scholarly journals GeoSOT-Based Spatiotemporal Index of Massive Trajectory Data

2019 ◽  
Vol 8 (6) ◽  
pp. 284 ◽  
Author(s):  
Chunyao Qian ◽  
Chao Yi ◽  
Chengqi Cheng ◽  
Guoliang Pu ◽  
Xiaofeng Wei ◽  
...  
Keyword(s):  
Large Scale ◽  
User Behavior ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Range Query ◽  
Space Time ◽  
Trajectory Data ◽  
Time Dimension ◽  
Growth Trend ◽  
Query Efficiency

With the rapid development of global positioning technologies and the pervasiveness of intelligent mobile terminals, trajectory data have shown a sharp growth trend both in terms of data volume and coverage. In recent years, increasing numbers of LBS (location based service) applications have provided us with trajectory data services such as traffic flow statistics and user behavior pattern analyses. However, the storage and query efficiency of massive trajectory data are increasingly creating a bottleneck for these applications, especially for large-scale spatiotemporal query scenarios. To solve this problem, we propose a new spatiotemporal indexing method to improve the query efficiency of massive trajectory data. First, the method extends the GeoSOT spatial partitioning scheme to the time dimension and forms a global space–time subdivision scheme. Second, a novel multilevel spatiotemporal grid index, called the GeoSOT ST-index, was constructed to organize trajectory data hierarchically. Finally, a spatiotemporal range query processing method is proposed based on the index. We implement and evaluate the index in MongoDB. By comparing the range query efficiency and scalability of our index with those of the other two space–time composite indexes, we found that our approach improves query efficiency levels by approximately 40% and has better scalability under different data volumes.

AN AUTOMATIC SEMANTIC MAP GENERATION METHOD USING TRAJECTORY DATA

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 63-67
Author(s):  
Y. Miao ◽  
X. Tang ◽  
Z. Wang
Keyword(s):  
Human Behavior ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Group Behavior ◽  
Trajectory Data ◽  
Geometric Information ◽  
Semantic Map ◽  
Map Generation ◽  
Terrain Features ◽  
Unknown Region

Abstract. It’s easily to obtain the geometric information of terrain features in a timely manner using advanced surveying and mapping methods, but it is impossible to obtain their semantic information with low latency due to the rapid development of cities. The popularity of GPS-enabled devices and technologies provide us a large number of personal location information. Moreover, it is possible to extract the personal or group behavior pattern due to the regularity of human behavior. Those conditions make it possible to extract and identify human behavior patterns from their trajectory data. In this paper, we present an automatic semantic map generation method that extract semantic patterns and take advantage of them to tagging spatial objects in an unknown region based on known semantic patterns. We study the regularity of trajectory data and build the semantic pattern based on the regularity of human behavior. Most importantly, we use known semantic patterns to identify the semantics of the stay points in the unknown region, and use this method to realize the semantic recognition of the stay points. Results of the experiments show the effectiveness of our proposed method.

Research of Data Mining Algorithm Based on the Intrusion Prevention System

2014 ◽  
Vol 644-650 ◽  
pp. 1787-1790
Author(s):  
Xian Hong Zhang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
User Behavior ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Current Model ◽  
Data Mining Algorithm ◽  
Intrusion Prevention ◽  
Mining Association Rules ◽  
Intrusion Prevention System

With the rapid development of network, the security problem of network becomes an issue which has been paid more and more attentions to. Among so many methods of intrusion prevention, data mining is a very effective one. The FP-growth algorithm is the most widely used algorithm for mining frequent item-sets, which is also an algorithm for mining association rules without candidate set. However, the FP-growth algorithm needs large memory when mining large database,and its running speed is slow. In order to overcome these problems, based on the FP-growth algorithm, this paper proposed an optimized algorithm. This paper compared the new algorithm with the previous one based on intrusion prevention model for campus network by experiments. Based on Experiments, we can draw the conclusion that, mining association rules by using the improved FP-growth algorithm can effectively detect the users’ behavior pattern, historical pattern and the current model to calculate the similarity of users, and provides the possibility to accurately judge the user behavior.

scholarly journals Exploring Urban Taxi Drivers’ Activity Distribution Based on GPS Data

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaowei Hu ◽  
Shi An ◽  
Jian Wang
Keyword(s):  
Large Scale ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Activity Space ◽  
Space Distribution ◽  
Distribution Characteristics ◽  
Taxi Drivers ◽  
Activity Distribution ◽  
Time Operation ◽  
The Difference

With the rapid development of information communication technology and data mining technology, we can obtain taxi vehicle’s real time operation status through the large-scale taxi GPS trajectories data and explore the drivers’ activity distribution characteristics. Based on the 204 continuous hours of 3198 taxi vehicles’ operation data of Shenzhen, China, this paper analyzed the urban taxi driver’s activity distribution characteristics from different temporal and spatial levels. In the time level, we identified the difference with taxi daily operation pattern (weekday versus weekends), continuous time in one day, passengers in vehicle time, and taxi drivers’ operation frequency; in the space level, we explored the taxi driver’s searching pattern, including searching activity space distribution and the relationship between the pick-up locations and the drop-off locations. This research can be helpful for urban taxi drivers’ operation and behavior pattern identification, as well as the contribution to the geographical activity space analysis.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Research on the Marketing Strategy of Online Education -- Taking New Oriental as an Example

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Jialu Chen ◽  
Yingxiao Han ◽  
An Li
Keyword(s):  
Online Learning ◽  
Online Education ◽  
Online Teaching ◽  
Large Scale ◽  
Target Market ◽  
Growth Trend ◽  
Continuous Growth ◽  
Financial Investment ◽  
Marketing Mode ◽  
Marketing Costs

In recent years, with the development of society and the progress of science and technology, online learning has penetrated into people's daily life, and people's demand for high-quality curriculum products is more and more strong. From a macro perspective, the continuous growth of national financial investment in education, the continuous upgrading of China's consumption structure, the development of 5G technology and the popularization of AI intelligence make online teaching less limited. The online education industry is showing an explosive growth trend. More and more online education institutions are listed for financing, and the market value is soaring. However, in 2019, except for GSX, the latest online learning platforms such as New Oriental, Speak English Fluently and Sunlands, have been in a state of loss. Most of these agencies seize the market by increasing advertising investment, but at the same time, they also bring huge marketing costs, which affect the financial performance of the company. With the enhancement of Matthew effect, large-scale educational institutions occupy a large market through free classes and low-price classes, while small and medium-sized institutions with weak capital strength are often unable to afford high sales costs, facing the risk of capital chain rupture. Taking new Oriental online as an example, this paper analyzes the problems existing in the marketing strategies of online education institutions. It also puts forward suggestions on four aspects, which are target market, differentiated value, marketing mix and marketing mode, so as to make sure that online education institutions can control marketing expenses and achieve profits by improving course quality, expanding marketing channels and implementing precise positioning.

scholarly journals High-Rate Transition Metal-based Cathode Materials for Battery-Supercapacitor Hybrid Devices

2021 ◽  
Author(s):  
Cong Wang ◽  
Zehao Song ◽  
Pei Shi ◽  
Lin Lv ◽  
Houzhao Wan ◽  
...  
Keyword(s):  
Large Scale ◽  
Rapid Development ◽  
Electronic Devices ◽  
High Rate ◽  
Specific Power ◽  
Energy Storage Devices ◽  
Storage Devices ◽  
Electrochemical Devices ◽  
Hybrid Devices ◽  
Scale Grid

With the rapid development of portable electronic devices, electric vehicles and large-scale grid energy storage devices, it needs to reinforce specific energy and specific power of related electrochemical devices meeting...

scholarly journals Mining Vehicle Trajectories to Discover Individual Significant Places: Case Study using Floating Car Data in the Paris Region

2021 ◽  
pp. 036119812199550
Author(s):  
Danyang Sun ◽  
Fabien Leurent ◽  
Xiaoyan Xie
Keyword(s):  
Large Scale ◽  
City Planning ◽  
Human Mobility ◽  
Work Place ◽  
Trajectory Data ◽  
Floating Car Data ◽  
Vehicle Trajectories ◽  
Activity Types ◽  
Paris Region

In this study we discovered significant places in individual mobility by exploring vehicle trajectories from floating car data. The objective was to detect the geo-locations of significant places and further identify their functional types. Vehicle trajectories were first segmented into meaningful trips to recover corresponding stay points. A customized density-based clustering approach was implemented to cluster stay points into places and determine the significant ones for each individual vehicle. Next, a two-level hierarchy method was developed to identify the place types, which firstly identified the activity types by mixture model clustering on stay characteristics, and secondly discovered the place types by assessing their profiles of activity composition and frequentation. An applicational case study was conducted in the Paris region. As a result, five types of significant places were identified, including home place, work place, and three other types of secondary places. The results of the proposed method were compared with those from a commonly used rule-based identification, and showed a highly consistent matching on place recognition for the same vehicles. Overall, this study provides a large-scale instance of the study of human mobility anchors by mining passive trajectory data without prior knowledge. Such mined information can further help to understand human mobility regularities and facilitate city planning.

scholarly journals High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops

2021 ◽  
Vol 22 (15) ◽  
pp. 8266
Author(s):  
Minsu Kim ◽  
Chaewon Lee ◽  
Subin Hong ◽  
Song Lim Kim ◽  
Jeong-Ho Baek ◽  
...  
Keyword(s):  
Drought Stress ◽  
High Throughput ◽  
Large Scale ◽  
Crop Yields ◽  
Plant Traits ◽  
Rapid Development ◽  
Plant Responses ◽  
Phenotypic Data ◽  
Agricultural Technologies ◽  
High Throughput Phenotyping

Drought is a main factor limiting crop yields. Modern agricultural technologies such as irrigation systems, ground mulching, and rainwater storage can prevent drought, but these are only temporary solutions. Understanding the physiological, biochemical, and molecular reactions of plants to drought stress is therefore urgent. The recent rapid development of genomics tools has led to an increasing interest in phenomics, i.e., the study of phenotypic plant traits. Among phenomic strategies, high-throughput phenotyping (HTP) is attracting increasing attention as a way to address the bottlenecks of genomic and phenomic studies. HTP provides researchers a non-destructive and non-invasive method yet accurate in analyzing large-scale phenotypic data. This review describes plant responses to drought stress and introduces HTP methods that can detect changes in plant phenotypes in response to drought.

scholarly journals Cloud-based intelligent self-diagnosis and department recommendation service using Chinese medical BERT

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Junshu Wang ◽  
Guoming Zhang ◽  
Wei Wang ◽  
Ka Zhang ◽  
Yehua Sheng
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Medical Service ◽  
Rapid Development ◽  
Medical Knowledge ◽  
Language Models ◽  
Computing Environment ◽  
Computing Power ◽  
Cloud Computing Environment ◽  
Proposed Model

AbstractWith the rapid development of hospital informatization and Internet medical service in recent years, most hospitals have launched online hospital appointment registration systems to remove patient queues and improve the efficiency of medical services. However, most of the patients lack professional medical knowledge and have no idea of how to choose department when registering. To instruct the patients to seek medical care and register effectively, we proposed CIDRS, an intelligent self-diagnosis and department recommendation framework based on Chinese medical Bidirectional Encoder Representations from Transformers (BERT) in the cloud computing environment. We also established a Chinese BERT model (CHMBERT) trained on a large-scale Chinese medical text corpus. This model was used to optimize self-diagnosis and department recommendation tasks. To solve the limited computing power of terminals, we deployed the proposed framework in a cloud computing environment based on container and micro-service technologies. Real-world medical datasets from hospitals were used in the experiments, and results showed that the proposed model was superior to the traditional deep learning models and other pre-trained language models in terms of performance.

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