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Empowering data Mesh with Federated Learning

Empowering Data Mesh with Federated Learning

引用

2024 ieee international conference on Big data, Bigdata 2024

作者： Li, Haoyuan Toor, Salman Eindhoven University of Technology Department of Industrial Design Eindhoven Netherlands Uppsala University Scaleout Systems Department of Information Technology Uppsala Sweden

ISBN: (纸本)9798350362480

The evolution of data architecture has seen the rise of data lakes, aiming to solve the bottlenecks of data management and promote intelligent decision-making. However, this centralized architecture is limited by the proliferation of data sources and the growing demand for timely analysis and processing. A new data paradigm, data Mesh, is proposed to overcome these challenges. In this decentralized architecture where data is locally preserved by each domain team, traditional centralized machine learning cannot conduct effective analysis across multiple domains, especially for security-sensitive organizations. To this end, we introduce a pioneering approach that incorporates Federated Learning into data Mesh. This applied research article emphasizes the benefits of combining two distinct domains to achieve the best outcomes for industrial use cases. © 2024 ieee.

关键词： Federated learning

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data-driven Methods for Travel Time Estimation: A Survey 26

Data-driven Methods for Travel Time Estimation: A Survey

引用

ieee 26th international conference on Intelligent Transportation systems (ITSC)

作者： Zheng, Zhipeng Ye, Yongchao Zhu, Yuanshao Zhang, Shiyao Yu, James J. Q. Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Univ York Dept Comp Sci York N Yorkshire England

ISBN: (纸本)9798350399462

Travel time estimation is a crucial component of intelligent transportation systems, affecting various applications such as navigation, ride-hailing, and route planning. Traditional methods for travel time estimation rely on subjective judgments, limited data sources, and straightforward modeling techniques. Owing to recent advances in data mining and machine learning, numerous data-driven methods are adopted to address the problem that occurred in traditional schemes, which demonstrate exceptional performance. In this paper, we present a comprehensive survey of data-driven methods for travel time estimation, encompassing application scenarios, spatial-temporal modeling approaches, and data representation learning techniques. To support and promote further research in this field, we provide a valuable list of open data sources and source codes, offering researchers a solid foundation for their future endeavors. Furthermore, this survey discusses emerging trends and key challenges faced by the research community, such as the integration of real-time data streams and the use of uncertainty estimation. We also explore the potential impact of these advancements on transportation systems, highlighting opportunities for improvement and innovation. To the best of our knowledge, this work is among the first to offer a comprehensive, in-depth review of data-driven methods for travel time estimation, providing researchers and practitioners with a valuable reference in the field.

关键词： data mining

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Improving data science Applications with a Visual Cross-Platform Execution Environment

Improving Data Science Applications with a Visual Cross-Plat...

引用

2024 ieee international conference on Big data, Bigdata 2024

作者： Ponce, Lucas M. Guedes, Dorgival Department of Computer Science Belo Horizonte Brazil

ISBN: (纸本)9798350362480

Scheduling and processing decisions in multiplatform data systems can lead to efficient execution of workflow tasks across available platforms. In this work, we discuss how to create a cross-platform system for data science tasks that leverages modern data abstractions, such as visual programming and dataFrames. Our approach uses an extended graph to represent workflows, capturing various execution aspects, including dataset evolution, execution costs, and data migration costs across different platforms and environments. This graph can be further transformed based on predefined rules, optimization techniques, and the application of machine learning tools to guide decision-making during the scheduling of the blocks that compose the workflows in question. Empirically, we demonstrate that our prototype, which initially supports Spark and Pandas, can identify not only the best platform with 90.8% accuracy, or the best execution environment for an entire workflow (83.8%), but also achieve performance gains of up to 2.5 times by scheduling tasks on the best platform for each activity. © 2024 ieee.

关键词： data science

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Enhancing IoT Security with Asynchronous Federated Learning for Seismic Inversion 59

Enhancing IoT Security with Asynchronous Federated Learning ...

引用

59th Annual ieee international conference on Communications (ieee ICC)

作者： Manu, Daniel Lin, Youzuo Yao, Jingjing Li, Zhirun Sun, Xiang Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA Univ N Carolina Sch Data Sci & Soc Chapel Hill NC 27599 USA Los Alamos Natl Lab Los Alamos NM USA Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA

ISBN: (纸本)9798350304060;9798350304053

Seismic data/images are critical to understand the structure of subsurfaces. However, for accurate structural analysis, seismic images need to be converted into velocity images that can recognize the depth and thickness of subsurface layers, and this conversion is usually achieved by using seismic full-waveform inversion (FWI). Various Deep neural networks (DNNs) have recently been proposed to replace FWI, and well-trained DNNs typically have lower computational costs but generate similar velocity images as compared to FWI. Yet, training a DNN model is non-trivial, which requires transmitting seismic data that are sensed by seismic receivers in the field to a centralized data center, leading to data privacy and security issues. Seismic field tests are normally conducted in rural areas, where data centers and Internet infrastructures are not available. Hence, it is impossible to train a DNN and achieve seismic inversion in real-time. In this paper, we propose the Asynchronous Federated Learning for Seismic Inversion (AsyncFedInv) framework, which applies multiple IoT devices in terms of edge computing boards to collaboratively train a compact UNet model in real-time based on a novel asynchronous federated learning, where 1) a staleness function is applied to mitigate model staleness, and 2) clients that generate similar local models would suspend its training, thus reducing the communication costs and energy consumption. Simulation results demonstrate that AsyncFedInv achieves a similar convergence rate but lower training loss and better testing performance as compared to a baseline algorithm FedAvg.

关键词： Seismic inversion federated learning asynchronous

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Power data Quality Assessment based on Entropy Weight Method and Grey System Theory 11

Power Data Quality Assessment based on Entropy Weight Method...

引用

11th ieee international conference on Cyber Security and Cloud Computing, CSCloud 2024

作者： Guo, Naiwang Shen, Quanjiang Liu, Chang Shanghai Electric Power Research Institute Shanghai China

ISBN: (纸本)9798350376982

With the increase in the scale of power systems, the development of measurement technology, and the decrease in costs, the amount of data in power systems is showing a rapid growth trend, gradually acquiring the characteristics of big data. Making full use of big data to improve the planning, operation, and control of power systems has received increasing attention. How to evaluate the quality of big data is an important issue worthy of study. There have been considerable research reports on data quality improvement techniques such as data cleaning, data integration, and similar record detection. However, research on data quality evaluation is still in its infancy. Against this background, a comprehensive evaluation method for the quality of power big data is proposed based on the characteristics of power systems and the quality characteristics of power big data. Firstly, an index system for evaluating the quality of power big data is constructed. Then, for the time-sensitive problem of big data processing, a MapReduce parallel K-means clustering algorithm is used to achieve fast preprocessing of big data sample sets. Afterwards, the objective weights of various data sets are calculated using entropy weight method, and the gray evaluation method is used to determine the level of data quality. On this basis, a comprehensive evaluation of sample data sets is realized. © 2024 ieee.

关键词： data assimilation

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Application of Artificial Intelligence-Based Big data Technology in Decision Support systems 2

Application of Artificial Intelligence-Based Big Data Techno...

引用

2nd ieee international conference on Sensors, Electronics and Computer Engineering, ICSECE 2024

作者： Ye, Yuan Wuhan Business University Hubei Wuhan China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373646

With the rapid development of data technology, artificial intelligence big data technology has demonstrated unprecedented importance and effectiveness in supporting decision systems. This paper focuses on how artificial intelligence big data technology is revolutionizing traditional decision support systems, particularly its impact and applications in data mining, information integration, and more. Through in-depth analysis of intelligent decision support databases and various information integration models, this research showcases how the technology optimizes the decision-making process, enhancing the accuracy and efficiency of decisions. Empirical studies further validate the effectiveness and potential of intelligent decision support systems in practical applications. Overall, this paper aims to provide readers with profound insights and understanding of this technological field, anticipating its broad application prospects in future decision support practices. © 2024 ieee.

关键词： data accuracy

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Towards A Framework For Farm-scale Digital Twins 27

Towards A Framework For Farm-scale Digital Twins

引用

ACM/ieee 27th international conference on Model Driven Engineering Languages and systems: Companion Proceedings (MODELS)

作者： Fakeye, Ireoluwa Maas, Ellen Harris, Paul Oulaid, Bader Baker, Chris Rothamsted Res Intelligent Data Ecosyst Harpenden Herts England Rothamsted Res Net Zero & Resilient Farming North Wyke England

ISBN: (纸本)9798400706226

Enhancing agricultural productivity while maintaining ecological balance amidst climate change is a looming challenge. The future of resilient farming and food security will depend upon the effectiveness of collecting, interpreting, and acting on data. An agricultural digital twin (DT) can provide a feedback loop which improves both farm management and the computer system which informs it through integrating right-time sensor data, process-based models (PBMs), data-driven models (DDMs), and hybrid approaches. Three demonstrator DTs for farm ecosystems are currently under development, utilizing extensive datasets from three instrumented research farms at the North Wyke Farm Platform in Devon, UK to drive and evaluate the accuracy of models in simulating key agroecosystem processes, such as soil nutrient cycling, water balance, and crop performance. The implementation process involves data collection, processing, model integration, and visualization. Key measurements are gathered up to every 15 minutes. PBMs along with DDMs and hybrid models will be utilized in an ensemble to enhance predictive accuracy and robustness. The DT architecture consists of three tiers. A client tier focuses on creating a user-friendly web frontend and API. An analysis and retrieval tier will facilitate the orchestration of services by a container registry and Kubernetes master node. A simulation tier will handle intensive data processing and model simulations with Apache Spark and high-performance computing nodes. We expect the DTs to improve decision-making, enhance system resilience against biotic and abiotic stresses, and pave the way for sustainable agricultural innovation.

关键词： Digital twin Information Management Framework modelling agriculture

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data-DRIVEN UNCERTAINTY REVENUE MODELING FOR COMPUTATION RESOURCE ALLOCATION IN RECOMMENDATION systems

DATA-DRIVEN UNCERTAINTY REVENUE MODELING FOR COMPUTATION RES...

引用

2024 Winter Simulation conference, WSC 2024

作者： Liu, Feixue Miao, Yuqing Ye, Yun Wang, Peisong Liu, Xinglu Zhang, Kai Chan, Wai Kin Tsinghua-Berkeley Shenzhen Institute Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Institute of Data and Information Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Ant Group Shanghai China

ISBN: (纸本)9798331534202

In recent years, as the resource consumption of computation-intensive recommendation systems (RS) significantly increased, and the supply of large-scale resources encountered bottleneck, computation resource allocation for improving computing efficiency grabbed the attention of the industry. The simulation of revenue is a focal point in the allocation problem. However, due to the complex engineering architecture of RS, no existing research proposes a simulation model that addresses the relationship between resource allocation strategies and benefits. This paper, based on real data from Alipay’s advertising RS and integrating queuing theory, models the relationship from resource allocation decisions to revenue considering exposure randomness of traffic. We further merge allocation tasks with capacity planning (CP) to establish a two-stage joint optimization model and use the revenue model above as the objective. The proposed model outperforms the baseline with 1.9% in revenue, and represents flexible adaptation of exposure rate, providing insights for the simulation of industrial RS. © 2024 ieee.

关键词： Queueing theory

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Task Allocation in Heterogeneous Multi-Robot systems Based on Preference-Driven Hedonic Game

Task Allocation in Heterogeneous Multi-Robot Systems Based o...

引用

ieee international conference on Robotics and Automation (ICRA)

作者： Zhang, Liwang Li, Minglong Yang, Wenjing Yang, Shaowu Natl Univ Def Technol Dept Intelligent Data Sci Coll Comp Sci & Technol Changsha 410073 Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Multiple preferences between robots and tasks have been largely overlooked in previous research on Multi-Robot Task Allocation (MRTA) problems. In this paper, we propose a preference-driven approach based on hedonic game to address the task allocation problem of muti-robot systems in emergency rescue scenarios. We present a distributed framework considering various preferences between robots and tasks to determine the division of coalitions in such problems and evaluate the scalability and adaptability of our algorithm through relevant experiments. Furthermore, considering the strict communication limitations in emergency rescue scenarios, we have verified that our algorithm can efficiently converge to a Nash-stable coalition partition even in conditions of insufficient communication distance.

关键词： Multipurpose robots

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Towards a novel data Mining System for Medication Error Management 20

Towards a novel Data Mining System for Medication Error Mana...

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20th ACS/ieee international conference on Computer systems and Applications (AICCSA)

作者： Touati, Hanae Thabet, Rafika Fontanili, Franck Cleostrate, Marie-Helene Cufi, Marie-Noelle Lamine, Elyes Toulouse Univ CGI IMT Mines Albi Albi France Sousse Univ ISITCom MARS Lab LR17RS05 Tunis Tunisia Intercommunal Hosp Ctr Castres France Toulouse Univ ISIS Engn INUC Castres France Toulouse Univ CGI IMT Mines Albi Castres France

ISBN: (纸本)9798350319439

Medication errors associated with the Medication Use Process present significant risks and require effective management. However, current practices do not address these risks. There is a lack of awareness that medication errors are unintended risks, and a lack of dedicated systems to manage them. To overcome these limitations, a digital system exploiting massive medical data (big data) is proposed. By integrating various data sources, this system aims to provide adaptable medication errors' management and continuous improvement. This article presents an overview of the system requirements and highlights the potential of data Mining in healthcare. Implementing this system could revolutionize medication error management and improve patient safety.

关键词： Medication Use Process Medication error Requirement data science Reporting Classification

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