The assets of remote senses digital world daily generate massive volume of real-time data where insight information has a potential significance if collected and aggregated effectively. we propose real-time Big Data analytical architecture for remote sensing satellite application that welcomes both online and offline data processing

1. A Project Review
on
Real Time Big Data Analytical Architecture for
Remote Sensing Applications.
By
K.S.HARSHITHA 133F1A0506
D. CHARANI 133F1A0504
P. YAMINI 133F1A0525
B. Tech (IV-II semester)
KKC INSTITUTE OF TECHNOLOGY AND ENGINEERING
(Approved by AICTE, New Delhi & Affiliated to JNTUA, Anantapuramu)
PR Mangalam, Puttur -517584,Chittor Dist-A.P
Supervisor Head of the Department
Ms. K.Sujatha, M. Tech. Mr. S.R. Srikanth, M.Tech.
Assistant Professor, Assistant Professor & Head,
Department of CSE, KKCT, Puttur. Department of CSE, KKCT,Puttur.
Department Of Computer Science and Engineering
2013-2017

2. 1. Abstract
2. Existing System
3. Proposed System
4. System Configuration
5. Analysis Model
6. Proposed Architecture
7. Process Modules
8. Designing using UML Diagrams
9. Project Implementation & Results
10. Conclusion and future work

3. The assets of remote senses digital world daily generate
massive volume of real-time data where insight information has a
potential significance if collected and aggregated effectively. In
today’s era, there is a great deal added to real-time remote sensing
Big Data than it seems at first, and extracting the useful information
in an efficient manner. Keeping in view the above mentioned
factors, there is a need for designing a system architecture that
welcomes both real-time, as well as offline data processing.

4. The assets of remote senses digital world daily generate
massive volume of real-time data and that analyse the data only in
the online.
Disadvantages
1. Difficult to transform the sensed data to the scientific form.
2. Data collected are not in a format ready for analysis.
3. Only Online data can be processed.

5. The solution is by designing an architecture, which is used
to analyze real time data by directly transmitting to the filtration
and load balancer server ,as well as offline data for later usage.
Advantages
1. Capabilities of filtering, dividing, and parallel processing.
2. Better choice for real-time Big Data analysis.
3. Easy to analyze remote sensing data sets.

6. Hardware configuration
Processor : Pentium–IV
Speed : 1.1GHz
Ram : 4 GB (min)
Hard Disk : 20 GB
Software configuration
Operating System : Windows 7/UBUNTU
Programming Language : Java 1.7,Hadoop2
Database : MYSQL
IDE : Oracle Virtual Box
File Transfer : PuTTY, WinSCP

8.  Remote Sensing Big Data Acquisition
 Data processing
 Data Analysis and Decision
 Performance Analysis

13. What is Hadoop?
Hadoop is an open source, Java-based programming framework
that supports the processing and storage of extremely large data sets in
a distributed computing environment.
Components :
 Hadoop Distributed file System(HDFS)
 Hadoop Yet Another Resource Negotiator(YARN)
 Hadoop Map Reduce

14.  Start the Hadoop through Virtual Box.
 Transfer the dataset from windows to ubuntu by using PuTTY &
WinSCP.
 Activate the nodes & dump the dataset in HDFS.
 Perform Map Reduce Job by copying jar files to HDFS.
 Copy the related Graph data into /var/www/html
 To view the output open the graph in browser

15. It uses for the creation and management of guest virtual
machines and derivations of Windows, Linux, etc.,

17. It is a free & open source terminal emulator and network file
transfer application.

18. It is an open source SFTP,FTP client for Windows to transfer
files between a local and a remote computer securily.

28.  Valid Input : identified classes of valid input must
be accepted.
 Invalid Input : identified classes of invalid input must
be rejected.
 Functions : identified functions must be exercised.
 Output : identified classes of application output
must be exercised.
 Systems/ Procedures : interfacing systems or procedures
must be invoked.

29. The architecture efficiently processed and analyzed real-time
and offline remote sensing Big Data for decision-making.
For future work, we are planning to extend the proposed
architecture to make it compatible for applications like sensors and
social networking, earthquake prediction, etc.