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

共 138 条记录，以下是41-50 订阅

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Context-Aware Online IoT Service Scheduling Approach in Cloud Environments 7

Context-Aware Online IoT Service Scheduling Approach in Clou...

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7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering

作者： Wang, Xinyu Li, Han Zhao, Yun 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 North China University of Technology Beijing China

ISBN: (纸本)9781510656437

IoT applications consist of a series of correlated IoT services. Compared with traditional services, IoT services have new features in distribution, dependency, security and dynamism, which pose new challenges to the assurance of performance for cloud-based IoT services. Service scheduling on the cloud is an effective approach to guarantee service performance. Taking into account the complex contextual information of IoT services, this paper proposes a Context-Aware Online IoT Service Scheduling Approach in Cloud Environments. First, based on the characteristics of IoT services, a description of the IoT service scheduling problem is given, including the constraints of transforming the contexts of IoT services into scheduling. Then, the service scheduling problem is mapped to a scheduling plan prediction problem based on a neural network approach. To verify the effectiveness of the method, two baseline algorithms were selected and simulated on the WorkflowSim platform together with the method proposed in this paper. In comparison, our method is found to have similar scheduling costs with the optimization method, but can improve the efficiency when generating the scheduling plan, which can better meet the demands of low time delay for online scheduling under the conditions of dynamic changes in IoT context. © 2022 SPIE. All rights reserved.

关键词： Scheduling

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A data-driven Exploratory Service Composition Tool for data Scientists

A Data-driven Exploratory Service Composition Tool for Data ...

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2021 IEEE World Congress on Services, SERVICES 2021

作者： Chen, Gaojian Wang, Jing Li, Qianwen Yuan, Yunjing North China University Of Technology Beijing Key Laboratory On Integration And Analysis Of Large-Scale Stream Data Beijing China

ISBN: (纸本)9781665427197

In the era of big data, data scientists gain insights from data and make decisions. However, when constructing a data analysis pipeline, data scientists are often required to be proficient in multiple algorithm models and theoretical foundations, and they have to select and combine multiple algorithm models, and repeatedly adjust algorithms and parameters to construct a high-performance data analysis pipeline. In response to the above problems, this paper proposes an exploratory service composition tool, which can perform real-time service recommendations according to users' needs and data features, and assists users in constructing data analysis pipelines in an exploratory manner. This method can reduce the difficulty of data analysis, effectively save labor costs, and improve the quality and efficiency of data analysis. © 2021 IEEE.

关键词： Reinforcement learning

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Research on User Demand-Driven Service Matching Methodology 6

Research on User Demand-Driven Service Matching Methodology

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6th IEEE International Conference on Cloud Computing and Big data Analytics, ICCCBDA 2021

作者： Zhang, Huiying Yang, Dongju Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data North China University of Technology Beijing China

ISBN: (纸本)9780738105338

Services have seen a spike, and thus, how to accurately identify the needs of the users and match them with the appropriate services becomes the key to the research of service matching. Service matching involves a lot of work, of which the existing methods to complete the matching mainly depend on semantic similarity while the information of the service function is rarely taken into consideration. To solve the problem above, a user demand-driven method will be introduced. Based on the original service model, the user demand model is established, which aims at the actual needs of users. To cope with incomplete and inaccurate expressions of users' demands, the UserCF is applied to matching the similar needs of users so that the tag set is generated, making the user service tag information complete and modified;the LMIA is recommended to match the classified demands of users level by level with the purpose of weighted fusion. In the analysis of living examples, it is suggested that the method is more feasible than workable than the existing methods of service matching, and meanwhile, an 5.3% higher precision are obtained. © 2021 IEEE.

关键词： Semantics

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An Intent Understanding and Entity Extraction Method Based on Intent-Slot Attention Mechanism

An Intent Understanding and Entity Extraction Method Based o...

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Electronic Information Engineering and Computer Science (EIECS), 2021 International Conference on

作者： Sheng-Yuan Cao Chen Liu Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data School of Information North China University of Technology Beijing China

In the realm of chronic disease health dialog, natural language understanding plays a pivotal role. It encompasses two vital subtasks: recognizing user intentions and populating relevant data slots. To establish a symbiotic relationship between these intentions and slots, fostering semantic-level modeling, we present an innovative algorithm for comprehending medical consultation intentions and extracting pertinent entities. This algorithm leverages an intention-slot attention mechanism. Our approach introduces a slot pick-through mechanism, which effectively captures the explicit connection between user intent and data slots. Furthermore, we have designed an intent-slot attention mechanism layer and devised a method for embedding contextual intent information into data slots in the form of intent label vectors. In conclusion, our experimental results, evaluated on the MedDG dataset, demonstrate the superiority of our proposed algorithm over other comparative methods. We achieve an accuracy rate of 78.1% for medical intent recognition, an F1 value of 94.9% for semantic slot-filling, and a sentence-level semantic framing accuracy of 73.2%.

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Research on Construction Method of Science and Technology Service Domain Knowledge Map

Research on Construction Method of Science and Technology Se...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Jiaqi Qian 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

With the development of the science and technology service industry, there is an urgent need to effectively organize multi-source and heterogeneous science and technology resources to meet the personalized needs of science and technology services in different industrial fields, that is, to provide various services to different industrial fields according to industry perspectives. Required scientific and technological resources, including scientific and technological project resources and scientific and technological consulting resources, etc. Effective organization and management of scientific and technological resources can be completed by building a domain knowledge map for industrial industry scientific and technological services. However, these resource fields have a large number of data types, are highly professional, and have complex businesses. How to obtain useful data, information, and business knowledge from massive data and store it in a graph database to complete the construction of a knowledge graph is the subject of this article. Concerns. Therefore, this article proposes a set of methods that integrate data preprocessing and knowledge modeling based on structured data processing and ontology technology to complete the construction of domain knowledge graphs.

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Efficient Early Sparsification for Accelerating Solutions to the Traveling Salesman Problem 10

Efficient Early Sparsification for Accelerating Solutions to...

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10th International Conference on Fuzzy Systems and data Mining, FSDM 2024

作者： Jiang, Yonglu Xing, Hongyu Shi, Hanchen Wei, Zijing Yang, Zhongguo School of Information Science and Technology North China University of Technology Beijing China School of London Brunel North China University of Technology Beijing China Department of Mathematics Brunel University London London United Kingdom Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of Technology Beijing China

ISBN: (纸本)9781643685694

The Travelling Salesman Problem (TSP) is a well-established NP-hard problem, often tackled using heuristic algorithms. While state-of-the-art learning-driven approaches for TSP perform comparably to classical solvers when trained on trivially small instances, they struggle to generalize the learned policy to larger, practical-scale instances. Consequently, as the number of cities increases, the efficiency of these algorithms diminishes significantly. This paper focuses on the early elimination of non-advantageous edges while retaining advantageous ones, thereby accelerating the TSP solution. Our approach involves capturing the differential frequencies of various edges during the early stages of an intelligent evolutionary algorithm, which we term as "knowledge" derived from the evolutionary process. We then design effective features to characterize the similarities and differences between edges. Leveraging this "knowledge", we label edges as either advantageous or non-advantageous. By integrating the guidance of a predictor, we intervene in the intelligent evolutionary algorithm to sparsify the TSP graph, ultimately enhancing the TSP solution. Our method effectively prunes edges in TSP instances, preserving the optimal path while minimizing redundant edges. Experimental results demonstrate that our approach significantly improves both computational efficiency and solution quality on the TSP50 and TSP100 test sets compared to other models. This methodology offers a novel perspective by extracting and utilizing latent knowledge among edges, thereby enhancing the performance of intelligent evolutionary algorithms in solving the TSP. Our findings not only improve current methodologies but also encourage further exploration and development in related fields and practical applications. © 2024 The Authors.

关键词： Traveling salesman problem

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Web Service Clustering Method Based on Word Vector and Biterm Topic Model 6

Web Service Clustering Method Based on Word Vector and Biter...

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6th IEEE International Conference on Cloud Computing and Big data Analytics, ICCCBDA 2021

作者： Yang, Dongju He, Dan Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Data Engineering Institute North China University of Technology Beijing China

ISBN: (纸本)9780738105338

The increasing number of Web services brings challenges for accurate and efficient service discovery. Service clustering technology can narrow the retrieval range of service discovery and improve the efficiency of service discovery. However, due to the short text features with few words and difficult feature extraction in service description text, the traditional text clustering model can not get effective clusters. In this paper, a web service clustering method based on word vector and BTM (Biterm Topic Model) is proposed. Firstly, the sparse feature problem of short text is alleviated by expanding the word vector. Secondly, the extended service description text is modeled by the BTM based on Gibbs sampling. Finally, the k-means algorithm is used to cluster web services. Compared with LDA (Latent Dirichlet Allocation) and BTM, this method improves the F-Measure of clustering index. Experimental results show that the proposed method can effectively alleviate the problem of sparse service description text features and improve the clustering effect of services. © 2021 IEEE.

关键词： Web services

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Research on Ship Classification Method Based on AIS data 15th

Research on Ship Classification Method Based on AIS Data

引用

15th CCF Conference on Computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020

作者： Luo, Pu Gao, Jing Wang, Guiling Han, Yanbo Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China North China University of Technology Beijing China

ISBN: (纸本)9789811625398

It is important for maritime authorities to effectively identify and classify unknown types of ships in historical trajectory data. A method of using trajectory image and training based on deep residual network (ResNet) to obtain ship type classifier is proposed. First, a method of integrating speed information into the trajectory image is proposed, then the trajectory image is input into the ResNet model for training to obtain the classifier. Finally, the real AIS data of 5 ship types are used for experiments. The experimental results show that the method can meet the requirements of type identification and classification of ships based on AIS data, and provides technical support for further research on the identification of camouflaged ships, mining of ship behavior patterns and anomaly detection. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Ships

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

The establishment and adaptive iteration of service communit...

引用

Electronic Technology, Communication and Information (ICETCI), IEEE International Conference on

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

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.

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A Distributed Microservice Scheduling Optimization Method

A Distributed Microservice Scheduling Optimization Method

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Control, Electronics and Computer Technology (ICCECT), IEEE International Conference on

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

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.

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