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MaritimeDS: a data service framework for unsupervised maritime traffic monitoring based on trajectory big data

Journal of Reliable Intelligent Environments 2022年第1期8卷 3-19页

作者： Yang, Xuankai Wang, Guiling Gao, Jing 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 North China University of Technology Beijing100144 China

The large maritime traffic volume and its great impact on economy, environment and security make an unsupervised maritime traffic monitoring system in great need. Most of the current related research focuses on intelligent analysis or trajectory mining algorithms, while ignoring how to make good use of domain knowledge and model flexible software components, which leads to the related business functions implemented by IT staff through ad hoc coding. And what’s more, the lack of rich and detailed road information in the maritime transportation field makes it challenging to carry out diverse maritime monitoring business. This paper proposes a data service framework for unsupervised maritime traffic monitoring named MaritimeDS. The framework provides a unified domain model, in which the vessel trajectories are modeled through a layer-by-layer data model and maritime traffic structure is modeled as a spatial model. The highest layer is semantic trajectory, which is modeled based on the traffic structure generated through a novel T2I-CycleGAN model-based trajectory analysis service solving the problem of lacking paired training sets in maritime data. Case study and experiments show that compared with similar work, in the absence of detailed road information, IT staff can build maritime traffic structure, and carry out unified modeling and implementation of business functions related to traffic monitoring on this basis, which can improve development efficiency. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Trajectories

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Runtime reconfiguration of data services for dealing with out-of-range stream fluctuation in cloud-edge environments

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Digital Communications and Networks 2022年第6期8卷 1014-1026页

作者： Shouli Zhang Chen Liu Xiaohong Li Yanbo Han Division of Intelligence and Computing Tianjin UniversityTianjin300072China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of TechnologyBeijing10009China Cloud Computing Research Center North China University of TechnologyBeijing10009China

The integration of cloud and IoT edge devices is of significance in reducing the latency of IoT stream data processing by moving services closer to the *** this connection,a key issue is to determine when and where services should be *** service deployment strategies used to be static based on the rules defined at the design ***,dynamically changing IoT environments bring about unexpected situations such as out-of-range stream fluctuation,where the static service deployment solutions are not *** this paper,we propose a dynamic service deployment mechanism based on the prediction of upcoming stream *** effectively predict upcoming workloads,we combine the online machine learning methods with an online optimization algorithm for service deployment.A simulation-based evaluation demonstrates that,compared with those state-of-the art approaches,the approach proposed in this paper has a lower latency of stream processing.

关键词： IoT stream processing Edge computing Out-of-Range stream fluctuation Dynamic service deployment

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Improving IoT Services Through Business-Process-Aligned Modeling Method 16th

Improving IoT Services Through Business-Process-Aligned Mode...

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16th International Conference on Service Science, ICSS 2023

作者： Hu, Jianhang Wang, Guiling Wang, Hai Bai, Wentao Li, Junhua Yu, Jian Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China North China University of Technology Beijing100144 China Department of Computer Science Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9789819944019

In recent years, the Internet of Things (IoT) has facilitated the transformation of various traditional systems into a more convenient form of interaction and integration among people, machines, and things. In industry, there exists a significant class of process-oriented information systems, known as Process-aware Information Systems (PAIS), which are supported by business process management (BPM). The aim of business processes is to provide programming paradigms and business logic for IoT, while IoT can facilitate the quick and accurate capture of physical world changes by business processes, enabling them to respond in a timely manner. The heterogeneity in data and protocols among IoT physical entities, along with the continuous nature of their data, poses a challenge for traditional PAIS. These systems lack strict temporal semantics, and their business process event interaction mechanisms only apply to discrete events, making it difficult to efficiently handle continuous events. To address this challenge, this paper proposes a new IoT service model that has the capability to handle and respond to continuous IoT events, and enable the service to be bound to an executable element in a business process. By doing so, the IoT service can become one of the basic programming components for PAIS to respond promptly to changes in the physical world. The proposed model has been validated through case studies and experimental research, demonstrating its effectiveness and efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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A Novel Science and Technology Resource Recommendation Service based on Knowledege Graph and Collaborative Filtering

A Novel Science and Technology Resource Recommendation Servi...

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2022 CCF International Conference on Service Science, CCF ICSS 2022

作者： Zhao, Xinyu Liu, Chen Zhang, Shuo You, Xin North China University of Technology School of Information Science and Technology Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China

ISBN: (数字)9781665498616

ISBN: (纸本)9781665498616

To address the problems of large volume of science and technology information, low information value density, and matrix sparsity of recommendation algorithms, we propose STIR-KG, a science and technology information recommendation method integrating knowledge graph, and build a science and technology information recommendation service. The main contributions are: (1) Establishing a new material knowledge graph, which has been open-sourced in GitHub (2) Combining collaborative filtering methods with knowledge graphs to solve the cold-start and matrix sparsity problems. (3) Propose the representation learning method TransAR, which enhances the representation capability compared with traditional methods, and uses the Mahalanobis distance metric score function to reduce the influence of irrelevant dimensions on the similarity calculation. (4) Based on the STIR-KG method, we use the streaming computing framework Flink to build a recommendation service for scientific and technical information, which captures user interest migration in real time and makes the recommendation results more time-efficient. And according to the experimental verification, STIR-KG has significantly improved the accuracy and recall rate compared with other algorithms. © 2022 IEEE.

关键词： Collaborative filtering

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An Internet of Things based scalable framework for disaster data management

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Journal of Safety Science and Resilience 2022年第2期3卷 136-152页

作者： Zhiming Ding Shan Jiang Xinrun Xu Yanbo Han Institute of Software Chinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China North China University of Technology Beijing 100144China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data BeijingChina

In recent years, undesirable disasters attacked the cities frequently, leaving heavy casualties and serious economiclosses. Meanwhile, disaster detection based on the Internet of Things(IoT) has become a hot spot that benefitedfrom the established development of smart city construction. And the IoT is visibly sensitive to the managementand monitoring of disasters, but massive amounts of monitoring data have brought huge challenges to datastorage and data analysis. This article develops a new and much more general framework for disaster emergencymanagement under the IoT environment. The framework is a bottom-up integration of highly scalable Raw dataStorages(RD-Stores) technology, hybrid indexing and queries technology, and machine learning technology foremergency disasters. Experimental results show that hybrid index and query technology have better performanceunder the condition of supporting multi-modal retrieval, and providing a better solution to offer real-time retrievalfor the massive sensor sampling data in the IoT. In addition, further works to evaluate the top-level sub-applicationsystem in this framework were performed based on the GPS trajectory data of 35,000 Beijing taxis and thevolumetric ground truth data of 7,500 images. The results show that the framework has desirable scalability andhigher utility.

关键词： Disaster data management IoT Disaster detection Big data Artificial intelligence

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Emergency logistics scheduling with multiple supply-demand points based on grey interval

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Journal of Safety Science and Resilience 2022年第2期3卷 179-188页

作者： Zhiming Ding Xinrun Xu Shan Jiang Jin Yan Yanbo Han University of Chinese Academy of Sciences Beijing100049China Institute of Software Chinese Academy of Sciences Beijing050052China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing100124China North China University of Technology Beijing100144China

This study aimed to address the problem of post-disaster emergency material dispatching from multiple supply points to multiple demand *** large-scale natural disasters,it is very important for multiple emergency material supply points to serve as sources of materials for multiple disaster sites and to determine emergency material scheduling solutions ***,the quantity of emergency materials required at each disaster site is *** address this issue,in this study,we developed an emergency material scheduling model with multiple logistics supply points for multiple demand points based on the grey interval *** optimize the proposed multi-supply-point and multi-demand-point emergency material scheduling mode,a multi-objective optimization algorithm based on a genetic algorithm was *** results demonstrate that the multi-objective optimization method can solve the emergency logistics scheduling problem better than the particle swarm optimization multi-objective solution ***,the multi-supply point and multi-demand point emergency material dispatch model and optimization algorithm provides robust support for emergency management system decision-makers when they need to respond quickly to disaster relief activities.

关键词： Emergency logistics scheduling Multi-objective programming Disaster relief

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A microservice fault identification method based on LightGBM 8

A microservice fault identification method based on LightGBM

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8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022

作者： Jing, Ning Li, Han Zhao, Zhuofeng North China University of Technology College of Information Beijing100144 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing100144 China

ISBN: (纸本)9781665477352

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. © 2022 IEEE.

关键词： Software design

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Spatial-Temporal Aware Business Event Forecasting for Proactive Services from IoT Sensory data 26

Spatial-Temporal Aware Business Event Forecasting for Proact...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhang, Jiayu Wang, Guiling Shi, Yongpeng Zheng, Xin Yu, Jian 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

ISBN: (纸本)9798350331684

With the development of IoT and AI, better knowledge and information can be learned and extracted from IoT sensory data which enables business systems to proactively provide services to customers. This paper is the first study that attempts to forecast high-level business events from raw IoT sensory event data to improve the proactivity of services and applications. We propose a deep learning based business event forecasting framework, i.e., IoT2BE, which extracts prior knowledge to identify business events from IoT sensory data, extracts features in multi-views including the spatial and temporal view, generates spatio-temporal business event embeddings, and uses a seq2seq model with attention to predict the future business events. Extensive experiments are based on two datasets including one real-world maritime ship trajectory dataset and one publicly available raw sensor dataset from a smart home environment. The results demonstrate that our framework can be effectively applied in various business scenarios. © 2023 IEEE.

关键词： Forecasting

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A Time-series Prediction Algorithm Based on a Hybrid Model

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Recent Advances in Computer Science and Communications 2023年第1期16卷 3-17页

作者： Cao, Danyang Ma, Jinfeng Sun, Ling Ma, Nan 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 Beijing100144 China National Satellite Meteorological Center Beijing100081 China Key Laboratory of Radiometric Calibration and Validation for Environmental Satellite Beijing100081 China College of Robotics Beijing Union University Beijing100101 China

Background: In reality, time series is composed of several basic components, which have linear, nonlinear and non-stationary characteristics at the same time. Directly using a single model will show some limitations and the prediction accuracy is difficult to improve. Methods: We propose a mixed forecasting model based on time series decomposition, namely STL-EEMD-LSTM model. First, we use STL filtering algorithm to decompose the time series to obtain the trend component, seasonal component and the remainder component of the time series;then we use EEMD to decompose the seasonal component and the remainder component to obtain multiple sub-sequences. After this, we reconstruct the new seasonal component and the remainder component according to the fluctuation frequency of the sub-sequence. Finally, we use LSTM to build a prediction model for each component obtained by decomposition. Results: We applied the proposed model to simulation data and the time series of satellite calibration parameters and found that the hybrid prediction model proposed in this paper has high prediction accuracy. Conclusion: Therefore, we believe that our proposed model is more suitable for the prediction of time series with complex components. © 2023 Bentham Science Publishers.

关键词： Time series

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Online Knowledge Fusion Method for Fault Diagnosis of Power Plant Equipment 11

Online Knowledge Fusion Method for Fault Diagnosis of Power ...

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11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Xin, You Chen, Liu Yang, Yang School of Information North China University of Technology Beijing100144 China Beijing Key Laboratory of Large-scale Stream Data Integration and Analysis Technology Beijing100144 China Beijing Huaxia Qixin Technology Co. Ltd China

ISBN: (纸本)9798350333664

There are many types of documents in fossil-fuel power station to describe equipment failures, including maintenance records, treatment diagnosis suggestions, historical cases, and equipment knowledge. The knowledge of equipment anomaly diagnosis and handling is scattered in different documents. Extract and fuse scattered knowledge from these scattered documents to generate a knowledge graph for equipment fault di-agnosis, providing necessary decision support for maintenance personnel to discover and handle equipment faults. This article proposes an implementation method for extracting and integrating equipment fault knowledge from diverse and multi type text records to form a knowledge graph. A thermal power plant equipment fault Q&A system based on the fusion of open source large language models and knowledge graphs has been developed. Main contributions: (1) A knowledge extraction algorithm integrating BERT-WWM model and pointer annotation method is proposed to extract entity relations of fault text jointly. Experiments show that the method performs well in extracting overlapped triples, and F1 is improved by 8.51 % compared with existing algorithms;(2) A knowledge fusion model based on RoBERTa-BiLSTM is proposed, which fully utilizes the feature information of the entity text to be disambiguated and the entity mention text, and cap-tures the interdependent features within the sentence through attention mechanism. The experiment shows that this method improves F1 by 9.56% compared to existing fusion algorithms. (3) Based on the open-source large model ChatGLM, a fusion method of knowledge graph and large model ChatGLM was explored, and a device fault question answering system for thermal power plants was implemented, achieving high accuracy in practical applications. © 2023 IEEE.

关键词： knowledge extraction knowledge fusion knowledge graph

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