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The parallel and precision adaptive method of marine lane extraction based on quadtree 14th

The parallel and precision adaptive method of marine lane ex...

14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Li, Zhuoran Wang, Guiling Meng, Jinlong Xu, Yao 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 No. 11 Shuangyuan Road Badachu Hi-Tech Park Shijingshan District Beijing100041 China

ISBN: (纸本)9783030129804

Extracting the marine lane results from the ocean spatial big data is a challenging problem. One of the challenges is that the quality of the trajectory data is quite low, and the trajectory data quality is extremely different in different areas. A parallel and precision adaptive method of marine lane extraction based on QuadTree is proposed to meet this challenge. The method takes advantage of several methods including average sampling, interpolation, removing noise, trajectory segmentation, and trajectory clustering based on GeoHash encoding through the MapReduce parallel computing framework. The preprocessing phase can effectively simplify the big data and improve the efficient of data processing. Based on the QuadTree data structure, a parallel merge filtering algorithm is proposed and implemented used Spark framework. The algorithm performs grid merging on the sparse grid regions, and obtaining a new grid result with different size. The sliding local window filtering algorithm based on the QuadTree is proposed to obtain the marine lane grid set data. Applying the Delaunay triangulation method on the grid data, the multi-precision marine lane results are effectively extracted. The experimental results show that the proposed method can automatically extract multi-precision marine lane using the trajectory data near the coast with high and low grid precision. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Extraction

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dataservicehatch: generating and composing continuous data services 3rd

Dataservicehatch: generating and composing continuous data s...

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3rd APWeb and WAIM Joint Conference on Web and Big data, APWeb-WAIM 2019

作者： Wang, Guiling Cui, Tongtong Zuo, Xiaojiang Xu, Yao 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 No. 11 Shuangyuan Road Badachu Hi-Tech Park Shijingshan District Beijing100041 China

ISBN: (纸本)9783030260743

In this paper, we present dataServiceHatch, a Web-based system that semi-automatically converts relational databases and stream data sources into Web Services and answers continuous queries on both traditional database tables and data streams by composing Web Services, rather than accesses databases or big data stream infrastructures directly. This can help organizations to unify the access entrance of all their data sources with just a few simple configurations and avoid exposing their data directly. dataServiceHatch also provides mechanisms to remove the need for manually writing a complex SQL-like query expression or service composition plan to answer a continuous query on data streams. © 2019, Springer Nature Switzerland AG.

关键词： Big data

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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...

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International Conference on Service Sciences (ICSS)

作者： Weiqiang GUO Zhuofeng ZHAO Zhentao ZHENG Yao XU Beijing Key Laboratory on Integration and Analysis of Large-scale Streaming Data North China University of Technology China Electronic Science and Technology Group Ocean Information Technology Research Institute Company

ISBN: (数字)9781728185316

ISBN: (纸本)9781728185323

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.

关键词： Cloud computing Feature extraction data models Trajectory Classification algorithms Marine vehicles Random forests

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

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Journal of Physics: Conference Series 2021年第1期1756卷

作者： Bowen Yang Lei Yuan Jin Yan Zhiming Ding Zhi Cai Limin Guo Beijing University of Technology Beijing China 100124 Institute of Software Chinese Academy of Sciences Beijing China 100190 Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing China 100144

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.

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A two-layer image caching mechanism based on social relationships 4

A two-layer image caching mechanism based on social relation...

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4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Ling, Zhao Jing, Wang Li-Jie, Niu 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)9781538683392

With the rapid development of Internet technology, the popularity of social networking has enriched people's lives. A large number of social applications such as Facebook, WeChat and QQ often use a large number of image resources, which will consume much network traffic for transferring the images and affect the speed of loading. Therefore, most applications use the strategy of displaying the thumbnail images first and then loading the original images according to the users' requests. In the design and implementation of the image server, using caching technology can reduce disk I/O and increase image access speed. But current caching mechanisms have not considered the different access patterns for thumbnails and original images, nor the social relationships between image generators. In this paper, a two-layer image caching mechanism based on social relationships is pro-posed, which divides the image cache into the thumbnail layer and the original image layer, and comprehensively considers the social relationships between users and the relationships between the thumbnails and the original images. Experiments show that the two-layer image caching mechanism can effectively improve the cache hit rate for images. © 2018 IEEE.

关键词： Image enhancement

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A Frequent Sequential Pattern Based Approach for Discovering Event Correlations 15th

A Frequent Sequential Pattern Based Approach for Discovering...

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15th Web Information Systems and Applications Conference, WISA 2018

作者： Cao, Yunmeng Liu, Chen Han, Yanbo Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of Technology Beijing China Data Engineering Institute North China University of Technology Beijing China

ISBN: (纸本)9783030029333

In an IoT environment, event correlation becomes more complex as events usually span over many interrelated sensors. This paper refines event correlations in an IoT environment, and proposes an algorithm to discover event correlations. We transform the event correlation discovery problem into a time-constrained frequent sequence mining problem. Moreover, we apply our approach in anomaly warning in a coal power plant. We have made extensive experiments to verify the effectiveness of our approach. © 2018, Springer Nature Switzerland AG.

关键词： Internet of things

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Error correction for search engine by mining bad case

Error correction for search engine by mining bad case

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作者： Duan, Jianyong Ji, Tianxiao Wang, Hao College of Computer Science and Technology North China University of Technology Beijing China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing100144 China

Automatic error correction of users' search terms for search engines is an important aspect of improving search engine retrieval efficiency, accuracy and user experience. In the era of big data, we can analyze and mine massive search engine logs to release the hidden mind with big data ideas. It can obtain better results through statistical modeling of query errors in search engine log data. But when we cannot find the error query in the log, we can't make good use of the information in the log to correct the query result. These undiscovered error queries are called Bad Case. This paper combines the error correction algorithm model and search engine query log mining analysis. First, we explored Bad Cases in the query error correction process through the search engine query logs. Then we quantified the characteristics of these Bad Cases and built a model to allow search engines to automatically mine Bad Cases with these features. Finally, we applied Bad Cases to the N-gram error correction algorithm model to check the impact of Bad Case mining on error correction. The experimental results show that the error correction based on Bad Case mining makes the precision rate and recall rate of the automatic error correction improved obviously. Users experience is improved and the interaction becomes more friendly. © Copyright 2018 The Institute of Electronics Information and Communication Engineers.

关键词： Search engines

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(WIP) Correlation-Driven Service Event Routing for Predictive Industrial Maintenance 25

(WIP) Correlation-Driven Service Event Routing for Predictiv...

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25th IEEE International Conference on Web Services, ICWS 2018

作者： Zhu, Meiling Liu, Chen Zhang, Shouli Han, Yanbo School of Computer Science and Technology Tianjin University Tianjin China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of Technology Beijing China

ISBN: (纸本)9781538672471

Predictive industrial maintenance promotes proactive scheduling of maintenance to minimize unexpected device faults. A fault is not always isolated and may be formed by a propagation of trivial anomalies, which are regarded as service events herein. In this paper, we firstly propose an algorithm for generating service event correlation. Such correlations can show us lots of clues to the anomaly/fault propagation. The correlations are encapsulated into service hyperlinks as our previous works did, and thus we depict the anomaly/fault propagation as service event routing among services via the refined service hyperlinks. Our scenario illustrates that a trivial anomaly may propagate into different faults under different service event correlations. It indicates that the destination of a service event is often uncertain. Therefore, this paper further proposes a heuristic approach to handle the uncertainty problem. Extensive experiments have been made to verify the effectiveness of the approach. © 2018 IEEE.

关键词： Maintenance

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An Improved multi-objective genetic algorithm based on orthogonal design and adaptive clustering pruning strategy

arXiv

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arXiv 2019年

作者： Yang, Xinwu You, Guizeng Zhao, Chong Dou, Mengfei Guo, Xinian Department of Faculty of Information Technology Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology Beijing Key Laboratory on Integration and Analysis of Large Scale Stream Data Beijing University of Technology No.100 Pingleyuan Chaoyang District Beijing100124 China Beijing University of Technology College of Computer and Information Engineering Henan Normal University.

Two important characteristics of multi-objective evolutionary algorithms are distribution and convergency. As a classic multiobjective genetic algorithm, NSGA-II is widely used in multi-objective optimization fields. However, in NSGA-II, the random population initialization and the strategy of population maintenance based on distance cannot maintain the distribution or convergency of the population well. To dispose these two deficiencies, this paper proposes an improved algorithm, OTNSGA-II II, which has a better performance on distribution and convergency. The new algorithm adopts orthogonal experiment, which selects individuals in manner of a new discontinuing non-dominated sorting and crowding distance, to produce the initial population. And a new pruning strategy based on clustering is proposed to self-adaptively prunes individuals with similar features and poor performance in non-dominated sorting and crowding distance, or to individuals are far away from the Pareto Front according to the degree of intraclass aggregation of clustering results. The new pruning strategy makes population to converge to the Pareto Front more easily and maintain the distribution of population. OTNSGA-II and NSGA-II are compared on various types of test functions to verify the improvement of OTNSGA-II in terms of distribution and convergency. Copyright © 2019, The Authors. All rights reserved.

关键词： Clustering algorithms

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A feature extraction method for predictive maintenance with time-lagged correlation–based curve-registration model

A feature extraction method for predictive maintenance with ...

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作者： Zhang, Shouli Liu, Chen Su, Shen Han, Yanbo Li, XiaoHong School of Computer Science and Technology Tianjin University Tianjin300072 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data North China University of Technology Beijing100144 China School of Computer Science and Technology North China University of Technology Beijing100144 China

With the prevalent development and use of predictive maintenance models for Internet-of-Things scenarios, the deep learning technology is gaining momentum. Feature extraction helps to increase efficiency in training the deep-learning–based predictive maintenance model. However, there are common situations of time-lagged correlations among industrial sensor data, resulting in reduction the effect of feature extraction. In this paper, we propose a feature extraction method for multisensors data with time-lagged correlation. A curve-registration method of correlation maximization algorithm is used to solve the problem of time-lagged correlation for multi sensors. Then we apply a recurrent neural network, namely, long short-term memory to develop a lightweight predictive maintenance model with the help of proposed feature extraction method. The effectiveness of the proposed feature extraction approach is demonstrated by examining real cases in a power plant. The experimental results indicate that our method can (1) effectively improve the accuracy of prediction and (2) improve the performance of the prediction model. © 2018 John Wiley & Sons, Ltd.

关键词： Feature extraction

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