scholarly journals A MANAGEMENT OF REMOTE SENSING BIG DATA BASE ON STANDARD METADATA FILE AND DATABASE MANAGEMENT SYSTEM

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 653-657
Author(s):  
H. Wu ◽  
K. Fu
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Management System ◽  
Database Management ◽  
High Capacity ◽  
Remote Sensing Data ◽  
Large Data ◽  
Remote Sensing Image ◽  
Database Management System ◽  
Metadata Standard

Abstract. As a kind of information carrier which is high capacity, remarkable reliability, easy to obtain and the other features，remote sensing image data is widely used in the fields of natural resources survey, monitoring, planning, disaster prevention and the others (Huang, Jie, et al, 2008). Considering about the daily application scenario for the remote sensing image in professional departments, the demand of usage and management of remote sensing big data is about to be analysed in this paper.In this paper, by combining professional department scenario, the application of remote sensing image analysis of remote sensing data in the use and management of professional department requirements, on the premise of respect the habits, is put forward to remote sensing image metadata standard for reference index, based on remote sensing image files and database management system, large data serialization of time management methods, the method to the realization of the design the metadata standard products, as well as to the standard of metadata content indexed storage of massive remote sensing image database management.

The Impact of Big Data on Security

Big Data ◽  
2016 ◽  
pp. 1495-1518
Author(s):  
Mohammad Alaa Hussain Al-Hamami
Keyword(s):  
Social Media ◽  
Big Data ◽  
Management System ◽  
Database Management ◽  
Database Systems ◽  
Structured Data ◽  
Database Management System ◽  
Unstructured Data ◽  
And Behavior ◽  
The Impact

Big Data is comprised systems, to remain competitive by techniques emerging due to Big Data. Big Data includes structured data, semi-structured and unstructured. Structured data are those data formatted for use in a database management system. Semi-structured and unstructured data include all types of unformatted data including multimedia and social media content. Among practitioners and applied researchers, the reaction to data available through blogs, Twitter, Facebook, or other social media can be described as a “data rush” promising new insights about consumers' choices and behavior and many other issues. In the past Big Data has been used just by very large organizations, governments and large enterprises that have the ability to create its own infrastructure for hosting and mining large amounts of data. This chapter will show the requirements for the Big Data environments to be protected using the same rigorous security strategies applied to traditional database systems.

scholarly journals RESEARCH ON MULTI-SOURCE SATELLITE IMAGE DATABASE MANAGEMENT SYSTEM

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 565-568
Author(s):  
N. Fu ◽  
L. Sun ◽  
H. Z. Yang ◽  
J. Ma ◽  
B. Q. Liao
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Management System ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Database Management System ◽  
Sensor Data ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Effective Organization

Abstract. For the exploration and analysis of electricity, it is necessary to continuously acquire multi-star source, multi-temporal, multi-level remote sensing images for analysis and interpretation. Since the overall data has a variety of features, a data structure for multi-sensor data storage is proposed. On the basis of solving key technologies such as real-time image processing and analysis and remote sensing image normalization processing, the .xml file and remote sensing data geographic information file are used to realize effective organization between remote sensing data and remote sensing data. Based on GDAL design relational database, the formation of a relatively complete management system of data management, shared publishing and application services will maximize the potential value of remote sensing images in electricity remote sensing.

scholarly journals Integrating E-Governance with Big Data Analytics using Apache Spark

2020 ◽  
Vol 8 (6) ◽  
pp. 1609-1615
Keyword(s):  
Big Data ◽  
Management System ◽  
Database Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Database Management System ◽  
Apache Spark ◽  
World Government ◽  
Government Services ◽  
The Government

The constant innovations and rapid developments in the IT industry have revolutionized the thinking and mindset of the people throughout the world. Government departments have also been computerized to provide transparent, efficient and responsible government through e-governance. The government have been providing access to various websites or portal for filing complaints, uploading or downloading forms, pictures, data or PDFs to avail the government services. Enlightened citizens are frequently using the portal to access government services. Thus, the size and volume of data that need to be managed by government departments have been increasing drastically under e-governance. The traditional database management system is not designed to deal with such mix type of data. Moreover, the speed at which the e-governance generated data need to be processed is another big challenge being faced by traditional database system. All the abovesaid concerns can be solved by using the emerging technology - Big Data Analytics techniques. Big data analytic techniques can make the government more efficient and transparent by processing structured, unstructured or mixed types data at a great speed. In this paper, we shall understand the scenario for the need or the emergence of big data analytics in egovernance and knowhow of Apache Spark. This paper proposes a practical approach to integrate big data analytics with egovernance using Apache Spark. This paper also reflects how major issues of traditional database management system (mixed type datasets, speed and accuracy) can be resolved through the integration of big data analytics and e-governance.

scholarly journals Analisis Kinerja Web Server pada SIM Manajemen Diklat Poltekpel Sorong Menggunakan RDBMS MySQL dan MariaDB

2020 ◽  
Vol 1 (1) ◽  
pp. 12-20
Author(s):  
Jeffry Jeffry
Keyword(s):  
Big Data ◽  
Open Source ◽  
Virtual Machine ◽  
Management System ◽  
Database Management ◽  
Web Server ◽  
Database Management System

Perkembangan teknologi informasi dan data meningkat pesat di era big data seperti sekarang ini. Database Management System menjadi bagian utama yang sangat penting untuk mengontrol arus data. Penelitian ini membandingkan kinerja web server yang menggunakan RDBMS open source yang berbeda antara MySQL dan MariaDB. Pengujian dilakukan pada Oracle Virtual Machine Virtualbox menggunakan ApacheBench untuk mengukur kinerja Web Server pada SIM Manajemen Diklat Poltekpel Sorong. Hasil percobaan menunjukkan bahwa web server ketika menggunakan RDBMS MySQL cenderung memiliki performa yang cukup stabil ketika permintaan akses web di bawah 300 kali secara bersamaan yaitu pada 100,200 dan 300 kali berturut-turut sebesar 7.764/ms, 16.386/ms dan 30.025/ms. Namun, saat permintaan akses web di atas 300 secara bersamaan RDBMS MariaDB justru menunjukkan kinerja yang lebih baik. Hal ini ditunjukkan dengan permintaan akses 400 dan 500 kali web server secara bersamaan, waktu respon terlihat lebih cepat dibandingkan ketika menggunakan RDBMS MySQL berturut-turut sebesar 51.877/ms dan 54.702/ms sedangkan RDBMS mariaDB untuk permintaan akses web server secara bersamaan pada 100,200,300,400 dan 500 berturut-turut sebesar 14.213/ms, 25.642/ms, 40.831/ms, 48.021/ms dan 51.630/ms

scholarly journals A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data

2021 ◽  
Vol 33 (6) ◽  
pp. 1-20
Author(s):  
Hui Lu ◽  
Qi Liu ◽  
Xiaodong Liu ◽  
Yonghong Zhang
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Semantic Knowledge ◽  
Learning Technology ◽  
Semantic Classification ◽  
Sensing Data

With the rapid development of satellite technology, remote sensing data has entered the era of big data, and the intelligent processing of remote sensing image has been paid more and more attention. Through the semantic research of remote sensing data, the processing ability of remote sensing data is greatly improved. This paper aims to introduce and analyze the research and application progress of remote sensing image satellite data processing from the perspective of semantic. Firstly, it introduces the characteristics and semantic knowledge of remote sensing big data; Secondly, the semantic concept, semantic construction and application fields are introduced in detail; then, for remote sensing big data, the technical progress in the study field of semantic construction is analyzed from four aspects: semantic description and understanding, semantic segmentation, semantic classification and semantic search, focusing on deep learning technology; Finally, the problems and challenges in the four aspects are discussed in detail, in order to find more directions to explore.

Data Modeling for Big Data

2015 ◽  
Vol 6 (1) ◽  
pp. 1-11 ◽  
Author(s):  
M Misbachul Huda ◽  
Dian Rahma Latifa Hayun ◽  
Zhin Martun
Keyword(s):  
Big Data ◽  
Data Management ◽  
Relational Database ◽  
Management System ◽  
Database Management ◽  
Large Scale ◽  
Database Management System ◽  
Large Scale Data ◽  
Relational Database Management ◽  
Scale Data

Today the rapid growth of the internet and the massive usage of the data have led to the increasing CPU requirement, velocity for recalling data, a schema for more complex data structure management, the reliability and the integrity of the available data. This kind of data is called as Large-scale Data or Big Data. Big Data demands high volume, high velocity, high veracity and high variety. Big Data has to deal with two key issues, the growing size of the datasets and the increasing of data complexity. To overcome these issues, today researches are devoted to kind of database management system that can be optimally used for big data management. There are two kinds of database management system, relational database management system and nonrelational system that can be optimally used for big data management. There are two kinds of database management, Relational Database Management and Non-relational Database Management. This paper will give reviews about these two database management system, including description, vantage, structure and the application of each DBMS. Index Terms - Big Data, DBMS, Large-scale Data, Non-relational Database, Relational Database.

scholarly journals A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data

2021 ◽  
Vol 33 (6) ◽  
pp. 0-0
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Semantic Knowledge ◽  
Learning Technology ◽  
Semantic Classification ◽  
Sensing Data

With the rapid development of satellite technology, remote sensing data has entered the era of big data, and the intelligent processing of remote sensing image has been paid more and more attention. Through the semantic research of remote sensing data, the processing ability of remote sensing data is greatly improved. This paper aims to introduce and analyze the research and application progress of remote sensing image satellite data processing from the perspective of semantic. Firstly, it introduces the characteristics and semantic knowledge of remote sensing big data; Secondly, the semantic concept, semantic construction and application fields are introduced in detail; then, for remote sensing big data, the technical progress in the study field of semantic construction is analyzed from four aspects: semantic description and understanding, semantic segmentation, semantic classification and semantic search, focusing on deep learning technology; Finally, the problems and challenges in the four aspects are discussed in detail, in order to find more directions to explore.

The Impact of Big Data on Security

2015 ◽  
pp. 276-298 ◽  
Author(s):  
Mohammad Alaa Hussain Al-Hamami
Keyword(s):  
Social Media ◽  
Big Data ◽  
Management System ◽  
Database Management ◽  
Database Systems ◽  
Structured Data ◽  
Database Management System ◽  
Unstructured Data ◽  
And Behavior ◽  
The Impact

Big Data is comprised systems, to remain competitive by techniques emerging due to Big Data. Big Data includes structured data, semi-structured and unstructured. Structured data are those data formatted for use in a database management system. Semi-structured and unstructured data include all types of unformatted data including multimedia and social media content. Among practitioners and applied researchers, the reaction to data available through blogs, Twitter, Facebook, or other social media can be described as a “data rush” promising new insights about consumers' choices and behavior and many other issues. In the past Big Data has been used just by very large organizations, governments and large enterprises that have the ability to create its own infrastructure for hosting and mining large amounts of data. This chapter will show the requirements for the Big Data environments to be protected using the same rigorous security strategies applied to traditional database systems.

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