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检索条件"机构=Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data"

共 139 条记录，以下是11-20 订阅

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The establishment and adaptive iteration of service community based on context association mining 3

The establishment and adaptive iteration of service communit...

引用

3rd IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Wang, Lijun North China University of Technology Beijing Key Laboratory on Integration and Analsis of Large-Scale Stream Data Beijing China

ISBN: (纸本)9798350398410

The scale and types of service resources on the internet are becoming increasingly complex, and providing services for users' personalized needs is a research hotspot. This article proposes a user-centered service community concept to create a service community that can meet their needs. Existing methods for constructing service communities typically cannot meet the requirements of a user-centered service community as proposed in this article. The main method for creating a user-centered service community is to analyze the QoS data of users' historical service calls and incorporate temporal and spatial contexts into a collaborative filtering algorithm to obtain resources that are relevant to the user. The obtained resources are then added to the service community. Finally, the community mean square error is used to verify the effectiveness and feasibility of the algorithm. In order to ensure the dynamic nature of the service community and consider changes in user needs, this article defines two rules to trigger adaptive iteration and updates the resources of the service community based on the triggering rules. © 2023 IEEE.

关键词： Collaborative filtering

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An Evolving Transformer Network Based on Hybrid Dilated Convolution for Traffic Flow Prediction 19th

An Evolving Transformer Network Based on Hybrid Dilated Con...

引用

19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Yu, Qi Ding, Weilong Sun, Maoxiang Huang, Jihai School of Information Science and Technology Beijing100144 China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing100144 China Zhengzhou University of Technology Zhengzhou450044 China

ISBN: (纸本)9783031545306

Decision making based on predictive traffic flow is one of effective solutions to relieve road congestion. Capturing and modeling the dynamic temporal relationships in global data is an important part of the traffic flow prediction problem. Transformer network has been proven to have powerful capabilities in capturing long-range dependencies and interactions in sequences, making it widely used in traffic flow prediction tasks. However, existing transformer-based models still have limitations. On the one hand, they ignore the dynamism and local relevance of traffic flow time series due to static embedding of input data. On the other hand, they do not take into account the inheritance of attention patterns due to the attention scores of each layer’s are learned separately. To address these two issues, we propose an evolving transformer network based on hybrid dilated convolution, namely HDCformer. First, a novel sequence embedding layer based on dilated convolution can dynamically learn the local relevance of traffic flow time series. Secondly, we add residual connections between attention modules of adjacent layers to fully capture the evolution trend of attention patterns between layers. Our HDCformer is evaluated on two real-world datasets and the results show that our model outperforms state-of-the-art baselines in terms of MAE, RMSE, and MAPE. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Convolution

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Improved NSGA-II based process dynamic multi-objective algorithm

Improved NSGA-II based process dynamic multi-objective algor...

引用

2023 IEEE International Conference on Control, Electronics and Computer Technology, ICCECT 2023

作者： Zhang, Yaze North China University of Technology Beijing Key Laboratory on Integration and Analysis of Large-Cale Stream Data Beijing China

ISBN: (纸本)9798350399028

The NSGA-II-T algorithm is proposed to solve the scheduling problem with dynamic changes in process data processing in the service Internet, modelling the problem as a dynamic multi-objective optimisation problem. In the event of regular changes in load, the solution in the next environment is predicted by the transformer model and added to the original population;in the event of changes caused by physical factors such as node downtime, the population is added randomly. The algorithm uses different means of adding populations to address the lack of diversity that can occur in static multi-objective algorithms when the environment changes. Experiments conducted on the WorkflowSim platform show that the NSGA-II-T algorithm outperforms other classical algorithms in all performance metrics when the environment changes significantly, demonstrating the effectiveness of the algorithm. © 2023 IEEE.

关键词： Multiobjective optimization

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A Dynamic Change Mechanism for Business Process in Science and Technology Service Domain 16th

A Dynamic Change Mechanism for Business Process in Science...

引用

16th International Conference on Service Science, ICSS 2023

作者： Lu, Shuangyu Ding, Weilong Tian, Han Yang, Kunfeng Zhao, Zhuofeng School of Information Science and Technology North China University of Technology Beijing China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China

ISBN: (纸本)9789819944019

How to successfully implement a dynamic change for business process during execution is a crucial issue that must be resolved in the application of science and technology service. However business process modification research in this domain is insufficient. The traditional approaches redefine the process to adapt to changing business or technical circumstances, the entire procedure takes too long and is inefficient. Aiming at the challenge of business process change in science and technology service application, we propose a business process change mechanism - dynamic change based on BPMN extended language and process validation method. The dynamic change mechanism may realize adding services, rolling back services, and jumping services in business processes. Ultimately, we use three science and technology service cases to verify that the mechanism is feasible in practical application. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Dynamics

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An IoT Service Development Framework Driven by Business Event Description 20th

An IoT Service Development Framework Driven by Business Eve...

引用

Proceedings of the 20th Web Information Systems and Applications Conference, WISA 2023

作者： Li, Junhua Wang, Guiling Hu, Jianhang Wang, Hai Zhang, Haoran Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China School of Information Science and Technology North China University of Technology Beijing China

ISBN: (纸本)9789819962211

In order to enable the integration between Business Process Management (BPM) and the Internet of Things (IoT), it is imperative to create an IoT service capable of encapsulating IoT device resources, converting raw data generated by IoT devices into high-level business events and transmitting these events to BPM. This would serve as a means of augmenting the intelligence and automation of business processes. Custom development of parsing and computation modules is required for different streaming data, and IoT services are built based on descriptions of business events. However, this process often involves a lot of repetitive work, and these valuable tasks are difficult to meet the requirements of changing data demands in different IoT scenarios. To solve this problem, this study proposes a code generation development framework for real-time stream data processing application, driven by business event descriptions in IoT scenarios. The framework can automatically generate real-time stream data processing applications that meet data processing needs based on domain data requirements described by business personnel in the IoT scenario. Through relevant experiments, the designed development framework demonstrates its potential to help non-software developers quickly configure Flink-based stream computing applications for scenarios and has a certain flexibility to adapt to real-time stream data processing requirements in common IoT scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Distributed log collection for business processes based on ELK architecture 2

Distributed log collection for business processes based on E...

引用

2nd IEEE International Conference on Electrical Engineering, Big data and Algorithms, EEBDA 2023

作者： Zong, Yize North China University of Technology Beijing Key Laboratory on Integration and Analysis of Large-cale Stream Data Beijing China

ISBN: (纸本)9781665462532

When it comes to executing service solutions in a distributed environment, the service solution is considered as a workflow split into many sub-processes that are scheduled to run independently and autonomously on each processing engine. Each organization's machines have its own set of limitations. They can't write too much code or rebuild the system, so they'll have to rely on a lightweight, low-code strategy to expand the service solution's process activities to collect valuable logs. As a result, we created a log collection system for the case, as mentioned earlier. The listener class is used to insert some functional code that sends the logs to the messaging middleware. Then, the service call records, or log data, are transmitted to the central administration platform. The ELK (Elasticsearch, Logstash, Kibana) framework, commonly used in log analysis nowadays, is then used to perform real-time data processing. Finally, the logs are visually displayed to provide intuitive feedback on log information and to make system maintenance and adjustment easier. © 2023 IEEE.

关键词： Middleware

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A Distributed Microservice Scheduling Optimization Method

A Distributed Microservice Scheduling Optimization Method

引用

2023 IEEE International Conference on Control, Electronics and Computer Technology, ICCECT 2023

作者： Li, Han Zhao, Yun Liu, Zhenxiong Liu, Wei North China University of Technology Beijing Key Laboratory on Integration and Analysis of Large-Cale Stream Data Beijing China

ISBN: (纸本)9798350399028

In the current Internet of Things environment, factors affecting scheduling are gradually increasing, and service performance is affected. There may be service delays, service non-response, and service feedback error messages. At the same time, service scheduling needs to be continued. Therefore, the scheduling The algorithm puts forward higher requirements. This paper proposes a scheduling algorithm based on depth-determined policy gradient (DDPG). By combining deep learning and reinforcement learning, it solves the problem of continuous action control and constructs a policy network and a value network. Double network structure to solve the problem of slow algorithm convergence. Secondly, this paper designs a microservice-based visual monitoring system to obtain the experimental data required by the algorithm and verify the effectiveness of the algorithm. Finally, 30 sets of data collected by the monitoring system are used to test and analyze the resource balance and service delay on the node in combination with the constraints of service and data dependency, and compare the delay and resource utilization with three typical algorithms. Experiments show that DDPG can show better performance under various constraints of the complex environment of the Internet of Things. © 2023 IEEE.

关键词： Scheduling algorithms

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Enhancing IoT Service Interface Through AsyncAPI with Extensions 16th

Enhancing IoT Service Interface Through AsyncAPI with Exten...

引用

16th International Conference on Service Science, ICSS 2023

作者： Wang, Hai Wang, Guiling Gao, Jing Hu, Jianhang Li, Junhua Zhang, Haoran School of Information Science and Technology North China University of Technology Beijing100144 China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China

ISBN: (纸本)9789819944019

In recent years, various Internet of Things (IoT) applications have penetrated into various areas of human life. In the IoT environment, various IoT service systems perform task collaboration by integrating with a large number of heterogeneous software and hardware devices in an open environment, providing intelligent services to the outside world. However, traditional service applications, when facing IoT with massive data, cannot meet the high real-time requirements of modern applications by relying on a pull-based response method, which also increases system operation pressure. Therefore, an event-driven architecture is adopted to provide IoT services, and service users only need to focus on how to determine the subsequent process based on event content. In this paper, we propose an IoT service interface model based on AsyncAPI, which provides a static expression of service for service subscribers, effectively expresses various operations, and makes IoT service usage more flexible. Secondly, we extend AsyncAPI to define a unified IoT service interface specification, solved the problem of protocol heterogeneity in event transmission. And complete the related code generation mechanism for the transmission of event information between systems. Finally, we apply this interface model to IoT service usage and conduct experimental verification, demonstrating the effectiveness of the IoT service interface model in event-driven communication. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Named Entity Recognition Service of Bert-Transformer-CRF Based on Multi-feature Fusion for Chronic Disease Management 16th

Named Entity Recognition Service of Bert-Transformer-CRF Ba...

引用

16th International Conference on Service Science, ICSS 2023

作者： Chen, Diya Liu, Chen Zhao, Zhuofeng School of Information North China University of Technology Beijing100144 China Beijing Key Laboratory of Large-scale Stream Data Integration and Analysis Technology Beijing100144 China

ISBN: (纸本)9789819944019

In chronic disease management, there are many kinds of chronic diseases, the professional terms are complex and diseases have their own characterised disease descriptions, making it difficult for some existing service recommendation methods to make accurate and personalised recommendations for patients with chronic diseases. Therefore, the paper proposes a service recommendation method based on the BERT-Transformer-CRF named entity recognition technology (BTC-SR) to achieve more accurate recommendation services. Firstly, the input disease text data is identified by a BERT (Bidirectional Encoder Representations from Transformers)-Transformer-CRF (Conditional Random Fields) model incorporating radical and pinyin features for named entities, then the relationships between the entities are extracted, and finally an implicit representation of the user is combined to present a recommendation list for the user. Experiments show that the proposed model achieves an F1 value of 60.15 for entity recognition on the CMeEE dataset, which provides better recognition results and lays the foundation for more accurate service recommendations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diseases

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Attention-Based Inductive Graph Neural Networks for Spatiotemporal Kriging

Attention-Based Inductive Graph Neural Networks for Spatiote...

引用

International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Chang, Wei Fang, Jun Zhao, Hongxia Zhang, Haibo Beijing Key Laboratory On Integration and Analysis of Large-Scale Stream Data Beijing China North China University of Technology Beijing100144 China State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9789819639601

Due to the high operation and maintenance cost of urban road monitoring equipment, the number of road installation equipment is limited, so it is necessary to use observable data and its potential spatiotemporal dependence to estimate the data of no sensor location to solve the problem of data sparsity, so as to realize the macro management of the road network. The main challenge of this problem is how to effectively model and exploit the spatiotemporal dependencies in the data. The existing models mainly use GNNs to model the spatiotemporal dependence, but this only considers the local spatiotemporal relationship of each node. Therefore, this paper proposes an AIGNN model, which aggregates the information of local neighbor nodes through graph neural network, and then captures the global spatiotemporal dependencies of each node through the attention mechanism, and uses the induction strategy to train the model to solve the spatiotemporal Kriging task. Experiments are carried out on three real-world spatiotemporal datasets, and the results show that AIGNN has performance advantages over the traditional linear model and the GNN-based Kriging model. © beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Graph neural networks

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