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检索条件"机构=Process and Data Science"
327 条 记 录,以下是41-50 订阅
排序:
Distributed Dimensionality Reduction Filtering for CPSs Under DoS Attacks
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IEEE/CAA Journal of Automatica Sinica 2023年 第4期10卷 1080-1082页
作者: Xiaoyuan Zheng Hao Zhang Xindi Yang Huaicheng Yan School of Artificial Intelligence and Data Science Hebei University of TechnologyTianjin 300401China Department of Control Science and Engineering Tongji UniversityShanghai 200092China Key Laboratory of Smart Manufacturing in Energy Chemcical Process of Ministry of Education East China University of Science and TechnologyShanghai 200237 School of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China
Dear Editor,This letter concerns about the distributed dimensionally reduction filtering for a class of cyber physical systems with denial of service(DoS)*** each sensor exchanging measurements with neighbors subjecte... 详细信息
来源: 评论
Agile Working during COVID-19 Pandemic
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IEEE Engineering Management Review 2021年 第2期49卷 18-32页
作者: Schmidtner, Markus Doering, Claudia Timinger, Holger Institute for Data and Process Science University of Applied Sciences Landshut LandshutD-84036 Germany
Values and principles of agile working emphasize the benefits of personal communication and open team spaces with members sitting in close proximity. This facilitates customer-oriented results and reduces the risk for... 详细信息
来源: 评论
XPM: eXplainable process Mining  3rd
XPM: eXplainable Process Mining
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3rd International Conference on Advances in Information and Communication Technology, ICTA 2024
作者: Kim, Kwanghoon Pio CCSRI Kyonggi University Suwon Korea Republic of Data. Process Engineering Research Lab. Div. of AI Computer Science and Engineering Kyonggi University Gyeonggi Suwon16227 Korea Republic of
This keynote speech is to all intents and purposes introducing a new process mining approach and its implemented system, which are named as eXplainable process mining (XPM) approach and system, respectively. Through t... 详细信息
来源: 评论
Wasserstein Weight Estimation for Stochastic Petri Nets
Wasserstein Weight Estimation for Stochastic Petri Nets
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International Conference on process Mining (ICPM)
作者: Tobias Brockhoff Merih Seran Uysal Wil M.P. Van Der Aalst Process and Data Science Group (PADS) RWTH Aachen University Aachen Germany
Traditional process models like Petri nets effectively describe the control flow of processes but fail to capture stochastic information such as choice likelihoods. To address this, Stochastic Labeled Petri Nets (SPNs... 详细信息
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An Efficient and Provably Secure SM2 Key-Insulated Signature Scheme for Industrial Internet of Things
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Computer Modeling in Engineering & sciences 2024年 第1期138卷 903-915页
作者: Senshan Ouyang Xiang Liu Lei Liu Shangchao Wang Baichuan Shao Yang Zhao School of Mechanical Engineering Northwestern Polytechnical UniversityXi’anChina Department of Process and Information Technology Chengdu Aircraft Industrial(Group)Co.Ltd.ChengduChina School of Information and Software Engineering The Network and Data Security Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
With the continuous expansion of the Industrial Internet of Things(IIoT),more andmore organisations are placing large amounts of data in the cloud to reduce ***,the channel between cloud servers and smart equipment is... 详细信息
来源: 评论
MQTT-4EST: RulE-basEd WEb Editor for Semantic-aware Topic Naming in MQTT  19
MQTT-4EST: RulE-basEd WEb Editor for Semantic-aware Topic Na...
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19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022
作者: Piller, Tobias Christian Merz, David Maria Khelil, Abdelmajid Institute for Data and Process Science Landshut University of Applied Sciences Am Lurzenhof 1 Landshut84036 Germany
Message Queuing Telemetry Transport (MQTT) is an established data transmission protocol in virtually all Smart-∗ domains. The protocol is based on the publish/subscribe pattern. Messages are published to so-called top... 详细信息
来源: 评论
CIML-R: Causally Informed Machine Learning Based on Feature Relevance
CIML-R: Causally Informed Machine Learning Based on Feature ...
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Swiss Conference on data science (SDS)
作者: Martin Surner Abdelmajid Khelil Computer Science Department Institute for Data and Process Science Landshut University of Applied Sciences Landshut Germany
Applications relying on machine learning and statistical learning techniques, such as neural networks, have significantly grown over the past decade. Nevertheless, as these techniques learn only from observational dat... 详细信息
来源: 评论
A Scalable database for the Storage of Object-Centric Event Logs  3
A Scalable Database for the Storage of Object-Centric Event ...
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3rd International Conference on process Mining Doctoral Consortium and Demo Track, ICPM-D 2021
作者: Berti, Alessandro Ghahfarokhi, Anahita Farhang Park, Gyunam van der Aalst, Wil M.P. Process and Data Science Department RWTH Aachen University Process and Data Science department Lehrstuhl fur Informatik 9 Aachen52074 Germany
Object-centric process mining provides a set of techniques for the analysis of event data where events are associated to several objects. To store Object-Centric Event Logs (OCELs), the JSON-OCEL and JSON-XML formats ... 详细信息
来源: 评论
TraVaG: Differentially Private Trace Variant Generation Using GANs
arXiv
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arXiv 2023年
作者: Rafiei, Majid Wangelik, Frederik Pourbafrani, Mahsa van der Aalst, Wil M.P. Process and Data Science RWTH Aachen University Aachen Germany
process mining is rapidly growing in the industry. Consequently, privacy concerns regarding sensitive and private information included in event data, used by process mining algorithms, are becoming increasingly releva... 详细信息
来源: 评论
Object-Centric Alignments
arXiv
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arXiv 2023年
作者: Liss, Lukas Adams, Jan Niklas van der Aalst, Wil M.P. Process and Data Science RWTH Aachen University Aachen Germany
processes tend to interact with other processes and operate on various objects of different types. These objects can influence each other creating dependencies between sub-processes. Analyzing the conformance of such ... 详细信息
来源: 评论