28-09-2016, 02:31 PM
1456614699-6697RealTimeBigDataAnalyticalArchitectureforRemoteSensingApplicationdocx.docx (Size: 2.05 MB / Downloads: 5)
ABSTRACT:
The assets of remote senses digital world daily generatemassive volume of real-time data (mainly referred to theterm “Big Data”), where insight information has a potential significanceif collected and aggregated effectively. In today’s era, thereis a great deal added to real-time remote sensing Big Data than itseems at first, and extracting the useful information in an efficientmanner leads a system toward a major computational challenges,such as to analyze, aggregate, and store, where data are remotelycollected. Keeping in view the above mentioned factors, there is aneed for designing a system architecture that welcomes both realtime,as well as offline data processing. Therefore, in this paper,we propose real-time Big Data analytical architecture for remotesensing satellite application. The proposed architecture comprisesthree main units, such as 1) remote sensing Big Data acquisitionunit (RSDU); 2) data processing unit (DPU); and 3) data analysisdecision unit (DADU). First, RSDU acquires data from thesatellite and sends this data to the Base Station, where initial processingtakes place. Second, DPU plays a vital role in architecturefor efficient processing of real-time Big Data by providing filtration,load balancing, and parallel processing. Third, DADU is theupper layer unit of the proposed architecture, which is responsiblefor compilation, storage of the results, and generation of decisionbased on the results received from DPU. The proposed architecturehas the capability of dividing, load balancing, and parallelprocessing of only useful data. Thus, it results in efficiently analyzingreal-time remote sensing Big Data using earth observatorysystem. Furthermore, the proposed architecture has the capabilityof storing incoming raw data to perform offline analysis onlargely stored dumps, when required. Finally, a detailed analysis ofremotely sensed earth observatory Big Data for land and sea areaare provided using Hadoop. In addition, various algorithms areproposed for each level of RSDU, DPU, and DADU to detect landas well as sea area to elaborate the working of an architecture.
EXISTING SYSTEM:
Most recently designed sensors used in the earth and planetary observatory system are generating continuous stream of data.
Moreover, majority of work have been done in the various fields of remote sensory satellite image data, such as change detection, gradient-based edge detection, region similarity based edge detection, and intensity gradient technique for efficient intraprediction.
DISADVANTAGES OF EXISTING SYSTEM:
Consequences of transformation of remotely senseddata to the scientific understanding are a critical task.
Normally, the data collected from remote areas are not in a format ready for analysis.
In remote access networks, where the data source such assensors can produce an overwhelming amount of raw data.
PROPOSED SYSTEM:
In this paper, we referred the highspeedcontinuous stream of data or high volume offline data to“Big Data,” which is leading us to a new world of challenges.
This paper presents a remote sensing Big Data analytical architecture, which is used to analyze real time, as well as offline data. At first, the data are remotely preprocessed, which is then readable by the machines. Afterward, this useful information is transmitted to the Earth Base Station for further data processing.
Earth Base Station performs two types of processing, such as processing of real-time and offline data. In case of the offline data, the data are transmitted to offline data-storage device.
The incorporation of offline data-storage device helps in later usage of the data, whereas the real-time data is directly transmitted to the filtration and load balancer server, where filtration algorithm is employed, which extracts the useful information from the Big Data.
On the other hand, the load balancer balances the processing power by equal distribution of the real-time data to the servers. The filtration and load-balancing server not only filters and balances the load, but it is also used to enhance the system efficiency.
The proposed architecture and the algorithms are implemented in Hadoop usingMapReduce programming by applying remote sensing earth observatory data.
The proposed architecture is composed of three major units, such as 1) RSDU; 2) DPU; and 3) DADU. These units implement algorithms foreach level of the architecture depending on the required analysis.
ADVANTAGES OF PROPOSED SYSTEM:
With data acquisition, in which much ofthe data are of no interest that can be filtered or compressed byorders of magnitude. With a view to using such filters, they donot discard useful information.
With data extraction, which drags out the useful information from the underlying sources and delivers it in a structured formation suitable for analysis. For instance, the data set is reduced to single-class label to facilitate analysis, even though the first thing that we used to think about Big Data as always describing the fact.
The incorporation of offline data-storage device helps in later usage of the data,
The load balancer balances the processing powerby equal distribution of the real-time data to the servers.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
System : Pentium IV 2.4 GHz.
Hard Disk : 40 GB.
Floppy Drive : 1.44 Mb.
Monitor : 15 VGA Colour.
Mouse : Logitech.
Ram : 512 Mb.
SOFTWARE REQUIREMENTS:
Operating system : Windows 7/UBUNTU.
Coding Language : Java 1.7 ,Hadoop 0.8.1
IDE : Eclipse
Database : MYSQL