The Experts below are selected from a list of 126771 Experts worldwide ranked by ideXlab platform
Michael R. Lyu - One of the best experts on this subject based on the ideXlab platform.
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An Online Performance Prediction Framework for Service-Oriented Systems
IEEE Transactions on Systems Man and Cybernetics: Systems, 2014Co-Authors: Yilei Zhang, Zibin Zheng, Michael R. LyuAbstract:The exponential growth of Web service makes building high-quality service-oriented systems an urgent and crucial research problem. Performance of the service-oriented systems highly depends on the remote Web services as well as the unpredictability of the Internet. Performance Prediction of service-oriented systems is critical for automatically selecting the optimal Web service composition. Since the performance of Web services is highly related to the service status and network environments which are variable over time, it is an important task to predict the performance of service-oriented systems at run-time. To address this critical challenge, this paper proposes an online performance Prediction Framework, called OPred, to provide personalized service-oriented system performance Prediction efficiently. Based on the past usage experience from different users, OPred builds feature models and employs time series analysis techniques on feature trends to make performance Prediction. The results of large-scale real-world experiments show the effectiveness and efficiency of OPred.
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wspred a time aware personalized qos Prediction Framework for web services
International Symposium on Software Reliability Engineering, 2011Co-Authors: Yilei Zhang, Zibin Zheng, Michael R. LyuAbstract:The exponential growth of Web service makes building high-quality service-oriented applications an urgent and crucial research problem. User-side QoS evaluations of Web services are critical for selecting the optimal Web service from a set of functionally equivalent service candidates. Since QoS performance of Web services is highly related to the service status and network environments which are variable against time, service invocations are required at different instances during a long time interval for making accurate Web service QoS evaluation. However, invoking a huge number of Web services from user-side for quality evaluation purpose is time-consuming, resource-consuming, and sometimes even impractical (e.g., service invocations are charged by service providers). To address this critical challenge, this paper proposes a Web service QoS Prediction Framework, called WSPred, to provide time-aware personalized QoS value Prediction service for different service users. WSPred requires no additional invocation of Web services. Based on the past Web service usage experience from different service users, WSPred builds feature models and employs these models to make personalized QoS Prediction for different users. The extensive experimental results show the effectiveness and efficiency of WSPred. Moreover, we publicly release our real-world time-aware Web service QoS dataset for future research, which makes our experiments verifiable and reproducible.
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ISSRE - WSPred: A Time-Aware Personalized QoS Prediction Framework for Web Services
2011 IEEE 22nd International Symposium on Software Reliability Engineering, 2011Co-Authors: Yilei Zhang, Zibin Zheng, Michael R. LyuAbstract:The exponential growth of Web service makes building high-quality service-oriented applications an urgent and crucial research problem. User-side QoS evaluations of Web services are critical for selecting the optimal Web service from a set of functionally equivalent service candidates. Since QoS performance of Web services is highly related to the service status and network environments which are variable against time, service invocations are required at different instances during a long time interval for making accurate Web service QoS evaluation. However, invoking a huge number of Web services from user-side for quality evaluation purpose is time-consuming, resource-consuming, and sometimes even impractical (e.g., service invocations are charged by service providers). To address this critical challenge, this paper proposes a Web service QoS Prediction Framework, called WSPred, to provide time-aware personalized QoS value Prediction service for different service users. WSPred requires no additional invocation of Web services. Based on the past Web service usage experience from different service users, WSPred builds feature models and employs these models to make personalized QoS Prediction for different users. The extensive experimental results show the effectiveness and efficiency of WSPred. Moreover, we publicly release our real-world time-aware Web service QoS dataset for future research, which makes our experiments verifiable and reproducible.
Zibin Zheng - One of the best experts on this subject based on the ideXlab platform.
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A Privacy-Preserving QoS Prediction Framework for Web Service Recommendation
2015 IEEE International Conference on Web Services, 2015Co-Authors: Pinjia He, Zibin ZhengAbstract:QoS-based Web service recommendation has recently gained much attention for providing a promising way to help users find high-quality services. To facilitate such recommendations, existing studies suggest the use of collaborative filtering techniques for personalized QoS Prediction. These approaches, by leveraging partially observed QoS values from users, can achieve high accuracy of QoS Predictions on the unobserved ones. However, the requirement to collect users' QoS data likely puts user privacy at risk, thus making them unwilling to contribute their usage data to a Web service recommender system. As a result, privacy becomes a critical challenge in developing practical Web service recommender systems. In this paper, we make the first attempt to cope with the privacy concerns for Web service recommendation. Specifically, we propose a simple yet effective privacy-preserving Framework by applying data obfuscation techniques, and further develop two representative privacy-preserving QoS Prediction approaches under this Framework. Evaluation results from a publicly-available QoS dataset of real-world Web services demonstrate the feasibility and effectiveness of our privacy-preserving QoS Prediction approaches. We believe our work can serve as a good starting point to inspire more research efforts on privacy-preserving Web service recommendation.
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An Online Performance Prediction Framework for Service-Oriented Systems
IEEE Transactions on Systems Man and Cybernetics: Systems, 2014Co-Authors: Yilei Zhang, Zibin Zheng, Michael R. LyuAbstract:The exponential growth of Web service makes building high-quality service-oriented systems an urgent and crucial research problem. Performance of the service-oriented systems highly depends on the remote Web services as well as the unpredictability of the Internet. Performance Prediction of service-oriented systems is critical for automatically selecting the optimal Web service composition. Since the performance of Web services is highly related to the service status and network environments which are variable over time, it is an important task to predict the performance of service-oriented systems at run-time. To address this critical challenge, this paper proposes an online performance Prediction Framework, called OPred, to provide personalized service-oriented system performance Prediction efficiently. Based on the past usage experience from different users, OPred builds feature models and employs time series analysis techniques on feature trends to make performance Prediction. The results of large-scale real-world experiments show the effectiveness and efficiency of OPred.
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wspred a time aware personalized qos Prediction Framework for web services
International Symposium on Software Reliability Engineering, 2011Co-Authors: Yilei Zhang, Zibin Zheng, Michael R. LyuAbstract:The exponential growth of Web service makes building high-quality service-oriented applications an urgent and crucial research problem. User-side QoS evaluations of Web services are critical for selecting the optimal Web service from a set of functionally equivalent service candidates. Since QoS performance of Web services is highly related to the service status and network environments which are variable against time, service invocations are required at different instances during a long time interval for making accurate Web service QoS evaluation. However, invoking a huge number of Web services from user-side for quality evaluation purpose is time-consuming, resource-consuming, and sometimes even impractical (e.g., service invocations are charged by service providers). To address this critical challenge, this paper proposes a Web service QoS Prediction Framework, called WSPred, to provide time-aware personalized QoS value Prediction service for different service users. WSPred requires no additional invocation of Web services. Based on the past Web service usage experience from different service users, WSPred builds feature models and employs these models to make personalized QoS Prediction for different users. The extensive experimental results show the effectiveness and efficiency of WSPred. Moreover, we publicly release our real-world time-aware Web service QoS dataset for future research, which makes our experiments verifiable and reproducible.
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ISSRE - WSPred: A Time-Aware Personalized QoS Prediction Framework for Web Services
2011 IEEE 22nd International Symposium on Software Reliability Engineering, 2011Co-Authors: Yilei Zhang, Zibin Zheng, Michael R. LyuAbstract:The exponential growth of Web service makes building high-quality service-oriented applications an urgent and crucial research problem. User-side QoS evaluations of Web services are critical for selecting the optimal Web service from a set of functionally equivalent service candidates. Since QoS performance of Web services is highly related to the service status and network environments which are variable against time, service invocations are required at different instances during a long time interval for making accurate Web service QoS evaluation. However, invoking a huge number of Web services from user-side for quality evaluation purpose is time-consuming, resource-consuming, and sometimes even impractical (e.g., service invocations are charged by service providers). To address this critical challenge, this paper proposes a Web service QoS Prediction Framework, called WSPred, to provide time-aware personalized QoS value Prediction service for different service users. WSPred requires no additional invocation of Web services. Based on the past Web service usage experience from different service users, WSPred builds feature models and employs these models to make personalized QoS Prediction for different users. The extensive experimental results show the effectiveness and efficiency of WSPred. Moreover, we publicly release our real-world time-aware Web service QoS dataset for future research, which makes our experiments verifiable and reproducible.
Yilei Zhang - One of the best experts on this subject based on the ideXlab platform.
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An Online Performance Prediction Framework for Service-Oriented Systems
IEEE Transactions on Systems Man and Cybernetics: Systems, 2014Co-Authors: Yilei Zhang, Zibin Zheng, Michael R. LyuAbstract:The exponential growth of Web service makes building high-quality service-oriented systems an urgent and crucial research problem. Performance of the service-oriented systems highly depends on the remote Web services as well as the unpredictability of the Internet. Performance Prediction of service-oriented systems is critical for automatically selecting the optimal Web service composition. Since the performance of Web services is highly related to the service status and network environments which are variable over time, it is an important task to predict the performance of service-oriented systems at run-time. To address this critical challenge, this paper proposes an online performance Prediction Framework, called OPred, to provide personalized service-oriented system performance Prediction efficiently. Based on the past usage experience from different users, OPred builds feature models and employs time series analysis techniques on feature trends to make performance Prediction. The results of large-scale real-world experiments show the effectiveness and efficiency of OPred.
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wspred a time aware personalized qos Prediction Framework for web services
International Symposium on Software Reliability Engineering, 2011Co-Authors: Yilei Zhang, Zibin Zheng, Michael R. LyuAbstract:The exponential growth of Web service makes building high-quality service-oriented applications an urgent and crucial research problem. User-side QoS evaluations of Web services are critical for selecting the optimal Web service from a set of functionally equivalent service candidates. Since QoS performance of Web services is highly related to the service status and network environments which are variable against time, service invocations are required at different instances during a long time interval for making accurate Web service QoS evaluation. However, invoking a huge number of Web services from user-side for quality evaluation purpose is time-consuming, resource-consuming, and sometimes even impractical (e.g., service invocations are charged by service providers). To address this critical challenge, this paper proposes a Web service QoS Prediction Framework, called WSPred, to provide time-aware personalized QoS value Prediction service for different service users. WSPred requires no additional invocation of Web services. Based on the past Web service usage experience from different service users, WSPred builds feature models and employs these models to make personalized QoS Prediction for different users. The extensive experimental results show the effectiveness and efficiency of WSPred. Moreover, we publicly release our real-world time-aware Web service QoS dataset for future research, which makes our experiments verifiable and reproducible.
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ISSRE - WSPred: A Time-Aware Personalized QoS Prediction Framework for Web Services
2011 IEEE 22nd International Symposium on Software Reliability Engineering, 2011Co-Authors: Yilei Zhang, Zibin Zheng, Michael R. LyuAbstract:The exponential growth of Web service makes building high-quality service-oriented applications an urgent and crucial research problem. User-side QoS evaluations of Web services are critical for selecting the optimal Web service from a set of functionally equivalent service candidates. Since QoS performance of Web services is highly related to the service status and network environments which are variable against time, service invocations are required at different instances during a long time interval for making accurate Web service QoS evaluation. However, invoking a huge number of Web services from user-side for quality evaluation purpose is time-consuming, resource-consuming, and sometimes even impractical (e.g., service invocations are charged by service providers). To address this critical challenge, this paper proposes a Web service QoS Prediction Framework, called WSPred, to provide time-aware personalized QoS value Prediction service for different service users. WSPred requires no additional invocation of Web services. Based on the past Web service usage experience from different service users, WSPred builds feature models and employs these models to make personalized QoS Prediction for different users. The extensive experimental results show the effectiveness and efficiency of WSPred. Moreover, we publicly release our real-world time-aware Web service QoS dataset for future research, which makes our experiments verifiable and reproducible.
Wei Xie - One of the best experts on this subject based on the ideXlab platform.
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WSC - A simulation-based Prediction Framework for stochastic system dynamic risk management
2018 Winter Simulation Conference (WSC), 2018Co-Authors: Wei Xie, Pu Zhang, Ilya O. RyzhovAbstract:We propose a simulation-based Prediction Framework which can quantify the Prediction uncertainty of system future response and further guide operational decisions for complex stochastic systems. Specifically, by exploring the underlying generative process of real-world data streams, we first develop a nonparametric input model which can capture the important properties, including non-stationarity, skewness, componentwise and time dependence. It can improve the Prediction accuracy, and the posterior predictive distribution can quantify the Prediction uncertainty accounting for both input and stochastic uncertainties. Then, we propose the simulation-based Prediction Framework which can efficiently search for the optimal operational decisions hedging against the Prediction uncertainty and minimizing the expected cost occurring in the planning horizon. The empirical study demonstrates that our approach has promising performance.
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Winter Simulation Conference - A simulation-based Prediction Framework for two-stage dynamic decision making
2016 Winter Simulation Conference (WSC), 2016Co-Authors: Wei XieAbstract:When we make operational decisions for high-tech manufacturing with products having short life times, there exist various challenges: (1) high uncertainty in the supply, production and demand; (2) limited amount of valid historical data; and (3) decision makers with a risk-averse attitude. We propose a simulation-based Prediction Framework to support real-time decision making. Specifically, we consider a generalized two-stage dynamic decision model accounting for both input uncertainty and system inherent stochastic uncertainty. Since the risk-adjusted cost objective involves nested risk measures, it could be computationally prohibitive to precisely estimate the system performance, especially for complex stochastic systems. Given a decision policy, in this paper, a metamodel-assisted approach is introduced to efficiently assess the system risk performance in the planning horizon, while delivering a credible interval quantifying the simulation estimation error. This information can guide us to select a good policy for real-time decision making.
Albert Y. S. Lam - One of the best experts on this subject based on the ideXlab platform.
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Delay Aware Power System Synchrophasor Recovery and Prediction Framework
IEEE Transactions on Smart Grid, 2019Co-Authors: Albert Y. S. Lam, David J. Hill, Yunhe HouAbstract:This paper presents a novel delay aware synchrophasor recovery and Prediction Framework to address the problem of missing power system state variables due to the existence of communication latency. This capability is particularly essential for dynamic power system scenarios where fast remedial control actions are required due to system events or faults. While a wide area measurement system can sample high-frequency system states with phasor measurement units, the control center cannot obtain them in real-time due to latency and data loss. In this work, a synchrophasor recovery and Prediction Framework and its practical implementation are proposed to recover the current system state and predict future states utilizing existing incomplete synchrophasor data. The Framework establishes an iterative Prediction scheme, and the proposed implementation adopts recent machine learning advances in data processing. Simulation results indicate the superior accuracy and speed of the proposed Framework, and investigations are made to study its sensitivity to various communication delay patterns for pragmatic applications.
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Delay aware transient stability assessment with synchrophasor recovery and Prediction Framework
Neurocomputing, 2018Co-Authors: David J. Hill, Albert Y. S. LamAbstract:Abstract Transient stability assessment is critical for power system operation and control. Existing related research makes a strong assumption that the data transmission time for system variable measurements to arrive at the control center is negligible, which is unrealistic. In this paper, we focus on investigating the impact of data transmission latency on synchrophasor-based transient stability assessment. In particular, we employ a recently proposed methodology named synchrophasor recovery and Prediction Framework to handle the latency issue and make up missing synchrophasors. Advanced deep learning techniques are adopted to utilize the processed data for assessment. Compared with existing work, our proposed mechanism can make accurate assessments with a significantly faster response speed.