a linear programming formulation for the data distribution problem on grids. Furthermore, a heuristic method, named HDDS (Heuristic Data Distribution Scheme), is proposed to solve this problem. We implement the parallel association rule mining method and conduct the experimentations on our grid testbed. Experimental results showed that data mining programs using our HDDS to distribute data could execute more efficiently than traditional schemes could.
A knowledge based real-time travel time prediction system for urban network
(2009-04-01)
Lee, Wei-Hsun
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Tseng, Shian-Shyong
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Tsai, Sheng-Han
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資訊工程學系
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Department of Computer Science
Many approaches had been proposed for travel time prediction in these decades; most of them focus on the predicting the travel time on freeway or simple arterial network. Travel time prediction for urban network in real time is hard to achieve for several reasons: complexity and path routing problem in urban network, unavailability of real-time sensor data, spatiotemporal data coverage problem, and lacking real-time events consideration. In this paper, we propose a knowledge based real-time travel time prediction model which contains real-time and historical travel time predictors to discover traffic patterns from the raw data of location based services by data mining technique and transform them to travel time prediction rules. Besides, dynamic weight combination of the two predictors by meta-rules is proposed to provide a real-time traffic event response mechanism to enhance the precision of the travel time prediction. (c) 2008 Elsevier Ltd. All rights reserved.
A Knowledge-based Approach to Retrieving Teaching Materials for Context-aware Learning
(2009)
Shih, Wen-Chung
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Tseng, Shian-Shyong
;
資訊工程學系
;
Department of Computer Science
With the rapid development of wireless communication and sensor technologies, ubiquitous learning has become a promising solution to educational problems. In context-aware ubiquitous learning environments, it is required that learning content is retrieved according to environmental contexts, such as learners' location. Also, a learning content retrieval scheme should be able to work with various instructional strategies for different learning activities. To solve the context-aware learning content retrieval problem, we propose a strategy-driven approach to derive content retrieval strategies from instructional strategies. Moreover, we construct a knowledge-based system to expand query keywords based on the derived strategies and then select relevant keywords according to geographical distance between entities of concept and learners. This system is composed of four components: knowledge transformation, query expansion, content retrieval and user interface. Besides, ontology construction algorithms, designed for teachers to easily build up ontology from course outlines, are applied to generate the rules of query expansion and the taxonomic index of learning object repository. The experimental results indicate that the proposed approach can increase the learning performance of students.
A methodology for retrieving SCORM-compliant teaching materials on grid environments
(2006)
Shih, Wen-Chung
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Yang, Chao-Tung
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Tseng, Shian-Shyong
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資訊工程學系
;
Department of Computer Science
This paper proposes a methodology for acquiring the top k highest-ranking SCORM-compliant teaching materials on grid environments, for a given query. Especially, the ranking criterion combines the relevance of the document and the efficiency of transmission. This methodology consists of three steps. First, the ranking function of each Learning Object Repository (LOR) is evaluated. Next, the number of documents to be retrieved from each LOR is decided according to the Ranking Ratio. Finally, the k documents are retrieved from the grid. To verify this approach, a prototype of the retrieval system was built on a grid testbed. Experimental results showed that the proposed approach can retrieve satisfactory teaching materials for users.
A new approach for constructing the concept map
(2007-11-01)
Tseng, Shian-Shyong
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Sue, Pei-Chi
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Su, Jun-Ming
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Weng, Jul-Feng
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Tsai, Wen-Nung
In recent years, e-learning system has become more and more popular and many adaptive learning environments have been proposed to offer learners customized courses in accordance with their aptitudes and learning results. For achieving the adaptive learning, a predefined concept map of a course is often used to provide adaptive learning guidance for learners. However, it is difficult and time consuming to create the concept map of a course. Thus, how to automatically create a concept map of a course becomes an interesting issue. In this paper, we propose a Two-Phase Concept Map Construction (TP-CMC) approach to automatically construct the concept map by learners' historical testing records. Phase 1 is used to preprocess the testing records; i.e., transform the numeric grade data, refine the testing records, and mine the association rules from input data. Phase 2 is used to transform the mined association rules into prerequisite relationships among learning concepts for creating the concept map. Therefore, in Phase 1, we apply Fuzzy Set Theory to transform the numeric testing records of learners into symbolic data, apply Education Theory to further refine it, and apply Data Mining approach to find its grade fuzzy association rules. Then, in Phase 2, based upon our observation in real learning situation, we use multiple rule types to further analyze the mined rules and then propose a heuristic algorithm to automatically construct the concept map. Finally, the Redundancy and Circularity of the concept map constructed are also discussed. Moreover, we also develop a prototype system of TP-CMC and then use the real testing records of students in junior high school to evaluate the results. The experimental results show that our proposed approach is workable. (C) 2005 Elsevier Ltd. All rights reserved.
A novel approach to collaborative testing in a crowdsourcing environment
(2013-08-01)
Tung, Yuan-Hsin
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Tseng, Shian-Shyong
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資訊工程學系
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Department of Computer Science
Software testing processes are generally labor-intensive and often involve substantial collaboration among testers, developers, and even users. However, considerable human resource capacity exists on the Internet in social networks, expert communities, or internet forums-referred to as crowds. Effectively using crowd resources to support collaborative testing is an interesting and challenging topic. This paper defines the collaborative testing problem in a crowd environment as an NP-Complete job assignment problem and formulates it as an integer linear programming (ILP) problem. Although package tools can be used to obtain the optimal solution to an ILP problem, computational complexity makes these tools unsuitable for solving large-scale problems. This study uses a greedy approach with four heuristic strategies to solve the problem. This is called the crowdsourcing-based collaborative testing approach. This approach includes two phases, training phase and testing phase. The training phase transforms the original problem into an ILP problem. The testing phase solves the ILP using heuristic strategies. A prototype system, called the Collaborative Testing System (COTS), is also implemented. The experiment results show that the proposed heuristic algorithms produce good quality approximate solutions in an acceptable timeframe. (c) 2013 Elsevier Inc. All rights reserved.
A novel approach to collaborative testing in a crowdsourcing environment (vol 86, pg 2143, 2013)
(2014-01-01)
Tung, Yuan-Hsin
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Tseng, Shian-Shyong
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Tsai, Wei-Tek
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資訊工程學系
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Department of Computer Science
A novel feature selection method for large-scale data sets
(2005)
Chen, Wei-Chou
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Yang, Ming-Chun
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Tseng, Shian-Shyong
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資訊工程學系
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Department of Computer Science
Feature selection is about finding useful (relevant) features to describe an application domain. The problem of finding the minimal subsets of features that can describe all of the concepts in the given data set is NP-hard. In the past, we had proposed a feature selection method, which originated from rough set and bitmap indexing techniques, to select the optimal (minimal) feature set for the given data set efficiently. Although our method is sufficient to guarantee a solution's optimality, the computation cost is very high when the number of features is huge. In this paper, we propose a nearly optimal feature selection method, called bitmap-based feature selection method with discernibility matrix, which employs a discernibility matrix to record the important features during the construction of the cleansing tree to reduce the processing time. And the corresponding indexing and selecting algorithms for such feature selection method are also proposed. Finally, some experiments and comparisons are given and the result shows the efficiency and accuracy of our proposed method.
A peer-to-peer based framework of InterLibrary cooperation for digital libraries
(2006)
Shih, Wen-Chung
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Yang, Chao-Tung
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Tseng, Shian-Shyong
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Chen, Chun-Jen
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資訊工程學系
Traditionally, InterLibrary Cooperation is an important interaction between libraries. Nevertheless, the emerging digital library architecture has not explicitly supported this requirement. In this paper, we proposed an InterLibrary Cooperation Framework for digital libraries using P2P technology. An application of this framework to Faculty Publication Sharing System was presented. Besides, a reputation model based on data mining is utilized to provide libraries with incentives to join this framework.
A performance-based parallel loop scheduling on grid environments
(2007-09-01)
Shih, Wen-Chung
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Yang, Chao-Tung
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Tseng, Shian-Shyong
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資訊工程學系
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Department of Computer Science
The effectiveness of loop self-scheduling schemes has been shown on traditional multiprocessors in the past and computing clusters in the recent years. However, parallel loop scheduling has not been widely applied to computing grids, which are characterized by heterogeneous resources and dynamic environments. In this paper, a performance-based approach, taking the two characteristics above into consideration, is proposed to schedule parallel loop iterations on grid environments. Furthermore, we use a parameter, SWR, to estimate the proportion of the workload which can be scheduled statically, thus alleviating the effect of irregular workloads. Experimental results on a grid testbed show that the proposed approach can reduce the completion time for applications with regular or irregular workloads. Consequently, we claim that parallel loop scheduling can benefit applications on grid environments.
A personalized learning content adaptation mechanism to meet diverse user needs in mobile learning environments
(2011-04-01)
Su, Jun-Ming
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Tseng, Shian-Shyong
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Lin, Huan-Yu
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Chen, Chun-Han
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資訊工程學系
With the heterogeneous proliferation of mobile devices, the delivery of learning materials on such devices becomes subject to more and more requirements. Personalized learning content adaptation, therefore, becomes increasingly important to meet the diverse needs imposed by devices, users, usage contexts, and infrastructure. Historical server logs offer a wealth of information on hardware capabilities, learners' preferences, and network conditions, which can be utilized to respond to a new user request with the personalized learning content created from a previous similar request. In this paper, we propose a Personalized Learning Content Adaptation Mechanism (PLCAM), which applies data mining techniques, including clustering and decision tree approaches, to efficiently manage a large number of historical learners' requests. The proposed method will intelligently and directly deliver proper personalized learning content with higher fidelity from the Sharable Content Object Reference Model (SCORM)-compliant Learning Object Repository (LOR) by means of the proposed adaptation decision and content synthesis processes. Furthermore, the experimental results indicate that it is efficient and is expected to prove beneficial to learners.
A rule-based CBR approach for expert finding and problem diagnosis
(2010-03-15)
Tung, Yuan-Hsin
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Tseng, Shian-Shyong
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Weng, Jui-Feng
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Lee, Tsung-Ping
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Liao, Anthony Y. H.
It is important to find the person with right expertise and the appropriate solutions in the specific field to solve a critical situation in a large complex system such as an enterprise level application. In this paper, we apply the experts' knowledge to construct a solution retrieval system for expert finding and problem diagnosis. Firstly, we aim to utilize the experts' problem diagnosis knowledge which can identify the error type of problem to suggest the corresponding expert and retrieve the solution for specific error type. Therefore, how to find an efficient way to use domain knowledge and the corresponding experts has become an important issue. To transform experts' knowledge into the knowledge base of a solution retrieval system, the idea of developing a solution retrieval system based on hybrid approach using RBR (rule-based reasoning) and CBR (case-based reasoning), RCBR (rule-based CBR), is proposed in this research. Furthermore, we incorporate domain expertise into our methodology with role-based access control model to suggest appropriate expert for problem solving, and build a prototype system with expert finding and problem diagnosis for the complex system. The experimental results show that RCBR (rule-based CBR) can improve accuracy of retrieval cases and reduce retrieval time prominently. (C) 2009 Elsevier Ltd. All rights reserved.