2020 IEEE International Conference on Web Services (ICWS)最新文献_第3页

Remaining Time Prediction of Business Processes based on Multilayer Machine Learning 基于多层机器学习的业务流程剩余时间预测

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00080

Xiaoxiao Sun, Wenjie Hou, Yuke Ying, Dongjin Yu

{"title":"Remaining Time Prediction of Business Processes based on Multilayer Machine Learning","authors":"Xiaoxiao Sun, Wenjie Hou, Yuke Ying, Dongjin Yu","doi":"10.1109/ICWS49710.2020.00080","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00080","url":null,"abstract":"Remaining time predictive monitoring of business processes (BPs) is a key research issue in business process mining, which provides timely predictive information for stakeholders to take proactive corrective actions to reduce process execution risk such as exceeding time limit or to adjust the priority of activities. However, current researches on remaining time prediction only consider the impact of internal attributes of single process instance, but ignore the resource competition among multiple instances executed together. Therefore, this paper takes resource competition into consideration and characterizes several inter-instance attributes as the input of prediction. We also prioritize and select some key activities that strongly impact the execution time of BPs according to historical event logs and include them as input of the prediction. Meanwhile, in order to solve the instability of one single prediction model in complex scenarios, a multilayer hybrid model constructed from XGBoost and LightGBM models using stacking technique is proposed. Experiments on four real-life datasets show that our approach of considering attributes among instances and including key activities into a hybrid model outperforms other prediction methods.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133454668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Security-Aware QoS Forecasting in Mobile Edge Computing based on Federated Learning 基于联邦学习的移动边缘计算安全感知QoS预测

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00046

Huiying Jin, Pengcheng Zhang, Hai Dong

引用次数: 1

A Practical Defense against Attribute Inference Attacks in Session-based Recommendations 基于会话的推荐中属性推理攻击的实用防御

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00053

Yifei Zhang, Neng Gao, Junsha Chen

{"title":"A Practical Defense against Attribute Inference Attacks in Session-based Recommendations","authors":"Yifei Zhang, Neng Gao, Junsha Chen","doi":"10.1109/ICWS49710.2020.00053","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00053","url":null,"abstract":"When users in various web and mobile applications enjoy the convenience of recommendation systems, they are vulnerable to attribute inference attacks. The accumulating online behaviors of users (e.g., clicks, searches, ratings) naturally brings out user preferences, and poses an inevitable threat of privacy that adversaries can infer one's private profiles (e.g., gender, sexual orientation, political view) with AI-based algorithms. Existing defense methods assume the existence of a trusted third party, rely on computationally intractable algorithms, or have impact on recommendation utility. These imperfections make them impractical for privacy preservation in real-life scenarios. In this work, we introduce BiasBooster, a practical proactive defense method based on behavior segmentation, to protect user privacy against attribute inference attacks from user behaviors, while retaining recommendation utility with a heuristic recommendation aggregation module. BiasBooster is a user-centric approach from client side, which proactively divides a user's behaviors into weakly related segments and perform them with several dummy identities, then aggregates real-time recommendations for user from different dummy identities. We estimate its effectiveness of preservation on both privacy and recommendation utility through extensive evaluations on two real-world datasets. A Chrome extension is conducted to demonstrate the feasibility of applying BiasBooster in real world. Experimental results show that compared to existing defenses, BiasBooster substantially reduces the averaged accuracy of attribute inference attacks, with minor utility loss of recommendations.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124399451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

NAFM: Neural and Attentional Factorization Machine for Web API Recommendation Web API推荐的神经和注意力分解机

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00050

Guosheng Kang, Jianxun Liu, Buqing Cao, Manliang Cao

{"title":"NAFM: Neural and Attentional Factorization Machine for Web API Recommendation","authors":"Guosheng Kang, Jianxun Liu, Buqing Cao, Manliang Cao","doi":"10.1109/ICWS49710.2020.00050","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00050","url":null,"abstract":"With the wide adoption of SOA (Service Oriented Architecture), a massive amount of innovative applications emerge on the Internet. One of the popular representations is Mashup composed of multiple Web APIs. Recommending desirable Web APIs to develop Mashup applications has attracted much attention. A dozen of service recommendation approaches are proposed by incorporating multi-dimensional features extracted from service repository into recommendation models. Among the existing works, factorization machine based models show better performance than traditional collaborative filtering techniques in accuracy. However, they either model factorized interactions with the same weight or neglect the non-linear and complex inherent structure of real-world data. In real-world applications, different predictor variables usually have different predictive power, and not all features contain useful signal for estimating the target. Moreover, higher-order feature interactions are usually underlain in real-world data. To address these drawbacks, this paper proposes a hybrid factorization machine model with a novel neural network architecture named NAFM by integrating deep neural network to capture the non-linear feature interactions and attention mechanism to capture the different importance of feature interactions. Comprehensive experiments on a real-world dataset show that the proposed approach outperforms the other state-of-the-art models for service recommendation.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117273450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Time-aware Service Recommendation Based on Dynamic Preference and QoS 基于动态偏好和QoS的时间感知服务推荐

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00052

Yanmei Zhang, Zhuo Li, Xiaoyi Tang, Fu Chen

引用次数: 2

Efficient and Privacy-Preserving Federated QoS Prediction for Cloud Services 高效且保护隐私的云服务联邦QoS预测

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00079

Yilei Zhang, Peiyun Zhang, Yonglong Luo, Jun Luo

引用次数: 6

Efficient Search for Moving Object Devices in Internet of Things Networks 物联网网络中移动对象设备的高效搜索

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00067

Jine Tang, Xiao Xue, Sami Yangui, Zhangbing Zhou

引用次数: 0

Computation Offloading and Content Caching with Traffic Flow Prediction for Internet of Vehicles in Edge Computing 基于边缘计算的车联网交通流预测计算卸载和内容缓存

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00056

Zijie Fang, Xiaolong Xu, Fei Dai, Lianyong Qi, Xuyun Zhang, Wanchun Dou

{"title":"Computation Offloading and Content Caching with Traffic Flow Prediction for Internet of Vehicles in Edge Computing","authors":"Zijie Fang, Xiaolong Xu, Fei Dai, Lianyong Qi, Xuyun Zhang, Wanchun Dou","doi":"10.1109/ICWS49710.2020.00056","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00056","url":null,"abstract":"The development of the Internet of Vehicles (IoV) enables numerous emerging in-vehicle applications to accommodate users with various contents, thus enhancing their traveling experiences. In IoV, content decoding tasks are typically offloaded to edge servers for implementation, as edge computing is an admirable paradigm to provide low-latency services. However, as different vehicular users may request the same contents, processing these contents repeatedly leads to the waste of storage, computation and bandwidth resources. Therefore, fine-grained computation offloading and content caching are demanded in IoV. In this paper, a joint optimization method for computation offloading and content caching based on traffic flow prediction, named COC, is proposed. Firstly, traffic flow covered by each edge server is predicted by a modified deep spatiotemporal residual network (ST-ResNet). Secondly, the non-dominated sorting genetic algorithm III (NSGA-III) is leveraged to realize the many-objective optimization to shorten the execution time and reduce the energy consumption of computation and transmission in IoV. Finally, evaluated by real-world big data from Nanjing China, COC shows a great reduction in execution time and energy consumption of transmission and computation compared to other methods.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"299 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116882447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Location-Aware and Budget-Constrained Application Replication and Deployment in Multi-Cloud Environment 多云环境下的位置感知和预算约束应用程序复制和部署

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00022

Tao Shi, Hui Ma, Gang Chen, Sven Hartmann

{"title":"Location-Aware and Budget-Constrained Application Replication and Deployment in Multi-Cloud Environment","authors":"Tao Shi, Hui Ma, Gang Chen, Sven Hartmann","doi":"10.1109/ICWS49710.2020.00022","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00022","url":null,"abstract":"To gain technical and economic benefits, enterprise application providers are increasingly moving their workloads to the cloud. With the increasing number of cloud resources from multiple cloud providers at different locations with differentiated prices, application providers face the challenge to select proper cloud resources to replicate and deploy applications to maintain low response time and high quality of user experience without running into the risk of over-spending. In this paper, we study the global-wide cloud application replication and deployment problem considering the application average response time, including particularly application execution time and network latency, subject to the budgetary control. To address the problem, we propose a GA-based approach with domain-tailored solution representation, fitness measurement, and population initialization. Extensive experiments using the real-world datasets demonstrate that our proposed GA-based approach significantly outperforms common application placement strategies, i.e., NearData and NearUsers, and our recently proposed hybrid GA approach.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114418862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Investigating the Evolution of Web API Cooperative Communities in the Mashup Ecosystem 研究Mashup生态系统中Web API合作社区的演变

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00060

Qing Qi, Jian Cao

引用次数: 1