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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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H-MGSR: A Hierarchical Motif-based Graph Attention Neural Network for Service Recommendation

H-MGSR: A Hierarchical Motif-based Graph Attention Neural Ne...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Zheng, Xin Wang, Guiling Zhang, Jiayu Zhang, Yuqi Wang, Nancy Yu, Jian 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

ISBN: (纸本)9798350304855

The rapid development of web services has made it increasingly challenging for developers to find desired web services. To address this issue, researchers have developed various powerful models for service recommender systems. Recently, graph neural networks have shown promising performance in various deep learning tasks including service recommendation. This paper proposes a novel graph neural network for web service recommendation using a hierarchical attention mechanism that combines a node-level and a motif-level attention mechanisms. The node-level attention mechanism is responsible for aggregating information by the importance of different neighbors, while the motif-level attention mechanism performs a weighted combination of the node embeddings generated from different motif adjacency matrices. Finally, the generated node embeddings are optimized by the multi-layer perceptron (MLP), which in turn provide recommendations. Experimental results on real-world datasets demonstrate that our proposed model outperforms state-of-the-art approaches. Additionally, we conduct a model analysis to investigate the importance of different motifs. Overall, our proposed method shows promising performance for web service recommendation and highlights the potential of using graph neural networks in this domain. © 2023 IEEE.

关键词： Graph neural networks

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A Dynamic Binding Method for Situation-aware IoT Services Targeting Proactive BPM 26

A Dynamic Binding Method for Situation-aware IoT Services Ta...

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

作者： Li, Xiuxian Wang, Guiling Shi, Yongpeng 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

By leveraging IoT data, Business Process Management (BPM) systems can sense the physical world situation and make more accurate decisions proactively, but the current BPM systems lack the effective method to take good use of IoT data. In this paper, a situation-aware dynamic binding method for IoT services targeting proactive BPM is proposed to meet this requirement. The method first establishes a prediction model to predict the bindable IoT services under given situations by constructing an encoder-decoder structure network model using bi-directional gated recurrent units (Bi-GRU) and incorporating an attention mechanism. Then our method creates a Dynamic IoT Service Task (DIT) model for BPM. This enables the BPM system to integrate the prediction model with dynamic switching of IoT services and a parallel running architecture for multiple IoT services. A prototype system is developed and a case study is conducted to evaluate the performance of the method. The study focuses on the safety supervision scenario for the transportation of hazardous Liquefied Natural Gas (LNG) by sea. Results show that the prediction model proposed in this paper outperforms other models on multiple indicators. Also, the case study and experimental results verify the effectiveness of the method. © 2023 IEEE.

关键词： Dynamics

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A Simulation Model for Proactive Services: A Case Study on Evading Dangerous Areas for LNG Ships 16th

A Simulation Model for Proactive Services: A Case Study on...

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16th International Conference on Service Science, ICSS 2023

作者： Shi, Yongpeng Wang, Guiling Zhang, Jiayu Li, Zhenyu Yu, Jian 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 Department of Computer Science Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9789819944019

Proactive service is a kind of service that can be automatically provided based on logical judgment without human intervention. The traditional approach to evaluating proactive services involves conducting quantitative analyses through public datasets. However, this method can only evaluate the effectiveness of the specific algorithms and has limitations in evaluating the effectiveness of real-world applications as a whole. Determining a more appropriate evaluation approach for proactive services is a challenging problem. To address this issue, this paper proposes a method to evaluate the effectiveness of proactive services from a simulation perspective with a case of LNG (Liquefied Natural Gas) shipping as an example. Using the multi-agent simulation method, we design and implement a simulation model for LNG ships navigating at sea while avoiding dangerous areas by AnyLogic, which is capable of interacting with the deep learning model for predicting ships’ locations. Our simulation model also defines a set of metrics for evaluating the effectiveness of proactive services in the application case. We present a simulation demonstration on evading dangerous areas for LNG ships and conduct simulation experiments. The experimental results show that the simulation model can serve as an effective evaluation method for proactive services. Additionally, the simulation model can also be used to analyze differences in the application effects of proactive services implemented under different prediction algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Task-load aware and predictive-based workflow scheduling in cloud-edge collaborative environment

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Journal of Reliable Intelligent Environments 2022年第1期8卷 35-47页

作者： Zhang, Mingzhu Yang, Zhongguo Yan, Jiahao Ali, Sikandar Ding, Weilong Wang, Guiling 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 Department of Information Technology The University of Haripur Haripur22620 Pakistan

There are a large number of real-time streaming data task processing workflows in online anomaly detection and intelligent transportation systems. Real-time data transmission is required between these processing tasks. To ensure real-time performance, it is inevitable for real-time workflow scheduling. However, due to the connected sensor data being updated in real-time, the task load of the workflow will fluctuate. In this case, the original scheduling plan is no longer optimal and needs to be renewed. Adjustments in scheduling plan bring new challenges to dynamic scheduling. At the same time, when the scheduling plan changes, the deep learning models and data used in these workflow tasks need to be redeployed at a high cost. To address these challenges, we use the data generating rate to express the task load’s fluctuation. Besides, we also consider the migration cost caused by the adjustment of the scheme in the continuous scheduling process during modeling and optimization. To achieve a low-cost scheduling scheme in a short time, we propose the NSGA-II-Seq2Seq algorithm which employs the historical scheduling plan to generate a candidate scheduling plan. The experiments are carried out in the modified WorkflowSim. Through experiment, it is found that the method proposed in this paper can adapt to the change of task load and can produce an adaptively fine-tuning scheduling scheme. In multiple consecutive scheduling experiments, the population optimization process of NSGA is accelerated, which significantly reduces the time to obtain the scheduling plan. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Scheduling

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A LLM Enhanced Two-Stage Approach for Multi-hop Knowledge Graph Reasoning 19th

A LLM Enhanced Two-Stage Approach for Multi-hop Knowledge Gr...

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

作者： Han, Dongxue Zhao, Zhuofeng Liu, Chen College of Information North China University of Technology Beijing100144 China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing100144 China

ISBN: (纸本)9789819623754

Multi-hop knowledge graph reasoning is an effective method for conducting complex knowledge question answering using natural language. Most existing researches adopt the iterative relation reasoning approach based on the overall semantics of the question to perform multi-hop knowledge graph reasoning. However, due to the overlooks of the local characteristics and fine-grained information of the relationships, there exist some issues such as large scales of candidate reasoning paths and unclear semantic relationships which need to be solved. In this paper, we propose a LLM(large language model) enhanced two-stage approach for multi-hop knowledge graph reasoning, aiming to enhance the efficiency and accuracy of complex knowledge question answering. Initially, our approach leverages the text understanding and generation capabilities of large language model, and employs structured knowledge template prompts to conduct coarse-grained relation filtering within a large set of candidate reasoning path relations, thus reducing the scale of the reasoning space. Subsequently, concerning the fine-grained evaluation of the semantic relations of candidate reasoning paths, a relation scoring mechanism based on SBERT+BiLSTM is designed, which is used to evaluate the relations from three perspectives: the similarity between the question and the relations, the relevance of the relations to the reasoning paths, and the degree of match between the question and candidate paths. Experimental results on standard datasets including QALD10, CWQ and GrailQA demonstrate that our approach achieves significant performance improvements in multi-hop KGQA tasks compared to existing approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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Road Network Enhanced Traffic Flow Prediction Service

Road Network Enhanced Traffic Flow Prediction Service

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IEEE International Conference on Web Services (ICWS)

作者： Yusheng Mei Zhuofeng Zhao Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data School of Information Science and Technology North China University of Technology Beijing China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

Traffic flow prediction with trajectories is one core technology of Intelligent Transportation System, but also a challenging task, and the fundamental challenge is to effectively model the underlying causality of flows among roads, which are not only affected by vehicle driving preference, but also by road network structure features. To this end, this paper proposes a novel model, namely RNTrGNN(Road Network Enhanced Trajectory-based Graph Neural Network), which utilizes both vehicle trajectory and road network spatial features for traffic flow prediction. Specifically, RNTrGNN utilizes graph propagation of trajectory transition patterns to model the spatial traffic demand on the road network. Also, a Graph Attention Network(GAT) is used to incorporate road segment embedding into the traffic demand. Moreover, we design a traffic constraint module to take into account the effect of neighboring roads based on road distance. Extensive experiments conducted on three real-world taxi trajectory datasets show that our method can significantly outperform existing baseline models.

关键词： Web services Roads Vehicle driving Urban areas Predictive models Graph neural networks Trajectory Public transportation Intelligent transportation systems Meteorology

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Tour Guide Robot Based on large Language Model and User Behavior analysis

Tour Guide Robot Based on Large Language Model and User Beha...

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Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Haoran Lin Jing Wang Yutong Zhan Yuan Zhang Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data School of Information Science and Technology North China University of Technology Beijing China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

With the continuous development of human-computer interaction technology, the applications of tour guide robots in scenarios such as museums and exhibition halls are becoming increasingly widespread. However, traditional tour guide robots primarily rely on predefined content and texts, as well as pre-planned routes, making it difficult to adjust in real-time based on the diverse needs and interests of different visitors. This limitation restricts interactivity and engagement. To address this issue, this paper proposes an intelligent tour guide robot system that integrates large language model and user behavior analysis technology, aimed at dynamically adjusting tour content by real-time perception of visitors' interests. This system leverages the natural language processing capabilities of large language model, along with user behavior analysis techniques, to provide visitors with a more personalized and interactive tour experience. Experimental validation shows that the proposed tour guide robot system can enhance the accuracy and proactivity of tour presentations, thereby increasing user engagement and the overall effectiveness of the tour.

关键词： Accuracy Robot kinematics large language models Knowledge based systems Semantics Systems architecture Gaze tracking Real-time systems User experience Natural language processing

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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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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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