Big Data Environment for Geospatial Data Analysis

被引:0
作者
Praveen, P. [1 ]
Babu, Ch. Jayanth [1 ]
Rama, B. [2 ]
机构
[1] SR Engn Coll, Warangal, Andhra Pradesh, India
[2] Kakatiya Univ, Warangal, Andhra Pradesh, India
来源
PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON COMMUNICATION AND ELECTRONICS SYSTEMS (ICCES) | 2016年
关键词
Geospatial data; Big Data; HDFS; Map Reduce;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Geo-information system produces huge and complex geospatial data with the process of collecting real time data through sensors devices. Ground surveying, remote sensing, mobile mapping are the major applications which produces geo-spatial data. There are many other applications like geo-located sensors, geo-tagged web contents, global navigation satellite system(GNSS), volunteered geographic information(VGI) tracking and so on. However, many issues would be answered when we reuse these huge data by applying the traditional data analysis tools. Big data is usually collections of huge data sets with sizes that exceed the storage capacity. This difficulty of storage and advantage of Big data handling tools availability, the data can be captured, managed and processed easily within optimal elapsed time. The hadoop is one of the framework with rich ecosystem which provides the distributed data storage through Hadoop Distributed File System (HDFS) and parallel processing through Map Reduce mechanism.
引用
收藏
页码:573 / 578
页数:6
相关论文
共 11 条
[1]  
[Anonymous], 2014, Machine-learning maestro michael jordan on the delusions of big data and other huge engineering efforts
[2]  
[Anonymous], 2009, NATL I STAND TECHNOL, DOI DOI 10.6028/NIST.SP.800-145
[3]  
Beyer M A., 2012, Gartner
[4]   Efficient Big Data Processing in Hadoop MapReduce [J].
Dittrich, Jens ;
Quiane-Ruiz, Jorge-Arnulfo .
PROCEEDINGS OF THE VLDB ENDOWMENT, 2012, 5 (12) :2014-2015
[5]  
Evans M.R., 2014, Big Data: Techniques and Technologies in Geoinformatics, P149
[6]  
Han J., DATA MINING CONCEPT
[7]  
Laney D, 2001, 3D DATA MANAGEMENT C
[8]  
Miller HJ, 2009, CH CRC DATA MIN KNOW, P1
[9]  
Morais C.D., 2012, Where is the Phrase "80% of Data is Geographic" From?
[10]  
Sester M., 2014, Abstracting Geographic Information in a Data Rich World, P119