Application Strategy of Big Data Processing Technology in Intelligent Transportation

With the rapid development of China’s society and economy, the process of urbanization has been accelerated, and the transportation system has become more complicated, especially the frequent occurrence of traffic accidents, traffic congestions, and environmental pollution. In the context of the rapid development of Internet technology, digital technology, artificial intelligence technology, etc. We apply them to traffic management as effective ways to improve China’s traffic operation management. Based on big data processing technology, this paper discusses its application strategy in intelligent transportation, in hope of serving as a reference.

Download Full-text

Application of Big Data Processing Technology in Human Resource Management Information System

Journal of Physics Conference Series ◽

10.1088/1742-6596/1881/3/032029 ◽

2021 ◽

Vol 1881 (3) ◽

pp. 032029

Author(s):

Jiayi Zeng

Keyword(s):

Information System ◽

Big Data ◽

Resource Management ◽

Human Resource Management ◽

Data Processing ◽

Human Resource ◽

Management Information System ◽

Processing Technology ◽

Management Information ◽

Big Data Processing

Download Full-text

Big Data Processing and Mining for Next Generation Intelligent Transportation Systems

Jurnal Teknologi ◽

10.11113/jt.v63.1949 ◽

2013 ◽

Vol 63 (3) ◽

Cited By ~ 13

Author(s):

Jelena Fiosina ◽

Maxims Fiosins, Jörg P. Müller

Keyword(s):

Big Data ◽

Data Processing ◽

Real World ◽

Intelligent Transportation Systems ◽

Social Impact ◽

Intelligent Transportation ◽

Transportation Systems ◽

Optimization Techniques ◽

Next Generation ◽

Big Data Processing

The deployment of future Internet and communication technologies (ICT) provide intelligent transportation systems (ITS) with huge volumes of real-time data (Big Data) that need to be managed, communicated, interpreted, aggregated and analysed. These technologies considerably enhance the effectiveness and user friendliness of ITS, providing considerable economic and social impact. Real-world application scenarios are needed to derive requirements for software architecture and novel features of ITS in the context of the Internet of Things (IoT) and cloud technologies. In this study, we contend that future service- and cloud-based ITS can largely benefit from sophisticated data processing capabilities. Therefore, new Big Data processing and mining (BDPM) as well as optimization techniques need to be developed and applied to support decision-making capabilities. This study presents real-world scenarios of ITS applications, and demonstrates the need for next-generation Big Data analysis and optimization strategies. Decentralised cooperative BDPM methods are reviewed and their effectiveness is evaluated using real-world data models of the city of Hannover, Germany. We point out and discuss future work directions and opportunities in the area of the development of BDPM methods in ITS.

Download Full-text

Realization and Research of Intelligent system of client electricity information based on the Big Data Processing Technology

Proceedings of the 2016 6th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2016) ◽

10.2991/icadme-16.2016.110 ◽

2016 ◽

Author(s):

Ming-Xin Wen ◽

Ming-Hua Chu ◽

Lei Fang ◽

Wen-Jiao Chen

Keyword(s):

Big Data ◽

Data Processing ◽

Intelligent System ◽

Processing Technology ◽

Big Data Processing

Download Full-text

Research of wireless network traffic analysis using big data processing technology

10.1109/icumt54235.2021.9631682 ◽

2021 ◽

Author(s):

Kovtsur Maxim ◽

Kistruga Anton ◽

Mikhailova Anastasiya ◽

Potemkin Pavel ◽

Volkogonov Vladimir

Keyword(s):

Big Data ◽

Data Processing ◽

Wireless Network ◽

Network Traffic ◽

Traffic Analysis ◽

Processing Technology ◽

Big Data Processing ◽

Network Traffic Analysis

Download Full-text

Big Data Processing Technology Research and Application Prospects

2014 Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control ◽

10.1109/imccc.2014.63 ◽

2014 ◽

Cited By ~ 2

Author(s):

Li Mu ◽

Zhu Lei

Keyword(s):

Big Data ◽

Data Processing ◽

Processing Technology ◽

Technology Research ◽

Big Data Processing ◽

Application Prospects

Download Full-text

Improving The Quality Of Education Management With Big Data Processing Technology

10.15405/epes.20121.3 ◽

2020 ◽

Author(s):

Ekaterina V. Bebenina

Keyword(s):

Big Data ◽

Data Processing ◽

Processing Technology ◽

Quality Of Education ◽

Big Data Processing ◽

Education Management

Download Full-text

High-Performance Geospatial Big Data Processing System Based on MapReduce

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7100399 ◽

2018 ◽

Vol 7 (10) ◽

pp. 399 ◽

Cited By ~ 10

Author(s):

Junghee Jo ◽

Kang-Woo Lee

Keyword(s):

Big Data ◽

Spatial Analysis ◽

Data Processing ◽

Spatial Data ◽

High Performance ◽

Rapid Development ◽

Processing System ◽

Data Processing System ◽

Seamless Integration ◽

Big Data Processing

With the rapid development of Internet of Things (IoT) technologies, the increasing volume and diversity of sources of geospatial big data have created challenges in storing, managing, and processing data. In addition to the general characteristics of big data, the unique properties of spatial data make the handling of geospatial big data even more complicated. To facilitate users implementing geospatial big data applications in a MapReduce framework, several big data processing systems have extended the original Hadoop to support spatial properties. Most of those platforms, however, have included spatial functionalities by embedding them as a form of plug-in. Although offering a convenient way to add new features to an existing system, the plug-in has several limitations. In particular, while executing spatial and nonspatial operations by alternating between the existing system and the plug-in, additional read and write overheads have to be added to the workflow, significantly reducing performance efficiency. To address this issue, we have developed Marmot, a high-performance, geospatial big data processing system based on MapReduce. Marmot extends Hadoop at a low level to support seamless integration between spatial and nonspatial operations of a solid framework, allowing improved performance of geoprocessing workflow. This paper explains the overall architecture and data model of Marmot as well as the main algorithm for automatic construction of MapReduce jobs from a given spatial analysis task. To illustrate how Marmot transforms a sequence of operators for spatial analysis to map and reduce functions in a way to achieve better performance, this paper presents an example of spatial analysis retrieving the number of subway stations per city in Korea. This paper also experimentally demonstrates that Marmot generally outperforms SpatialHadoop, one of the top plug-in based spatial big data frameworks, particularly in dealing with complex and time-intensive queries involving spatial index.

Download Full-text