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2021 CCF International Conference on Service Science, ICSS 2021

作者： Lan, Yuanyuan Fang, Lei Zhang, Mingzhu Su, Jianhua Yang, Zhongguo Li, Han North China University of Technology Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data School of Information Science and Technology Beijing China Oilfield Company Institute of PetroChina Changqing Exploration and Development Reserch Shanxi Xi'an China

ISBN: (纸本)9781665440455

In the cloud computing environment, the release and dynamic deployment of microservices face many challenges, and the services' dependency is an important consideration for service deployment. The service call chain logs record rich information, such as the time and delay of each service call in the business tracking process, and present the logs in the form of a call chain. Existing research does not fully consider the local composite service dependencies and their discontinuous dependencies in the service chain. In order to obtain these complex dependencies and provide basic supporting data for the dynamic deployment and adjustment of microservices, a dependency model of microservices is proposed, and a dependency mining method based on call chain logs is designed to extract local dependencies and the discontinuous dependency relationship. The effectiveness of the algorithm is verified by testing on Alibaba Cloud Computing's public data set. © 2021 IEEE.

关键词： Mining

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Meta-process: A noval approach for decentralized execution of process

Meta-process: A noval approach for decentralized execution o...

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2021 CCF International Conference on Service Science, ICSS 2021

作者： Fang, Lei Yang, Zhongguo Qin, Shenghui Zhang, Mingzhu Ali, Sikandar Zhao, Zhuofeng North China University of Technology Beijing Key Laboratory on Integration and Analysis of Large-cale Stream Data Beijing China China University of Petroleum Department of Computer Science and Technology Beijing China

ISBN: (纸本)9781665440455

With the rapid growth of internet usage for enterprise-wide and cross-enterprise business applications (such as those in Electronic Commerce), workflow systems are gaining importance as an infrastructure for automating inter-organizational interactions. However, the traditional centralized workflow management technology can no longer meet the needs of current application services. For example, in e-commerce, cross-enterprise business applications may cause many security problems and cross-domain problems in the implementation of workflow. At the same time, due to the uncertainty and variability of environment and user requirements in practical applications, many business logics are difficult to be completely defined in advance. Therefore workflow models need to be immediately built or adjusted dynamically. Nowadays, distributed scheduling and decentralized control of workflow have become the emerging trend and are facing many challenges at the forefront of Internet development technology. In this paper, a distributed workflow control execution method based on 'meta-process' is proposed. Specifically, we designed and implemented a decentralized distributed scheduling management system for workflow tasks. To manage and control the distributed scheduling of workflow, we constructed a 'meta-process', which can ensure the integrity of the control chain in the distributed scheduling process. Our system can efficiently handle the data state migration between task nodes and supports the dynamic adjustment of the workflow model. For validation, we simulated a large number of service scheme samples and applied them to the system, which proved that all service cases can be executed correctly. Therefore, the feasibility of this method is verified. © 2021 IEEE.

关键词： Electronic commerce

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A Hybrid Deep Learning Approach for Traffic Flow Prediction in Highway Domain 16th

A Hybrid Deep Learning Approach for Traffic Flow Prediction ...

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Wang, Zhe Ding, Weilong Wang, Hui 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 Beijing China-Power Information Technology Company Limited Beijing100192 China

ISBN: (纸本)9783030675394

With the development of cities, intercity highway plays a vital role in people’s daily travel. The traffic flow on the highway network is also increasingly concerned by road managers and participants. However, due to the influence of highway network topology and extra feature such as weather, accurate traffic flow prediction becomes hard to achieve. It is difficult to construct a multidimensional feature matrix and predict the traffic flow of the network at one time. A novel prediction method based on a hybrid deep learning model is proposed, which can learn multidimensional feature and predict network-wise traffic flow efficiently. The experiment shows that the prediction accuracy of this method is significantly better than existing methods, and it has a good performance during the prediction. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Network topology

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Research on User Demand-Driven Service Matching Methodology

Research on User Demand-Driven Service Matching Methodology

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IEEE International Conference on Cloud Computing and Big data analysis (ICCCBDA)

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

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.

关键词： Cloud computing Conferences Semantics Big data

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Geographic and Temporal Deep Learning Method for Traffic Flow Prediction in Highway Network 17th

Geographic and Temporal Deep Learning Method for Traffic F...

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

作者： Zhang, Tianpu Ding, Weilong Xing, Mengda Chen, Jun Du, Yongkang Liang, Ying 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 Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Science Beijing China

ISBN: (纸本)9783030926373

Traffic congestion has become an inevitable situation faced by all countries and the prediction accuracy of traffic flow, as one of the means to solve this problem, still needs to be improved. Most studies lack the consideration of the influence of multiple factors such as spatial factors, time series factors and other external factors, which makes the prediction effect of traffic flow unsatisfactory. In this paper a method is proposed based on deep learning that can capture the geographic spatial relationship among toll stations, the dynamic temporal relationship of historical traffic flow, extreme weather and calendar types. On the three metrics of MAPE, MAE, and RMSE, the prediction effect of our model has increased by 30% compared with KNN, GBRT and LSTM models. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Traffic congestion

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Cache-based Executive Request Dispatching Method in The Distributed Workflow System

Cache-based Executive Request Dispatching Method in The Dist...

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IEEE World Congress on Services (SERVICES)

作者： Bo Lv Weilong Ding Ji Liu North China University of Technology Beijing China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China

A Workflow management system (WfMS) is usually deployed in a Cloud environment in a distributed architecture, and the execution of the process task requires the workflow engine to parse the process definition in its cache. However, the current WfMS seldom considers the problem of the cache hit, which leads to repeated parse and decreases request response time and service quality of WfMS. A task execution dispatching method is proposed in this paper based on cache mechanism. The method enables each task’s requests to be dispatched to the best matching worker, and comprehensively utilizes the cache in the worker or the database. After experiments of practical scenes, compared with two traditional ones commonly used in business, our method is proved to improve the cache hit rate, reduces the response time, and improves system performance.

关键词： databases System performance Dispatching Time factors Task analysis Workflow management software Engines

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Traditional Chinese Medicine knowledge Service based on Semi-Supervised BERT-BiLSTM-CRF Model

Traditional Chinese Medicine knowledge Service based on Semi...

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2020 International Conference on Service Science, ICSS 2020

作者： Zhang, Mingzhu Yang, Zhongguo Liu, Chen Fang, Lei School of Information Science and Technology Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of Technology Beijing China

ISBN: (纸本)9781728185316

Most of Traditional Chinese Medicine (TCM) data and ancient records exist in the form of books. The unstructured medical information is the foundation for building TCM knowledge service. The existing methods are not accurate enough to solve TCM named entity recognition and require a lot of manual labeling data. This paper proposes a semi-supervised embedded Semi-BERT-BiLSTM-CRF model. Based on the book "Diagnosis of Traditional Chinese Medicine in Traditional Chinese Medicine", we select the physical features from the cleaned-up text information according to the characteristics of Chinese medicine classics, and then use a small amount of labeled data to train the BERT-BiLSTM-CRF model. The obtained model is used to predict unlabeled data and obtain pseudo-label data. The pseudo-label and labeled data are used as a training set for model training. Experiments show that TCM entity recognition accuracy of this method reaches 81.24%, which effectively improves the TCM entity recognition accuracy and reduces the manual labeling work. The results of this research can be applied to scenarios such as auxiliary diagnosis of TCM and expert system after subsequent improvement and transformation. © 2020 IEEE.

关键词： Diagnosis

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

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

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IEEE International Conference on Cloud Computing and Big data analysis (ICCCBDA)

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

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.

关键词： Analytical models Web services Clustering methods Computational modeling Clustering algorithms Feature extraction data models

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Phased Deep Spatio-temporal Learning for Highway Traffic Volume Prediction

arXiv

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arXiv 2023年

作者： Ding, Weilong Zhang, Tianpu Wang, Zhe School of Information Science and Technology North China University of Technology No. 5 Jinyuanzhuang Road Shijingshan District Beijing100144 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data No. 5 Jinyuanzhuang Road Shijingshan District Beijing100144 China Cloud & Smart Industries Group Tecent Inc Beijing China

Inter-city highway transportation is significant for citizens' modern urban life and generates heterogeneous sensory data with spatio-temporal characteristics. As a routine analysis in transportation domain, daily traffic volume estimation faces challenges for highway toll stations including lacking of exploration of correlative spatio-temporal features from a long-term perspective and effective means to deal with data imbalance which always deteriorates the predictive performance. In this paper, a deep spatio-temporal learning method is proposed to predict daily traffic volume in three phases. In feature pre-processing phase, data is normalized elaborately according to latent long-tail distribution. In spatio-temporal learning phase, a hybrid model is employed combining fully convolution network (FCN) and long short-term memory (LSTM), which considers time, space, meteorology, and calendar from heterogeneous data. In decision phase, traffic volumes on a coming day at network-wide toll stations would be achieved effectively, which is especially calibrated for vital few highway stations. Using real-world data from one Chinese provincial highway, extensive experiments show our method has distinct improvement for predictive accuracy than various traditional models,reaching 5.269 and 0.997 in MPAE and R-squre metrics, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Big data

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Where Is the Next Path? A Deep Learning Approach to Path Prediction Without Prior Road Networks 16th

Where Is the Next Path? A Deep Learning Approach to Path Pre...

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Wang, Guiling Zhang, Mengmeng Gao, Jing Han, Yanbo Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data North China University of Technology No.5 Jinyuanzhuang Road Shijingshan District Beijing100144 China School of Information Science and Technology North China University of Technology No.5 Jinyuanzhuang Road Shijingshan District Beijing100144 China

ISBN: (纸本)9783030675394

Trajectory prediction plays an important role in many urban and marine transportation applications, such as path planning, logistics and traffic management. The existing prediction methods of moving objects mainly focus on trajectory mining in Euclidean space. However, moving objects generally move under road network constraints in the real world. It provides an opportunity to take use of road network constraints or de-facto regular paths for trajectory prediction. As yet, there is little research work on trajectory prediction under road network constraints. And these existing work assumes prior road network information is given in advance. However in some application scenarios, it is very difficult to get road network information, for example the maritime traffic scenario on the wide open ocean. To this end, we propose an approach to trajectory prediction that can make good use of road network constrains without depending on prior road network information. More specifically, our approach extracts road segment polygons from large scale crowdsourcing trajectory data (e.g. AIS positions of ships, GPS positions of vehicles etc.) and translates trajectories into road segment sequences. Useful features such as movement direction and vehicle type are extracted. After that, a LSTM neural network is used to infer the next road segment of a moving object. Experiments on real-world AIS datasets confirm that our approach outperforms the state-of-the-art methods. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Trajectories

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