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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51-60 订阅

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Distributed log collection for business processes based on ELK architecture

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

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Electrical Engineering, Big data and Algorithms (EEBDA), IEEE International Conference on

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

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.

关键词： Electrical engineering Codes Microservice architectures Writing Maintenance engineering Load management Real-time systems

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Improved NSGA-II based process dynamic multi-objective algorithm

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

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

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

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.

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Efficient Time Series Predicting with Feature Selection and Temporal Convolutional Network 4

Efficient Time Series Predicting with Feature Selection and ...

引用

4th IEEE International Conference on Computer and Communication Engineering Technology, CCET 2021

作者： Li, Xiaoqi Liu, Chen He, Yiru North China University of Technology Beijing Key Laboratory Integration and Analysis of Large-Scale Stream Data School of Information Science and Technology Beijing China

ISBN: (纸本)9781665438902

TCN (Temporal Convolutional Network) has been proved that it outperforms canonical recurrent networks across different tasks and demonstrates longer effective memory. Besides, it speeds up the prediction with parallel calculation. The Granger causality test is used to detect temporal causality between variables to select the most influential features. In this paper, we adopt the Granger causality test and introduce TCN with two-dimensional convolution kernel to predict the temperature of superheater in thermal power plants. To reduce the noise of dataset, we use the Granger causality test to select features which have the greatest impact on the temperature prediction. The challenge of efficiency is addressed by applying the TCN with two-dimensional convolution kernel. To evaluate our method, we conducted experiments on time series predicting with the real-sensor data from thermal power plant. Compared with canonical recurrent network, our method has improved the predicting ability in terms of accuracy and efficiency. © 2021 IEEE.

关键词： Time series

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A Neural Network Based High-Rise Building Block Detection 4

A Neural Network Based High-Rise Building Block Detection

引用

4th IEEE International Conference on Computer and Communication Engineering Technology, CCET 2021

作者： Shu, Jie Guan, Yanjie Wang, Renxiang North China University of Technology School of Information Science and Technology Beijing China Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing China

ISBN: (纸本)9781665438902

High-rise building block detection is very useful for urban morphology analysis, such as urban wind field analysis. However, in previous studies, high-rise buildings are determined by data from several different sources. This paper presents a new method of high-rise building block detection based only on high-resolution satellite images. It first detects the potential regions of high-rise building blocks through a neural network. Then a filtering algorithm is used to distinguish these potential regions to obtain final high-rise building blocks. Experimental results show that this method is much better than other methods in detecting high-rise building blocks, with F1-score 0.87. This method also gives the possibility of using only a single source of data to detect high-rise buildings. © 2021 IEEE.

关键词： Morphology

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"Failure" Service Pattern Mining for Exploratory Service Composition 17th

"Failure" Service Pattern Mining for Exploratory Service Com...

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17th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Yuan, Yunjing Wang, Jing Han, Yanbo Li, Qianwen Chen, Gaojian Jiao, Boyang Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data School of Information Science and Technology No.5 Jinyuanzhuang Road Beijing Shijingshan District China

ISBN: (纸本)9783030926342

To adapt to uncertain and dynamic requirements, exploratory service composition enables business users to construct service composition processes in a trial-and-error manner. A large number of service composition processes are generated, which can be learned to improve the reusability of the service composition processes. By mining these service composition processes and abstracting the mining results to service patterns, the efficiency of service composition can be effectively improved. At present, there has been some research on the methods of service pattern mining. Most of the work focuses on successful service composition processes, but the ones that fail are also valuable. For example, by using the mining results of failure service composition processes, the accuracy of service recommendations can be improved. To solve this problem, this paper proposes a "failure" service pattern mining algorithm (FSPMA) for exploratory service composition, which extends the gSpan algorithm, and can mine "failure" service patterns from service composition processes for further reuse. Meanwhile, the exploratory service composition model and the service pattern model are explained for the FSPMA. The prototype implementation of the exploratory service composition environment is introduced, which integrates the FSPMA. The experimental evaluation is explained to verify the algorithm, and the result shows that the efficiency of the FSPMA has a significant improvement in mining "failure" service patterns compared with the gSpan algorithm and the TKG algorithm. Finally, the application of "failure" service patterns in service recommendations is given. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Reusability

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In-Air Handwriting Recognition Using Acoustic Impulse Signals 19th

In-Air Handwriting Recognition Using Acoustic Impulse Signal...

引用

19th International Conference on Smart Living and Public Health, ICOST 2022

作者： Niu, Kai Zhang, Fusang Fu, Xiaolai Jin, Beihong Peking University Beijing China Beijing Xiaomi Mobile Software Co. Ltd. Beijing China Institute of Software Chinese Academy of Science Beijing China Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China

ISBN: (纸本)9783031095924

This paper presents AcousticPAD, a contactless and robust handwriting recognition system that extends the input and interactions beyond the touchscreen using acoustic signals, thus very useful under the impact of the COVID-19 epidemic. To achieve this, we carefully exploit acoustic pulse signals with high accuracy of time of fight (ToF) measurements. Then we employ trilateration localization method to capture the trajectory of handwriting in air. After that, we incorporate a data augmentation module to enhance the handwriting recognition performance. Finally, we customize a back propagation neural network that leverages augmented image dataset to train a model and recognize the acoustic system generated handwriting characters. We implement AcousticPAD prototype using cheap commodity acoustic sensors, and conduct extensive real environment experiments to evaluate its performance. The results validate the robustness of AcousticPAD, and show that it supports 10 digits and 26 English letters recognition at high accuracies. © 2022, The Author(s).

关键词： Backpropagation

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Service-Based Event Penetration from IoT Sensors to Businesses: a Case Study

Service-Based Event Penetration from IoT Sensors to Business...

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2022 CCF International Conference on Service Science, CCF ICSS 2022

作者： Wang, Guiling Fang, Jun Wang, Jing Yu, Jian Zhang, Liang Han, Yanbo Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China North China University of Technology School of Information Science and Technology Beijing China Auckland University of Technology Department of Computer Science Auckland New Zealand Shanghai Key Laboratory of Data Science Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China

ISBN: (数字)9781665498616

ISBN: (纸本)9781665498616

By leveraging IoT Big data, BPM can gain real-time physical world information to make faster and more accurate decisions, but there is a technical gap between IoT sensors and businesses. To bridge the gap, an event penetration mechanism from IoT sensors to business processes is proposed along a practical case study. This paper presents a concrete IoT-BPM application case dealing with seaborne safety ensurance in transporting liquefied natural gas (LNG), analyzes its technical challenges, and examines the feasibility and supposed effects of the BRIBOT approach. © 2022 IEEE.

关键词： Internet of things

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A Novel Heuristic Method for Emergency Path Planning Based on Dynamic Spatial-Temporal Characteristics Map

A Novel Heuristic Method for Emergency Path Planning Based o...

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2020 International Conference on Industrial Applications of Big data and Artificial Intelligence, BDAI 2020

作者： Yang, Bowen Yuan, Lei Yan, Jin Ding, Zhiming Cai, Zhi Guo, Limin Beijing University of Technology Beijing100124 China Institute of Software Chinese Academy of Sciences Beijing100190 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing100144 China

Emergency path planning technology is one of the hot research points in intelligent transportation systems. There are many methodologies and applications in emergency path planning. However, due to the complexity of the urban network and crowded road conditions, the difficulty of emergency path planning. The objective of emergency path planning is to get the vehicle out of the emergency areas and to its destination in the shortest time. Road congestion caused by emergency situations in cities directly affects the original road network structure. Then the weight of the original road network is no longer suitable as a basis for path recommendation and the value of edges of weight will change over time. To handle the dynamic road network, a novel situational time-stamp heuristic search algorithm (STH) is introduced for the situation space. This algorithm can effectively solve the problem of diversity of situational networks. STH can build a heuristic that adapts to time changes based on the map refresh time, and ensures that the path given in the time window T is optimal. Moreover, STH can give a pruning strategy according to the search time window T, which significantly improves the efficiency of the algorithm. Finally, the path planned by STH is better than the baseline algorithm. © Published under licence by IOP Publishing Ltd.

关键词： Motion planning

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AISCW: Artificial Intelligence Supported Cooperative Work The New Frontier of CSCW 19th

AISCW: Artificial Intelligence Supported Cooperative Work Th...

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19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Shi, Meilin Wang, Guiling Zhang, Shaohua Jiang, Jinlei Xiang, Yong Computer Science Department Tsinghua University Beijing100084 China 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 Beijing100144 China Camford Royal School Beijing102211 China

ISBN: (纸本)9789819623723

Under today’s natural environmental, social and technological conditions, the global and large-scale challenges faced by humanity are becoming increasingly significant, making collaborative work increasingly important. Computer-Supported Cooperative Work (CSCW) is anticipated to assume a more pivotal role in modern society and within the fabric of production and daily life. In recent years, Artificial Intelligence has witnessed remarkable advancements, and the "cooperative intelligence" of AI systems has been enhanced. The role of computers in supporting human collaboration is no longer limited to an auxiliary role but also serves as a cooperative role, bringing new challenges to the study of CSCW, ranging from basic collaborative theories to key technologies. This paper proposes a new frontier in CSCW, AI Supported Cooperative Work (AISCW) and its system research. The objective is to take use of AI technologies to enhance and optimize human collaborative activities. By delving into the interplay between CSCW and AI, this paper outlines the foundational concepts, key issues and potential research focal points of AISCW. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： AISCW Cooperative AI CSCW

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A microservice fault identification method based on LightGBM

A microservice fault identification method based on LightGBM

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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Ning Jing Han Li Zhuofeng Zhao College of Information North China University of Technology Beijing China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing China

ISBN: (纸本)9781665456579

With the development of cloud computing, the software system architecture has gradually changed from a single architecture to a service-oriented architecture, of which microservice architecture is a typical representative. It is committed to providing users with more reliable, maintainable, and extensible software design services. Although the microservice architecture has many advantages, because there are multiple services in the microservice architecture, it becomes more difficult to detect faults when the system fails. How to efficiently detect the causes of faults is the key technology to ensure the performance and reliability of microservices. Aiming at this problem, this paper proposes a microservice fault identification method based on LightGBM method, which can analyze the historical operation information of microservices, learn and locate the fault causes, be used for fault identification, can quickly locate faults, and ensure the high availability of microservices. Compared with GBDT and XGBoost methods, the experimental results show that the accuracy of this method is 0.85, the recall rate is 0.81, and the F1 score is 0.83. Compared with other fault detection models, this method improves and can effectively detect abnormal services and identify fault microservices.

关键词： Fault diagnosis Cloud computing Software design Computational modeling Microservice architectures Computer architecture Prediction algorithms

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