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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81-90 订阅

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A Group Recognition Method of Scientific and Technological Personnel based on Relational Graph

A Group Recognition Method of Scientific and Technological P...

引用

2020 International Conference on Service Science, ICSS 2020

作者： Wang, Zhuohao Yang, Dongju Zhang, Hanshuo Institute of Scientific and Technical Information of China Beijing China 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)9781728185316

The key problem in the fine management of science and technology is to model the behavior characteristics of scientific and technical personnel and then find groups through various related cooperative relationships. Aiming at the analysis of team relationship of scientific and technical personnel data, this paper proposed a method to recognize the group of scientific and technological personnel based on relational graph. The relationship model of scientific and technological personnel was designed, and based on this, the relational graph was constructed with the relationship identification and extraction from source data. A frequent item mining algorithm based on Hadoop was proposed, which enabled getting the group of scientific and technological personnel by mining and analysis of data in relational graph. In this paper, the proposed method was experimented on both open and private data sets, and compared with several classical algorithms. The results showed that the method proposed in this paper has a significant improvement in execution efficiency. © 2020 IEEE.

关键词： Human resource management

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The Segmentation and Reconstruction Method of Business Process BPMN under Constraint Conditions

The Segmentation and Reconstruction Method of Business Proce...

引用

2021 CCF International Conference on Service Science, ICSS 2021

作者： Qin, Shenghui Zhao, Zhuofeng Su, Jianhua Yang, Zhongguo North China University of Technology Beijing Key Laboratory on Integration and Analysis of Largecale Stream Data Beijing China Exploration and Development Reserch Institute of PetroChina Changqing Oilfield Company Shanxi Xi'an China

ISBN: (纸本)9781665440455

Business process model and notation BPMN2.0 is an industry standard in the field of business process management. In order not to destroy the association rules between the various tasks of the business process, and to schedule them in the cloudedge environment, improve the service quality of the business process, this paper proposes a method of segmentation and reconstruction of the BPMN2.0 model, based on the BMPN2.0 model of the original business process and its constraints of the cloud-edge environment, the original business process is divided and reconstructed into multiple sub-business processes, so that the reconstructed sub-business process conforms to the BPMN2.0 modeling standard and is actually usable, providing basis for the scheduling of business processes in the cloud-edge environment. Through example verification and experimental analysis, the effectiveness of the segmentation reconstruction method in this paper is verified. © 2021 IEEE.

关键词： Systems engineering

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Lecture Information Service based on Multiple Features Fusion

Lecture Information Service based on Multiple Features Fusio...

引用

2020 International Conference on Service Science, ICSS 2020

作者： Yang, Zhongguo Zhang, Mingzhu Zhang, Zhongmei Liu, Chen Li, Han Lan, Yuanyuan 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 School of Management Engineering Shandong Jianzhu University Jinan China

ISBN: (纸本)9781728185316

Information service is always a hot topic especially when web is accessible anywhere. In university, lecture information is very import for students and teachers who want to take part in academic meetings. Therefore, lecture news extraction is an important and imperative task. Although many open information extraction methods have been proposed, but due to the highly heterogeneity of website, this task is still a challenge. In this manuscript, we propose a method based on fusing multiple features to locate lecture news in university web site. These features including the organization structure of lecture news catalog webpage, the visual similarity and the semantic of webpage. Additionally, this paper provide an information service based on a main content extraction algorithm for extracting lecture information. The stable and invariant features enable the propose method could adaptive to many kinds of campus website. The experiments conducted on 50 websites show the effectiveness and efficiencies of provided service. © 2020 IEEE.

关键词： Websites

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A Service Selection Framework for Anomaly Detection in IoT stream data

A Service Selection Framework for Anomaly Detection in IoT S...

引用

2020 International Conference on Service Science, ICSS 2020

作者： Yang, Zhongguo Ding, Weilong Zhang, Zhongmei Li, Han Zhang, Mingzhu Liu, Chen 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 School of Management Engineering Shandong Jianzhu University Jinan China

ISBN: (纸本)9781728185316

Many anomaly detection algorithms have been provided as services for more convenient and efficient utilization in IoT era. Due to concept drift existed in dynamic IoT stream data, it is a changeling task to apply proper anomaly detection services at run time. For effective on-line anomalous data discovery, this paper proposes a service selection framework to dynamically select and configure anomaly detection services. A fast classification model based on XGBoost is trained to identify the pattern of various stream data, so that suitable service can be selected and configured according to the pattern of stream data. Extensive experiments on real and synthetic data sets show that our framework can select suitable service for different scenarios, and the accuracy of the chosen services outperforms state-of-the-art methods. © 2020 IEEE.

关键词： Anomaly detection

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A method for resisting adversarial attack on time series classification model in iot system 1

引用

17th International Conference on Web Information Systems and Applications, WISA 2020

作者： Yang, Zhongguo Li, Han Zhang, Mingzhu Wang, Jingbin Liu, Chen 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 Changqing Oilfield Company General Manager Office Xian China

ISBN: (数字)9783030600297

ISBN: (纸本)9783030600280

IoT device is often associated with corresponding datasets, algorithms, and infrastructure. However, many potential threats exist in IoT basic infrastructure when deep learning algorithms are applied in these devices. Typically, a deep learning method is widely applied as the basic decision algorithm to classify the time series data, which is an important task in IoT data application. Nevertheless, they are vulnerable to adversarial examples, which bring potential risks to some fields such medical and security in which, a minor disturbing in the time series data could lead to wrong decision. In this paper, we show some white-box attack and random noise attack against time series data. Moreover, we show an adversarial example generated method which only changes one value of the original time series. To resist the adversarial attack, we train an adversarial examples detector to differentiate the adversarial examples from normal examples based on deep features. The adversarial examples detector could filter the adversarial examples before future impair happening. Experiments on UCR data sets show 97% of adversarial examples could be successfully detected generated by two common attack methods: FGSM and BIM. © Springer Nature Switzerland AG 2020.

关键词： Time series

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Exploiting Passive Beamforming of Smart Speakers to Monitor Human Heartbeat in Real Time

Exploiting Passive Beamforming of Smart Speakers to Monitor ...

引用

2021 IEEE Global Communications Conference (GLOBECOM)

作者： Zhi Wang Fusang Zhang Siheng Li Beihong Jin State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing China

ISBN: (纸本)9781728181059

Currently, cardiac diseases have become one of the biggest health concerns. Existing heartbeat monitoring methods either require dedicated intrusive devices (e.g., ECG devices) that suffer high costs or leverage video camera analyses that are light-sensitive. In this paper, leveraging the acoustic signals sent by a speaker and received by a microphone array, we develop a prototype system to achieve the contactless and low-cost heartbeat monitoring. In particular, while we exploit the passive beamforming to enhance the user's heartbeat signal, we design a filtering method in frequency domain to remove the line-of-sight (LoS) impact and retain the target-reflected signals, and propose a wideband time-delay method to estimate the direction of arrival of target-reflected signal. Thus, our prototype is able to robustly estimate the human heartbeat and push the limit of acoustic sensing range. The experimental results show that our prototype achieves a heart rate monitoring at 1.7 m with the estimation error of 0.5 bpm, which is comparable to ECG or other contact-based solutions.

关键词： Costs Heart beat Array signal processing Prototypes Electrocardiography Acoustics Microphone arrays

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An approach to discovering event correlations among edge sensor services

An approach to discovering event correlations among edge sen...

引用

International Journal of Internet Manufacturing and Services

作者： Liu, Chen Cao, Yunmeng Han, Yanbo Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of Technology Beijing China Institute of Data Engineering School of Computer Science and Technology North China University of Technology No. 5 Jinyuanzhuang Road Beijing100144 China

In an IoT environment, a surge in sensor data volume has exposed the shortcomings of cloud computing, particularly the limitation of network transmission capability and centralised computing resources. To handle these issues, this paper proposes a service-oriented framework, called as INFOG, to support the dynamic cooperation among sensors with the fog computing paradigm. Proactive data services and service hyperlinks, which are our previous work, are two key abstractions for the INFOG framework. The services are software-defined abstraction of physical sensors. They are deployed in edge nodes in INFOG. And service hyperlinks, encapsulation of service correlations, enable the cooperation of sensors at the software layer. We also propose a frequent sequential pattern-based approach to effectively discover service hyperlinks. Based on the dataset from a real power plant as well as several synthetic datasets, we do lots of experiments to verify the effectiveness and efficiency of our algorithm. © 2020 Inderscience Enterprises Ltd.

关键词： Fog computing Hypertext systems Computing resource Dynamic cooperation Effectiveness and efficiencies Frequent sequential patterns Network transmission Service oriented frameworks Service correlation Synthetic datasets

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Research on Construction and Evolution of Relational Graph of the Science and Technology data 5

Research on Construction and Evolution of Relational Graph o...

引用

5th IEEE International Conference on Computer and Communications, ICCC 2019

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

ISBN: (纸本)9781728147437

In recent years, the amount of scientific and technological data has been increasing, and the requirements for data analysis have been continuously improved. Among of them, the mining of data association relations and the construction and evolution of relational models are the research hotspots in recent years. How to obtain entities and their relationships from massive data, and then use the relational model to construct the relational graph and support the evolution of the graph is the key problem to be solved. Aiming at the above problems, this paper proposes a method for constructing and evolving relational graphs based on structured data, including source data preprocessing, entity and attribute identification, information extraction, relationship matching, and relational graph construction and evolution. The evolution and update algorithm of the graph based on Simhash and Hamming distance is proposed, and the update strategy of the entity similarity in the graph is analyzed. Combined with experimental verification, this method can automatically realize the extraction of structured data, the construction of relational graph and the evolution and update process of the graph, and the accuracy of the results was about 97%. © 2019 IEEE.

关键词： Hamming distance

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An IOT data Collection Mechanism Based on Cloud-Edge Coordinated Deep Learning 13th

An IOT Data Collection Mechanism Based on Cloud-Edge Coordin...

引用

13th China Conference on Wireless Sensor Networks, CWSN 2019

作者： Wang, Zi-hao Wang, Jing 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)9789811517846

The large-scale of data collection from IoT devices to central cloud brings several challenges that need to be overcome, especially for needs of real-time collection and bandwidth restrictions. In order to address this issue, we proposed a data collection method that combine cloud with edge node by using deep learning technology to provide the data collection service. The cloud is responsible for storing the large amount of historical sensor data, training the deep learning model, and deploying the model to the edge side. The edge node will receive the model of data prediction and then determines whether the real data will be uploaded to the cloud to optimize the model. Experiments show that the method we proposed can not only increase the speed of data collection, but also reduces the network traffic and eliminates bandwidth load effectively. © 2019, Springer Nature Singapore Pte Ltd.

关键词： data acquisition

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A Cloud-based Approach for Ship Stay Behavior Classification using Massive Trajectory data

A Cloud-based Approach for Ship Stay Behavior Classification...

引用

2020 International Conference on Service Science, ICSS 2020

作者： Guo, Weiqiang Zhao, Zhuofeng Zheng, Zhentao Xu, Yao Beijing Key Laboratory on Integration and Analysis of Large-scale Streaming Data North China University of Technology China China Electronic Science and Technology Group Ocean Information Technology Research Institute Company China

ISBN: (纸本)9781728185316

With the widespread application of AIS (Automatic Ship Identification System), ship trajectory data is being collected and becoming increasingly available. Consequently, a lot of ship trajectory data applications have become feasible that mine the value from the data. In this paper, based on massive ship trajectory data, we aim to classify two kinds of ship stay behavior for recognizing different areas in the port, namely berth and anchorage. The traditional trajectory data classification model mainly distinguishes the moving and staying state of moving objects, but there is little research on the classification of different kinds of stay behavior, especially for ship stay behavior classification. In this work, we propose an extraction algorithm based on the cloud storage and distributed computing frameworks to extract classification features by analyzing the behavioral characteristics of ships at berths and anchors. Second, with the consideration of the low precision, drift and sparsity characteristics of ship trajectory data, we design a series of experiments based on ten-fold cross-validation method for evaluating five classical classification models, such as XGBoost, Random Forest and so on. Third, experimental verifications of various classification models are conducted based on a real ship trajectory dataset, and the effectiveness of different models for recognizing ship stay area are compared. © 2020 IEEE.

关键词： Trajectories

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