REAL-TIME BIG DATA ANALYTICAL ARCHITECTURE FOR REMOTE SENSING APPLICATION
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 leads a system toward a major computational challenges, such as to analyze, aggregate, and store, where data are remotely collected. Keeping in view the above mentioned factors, there is a need for designing a system architecture that welcomes both realtime, as well as offline data processing. In this paper, we propose real-time Big Data analytical architecture for remote sensing satellite application.
The proposed architecture comprises three main units:
1) Remote sensing Big Data acquisition unit (RSDU);
2) Data processing unit (DPU); and
3) Data analysis decision unit (DADU).
First, RSDU acquires data from the satellite and sends this data to the Base Station, where initial processing takes place. Second, DPU plays a vital role in architecture for efficient processing of real-time Big Data by providing filtration, load balancing, and parallel processing. Third, DADU is the upper layer unit of the proposed architecture, which is responsible for compilation, storage of the results, and generation of decision based on the results received from DPU.
Recently, a great deal of interest in the field of Big Data and its analysis has risen mainly driven from extensive number of research challenges strappingly related to bonafide applications, such as modeling, processing, querying, mining, and distributing large-scale repositories. The term “Big Data” classifies specific kinds of data sets comprising formless data, which dwell in data layer of technical computing applications and the Web. The data stored in the underlying layer of all these technical computing application scenarios have some precise individualities in common, such as 1) largescale data, which refers to the size and the data warehouse; 2) scalability issues, which refer to the application’s likely to be running on large scale (e.g., Big Data); 3) sustain extraction transformation loading (ETL) method from low, raw data to well thought-out data up to certain extent; and 4) development of uncomplicated interpretable analytical over Big Data warehouses with a view to deliver an intelligent and momentous knowledge for them.
Big Data are usually generated by online transaction, video/audio, email, number of clicks, logs, posts, social network data, scientific data, remote access sensory data, mobile phones, and their applications. These data are accumulated in databases that grow extraordinarily and become complicated to confine, form, store, manage, share, process, analyze, and visualize via typical database software tools. Advancement in Big Data sensing and computer technology revolutionizes the way remote data collected, processed, analyzed, and managed. Particularly, 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.
In this paper, we referred the high speed continuous stream of data or high volume offline data to “Big Data,” which is leading us to a new world of challenges. Such consequences of transformation of remotely sensed data to the scientific understanding are a critical task. Hence the rate at which volume of the remote access data is increasing, a number of individual users as well as organizations are now demanding an efficient mechanism to collect, process, and analyze, and store these data and its resources. Big Data analysis is somehow a challenging task than locating, identifying, understanding, and citing data. Having a large-scale data, all of this has to happen in a mechanized manner since it requires diverse data structure as well as semantics to be articulated in forms of computer-readable format.
However, by analyzing simple data having one data set, a mechanism is required of how to design a database. There might be alternative ways to store all of the same information. In such conditions, the mentioned design might have an advantage over others for certain process and possible drawbacks for some other purposes. In order to address these needs, various analytical platforms have been provided by relational databases vendors. These platforms come in various shapes from software only to analytical services that run in third-party hosted environment. In remote access networks, where the data source such as sensors can produce an overwhelming amount of raw data.
We refer it to the first step, i.e., data acquisition, in which much of the data are of no interest that can be filtered or compressed by orders of magnitude. With a view to using such filters, they do not discard useful information. For instance, in consideration of new reports, is it adequate to keep that information that is mentioned with the company name? Alternatively, is it necessary that we may need the entire report, or simply a small piece around the mentioned name? The second challenge is by default generation of accurate metadata that describe the composition of data and the way it was collected and analyzed. Such kind of metadata is hard to analyze since we may need to know the source for each data in remote access.
BIG DATA AND CLOUD COMPUTING: CURRENT STATE AND FUTURE OPPORTUNITIES
AUTHOR: D. Agrawal, S. Das, and A. E. Abbadi
PUBLISH: Proc. Int. Conf. Extending Database Technol. (EDBT), 2011, pp. 530–533.
Scalable database management systems (DBMS)—both for update intensive application workloads as well as decision support systems for descriptive and deep analytics—are a critical part of the cloud infrastructure and play an important role in ensuring the smooth transition of applications from the traditional enterprise infrastructures to next generation cloud infrastructures. Though scalable data management has been a vision for more than three decades and much research has focussed on large scale data management in traditional enterprise setting, cloud computing brings its own set of novel challenges that must be addressed to ensure the success of data management solutions in the cloud environment. This tutorial presents an organized picture of the challenges faced by application developers and DBMS designers in developing and deploying internet scale applications. Our background study encompasses both classes of systems: (i) for supporting update heavy applications, and (ii) for ad-hoc analytics and decision support. We then focus on providing an in-depth analysis of systems for supporting update intensive web-applications and provide a survey of the state-of-theart in this domain. We crystallize the design choices made by some successful systems large scale database management systems, analyze the application demands and access patterns, and enumerate the desiderata for a cloud-bound DBMS.
CHANGE DETECTION IN SYNTHETIC APERTURE RADAR IMAGE BASED ON FUZZY ACTIVE CONTOUR MODELS AND GENETIC ALGORITHMS
AUTHOR: J. Shi, J. Wu, A. Paul, L. Jiao, and M. Gong
PUBLISH: Math. Prob. Eng., vol. 2014, 15 pp., Apr. 2014.
This paper presents an unsupervised change detection approach for synthetic aperture radar images based on a fuzzy active contour model and a genetic algorithm. The aim is to partition the difference image which is generated from multitemporal satellite images into changed and unchanged regions. Fuzzy technique is an appropriate approach to analyze the difference image where regions are not always statistically homogeneous. Since interval type-2 fuzzy sets are well-suited for modeling various uncertainties in comparison to traditional fuzzy sets, they are combined with active contour methodology for properly modeling uncertainties in the difference image. The interval type-2 fuzzy active contour model is designed to provide preliminary analysis of the difference image by generating intermediate change detection masks. Each intermediate change detection mask has a cost value. A genetic algorithm is employed to find the final change detection mask with the minimum cost value by evolving the realization of intermediate change detection masks. Experimental results on real synthetic aperture radar images demonstrate that change detection results obtained by the improved fuzzy active contour model exhibits less error than previous approaches.
A BIG DATA ARCHITECTURE FOR LARGE SCALE SECURITY MONITORING
AUTHOR: S. Marchal, X. Jiang, R. State, and T. Engel
PUBLISH: Proc. IEEE Int. Congr. Big Data, 2014, pp. 56–63.
Network traffic is a rich source of information for security monitoring. However the increasing volume of data to treat raises issues, rendering holistic analysis of network traffic difficult. In this paper we propose a solution to cope with the tremendous amount of data to analyse for security monitoring perspectives. We introduce an architecture dedicated to security monitoring of local enterprise networks. The application domain of such a system is mainly network intrusion detection and prevention, but can be used as well for forensic analysis. This architecture integrates two systems, one dedicated to scalable distributed data storage and management and the other dedicated to data exploitation. DNS data, NetFlow records, HTTP traffic and honeypot data are mined and correlated in a distributed system that leverages state of the art big data solution. Data correlation schemes are proposed and their performance are evaluated against several well-known big data framework including Hadoop and Spark.
Existing methods inapplicable on standard computers it is not desirable or possible to load the entire image into memory before doing any processing. In this situation, it is necessary to load only part of the image and process it before saving the result to the disk and proceeding to the next part. This corresponds to the concept of on-the-flow processing. Remote sensing processing can be seen as a chain of events or steps is generally independent from the following ones and generally focuses on a particular domain. For example, the image can be radio metrically corrected to compensate for the atmospheric effects, indices computed, before an object extraction based on these indexes takes place.
The typical processing chain will process the whole image for each step, returning the final result after everything is done. For some processing chains, iterations between the different steps are required to find the correct set of parameters. Due to the variability of satellite images and the variety of the tasks that need to be performed, fully automated tasks are rare. Humans are still an important part of the loop. These concepts are linked in the sense that both rely on the ability to process only one part of the data.
In the case of simple algorithms, this is quite easy: the input is just split into different non-overlapping pieces that are processed one by one. But most algorithms do consider the neighborhood of each pixel. As a consequence, in most cases, the data will have to be split into partially overlapping pieces. The objective is to obtain the same result as the original algorithm as if the processing was done in one go. Depending on the algorithm, this is unfortunately not always possible.
- A reader that loads the image, or part of the image in memory from the file on disk;
- A filter which carries out a local processing that does not require access to neighboring pixels (a simple threshold for example), the processing can happen on CPU or GPU;
- A filter that requires the value of neighboring pixels to compute the value of a given pixel (a convolution filter is a typical example), the processing can happen on CPU or GPU;
- A writer to output the resulting image in memory into a file on disk, note that the file could be written in several steps. We will illustrate in this example how it is possible to compute part of the image in the whole pipeline, incurring only minimal computation overhead.
We present 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. Furthermore, the filtered data are then processed by the parallel servers and are sent to data aggregation unit (if required, they can store the processed data in the result storage device) for comparison purposes by the decision and analyzing server. The proposed architecture welcomes remote access sensory data as well as direct access network data (e.g., GPRS, 3G, xDSL, or WAN). The proposed architecture and the algorithms are implemented in applying remote sensing earth observatory data.
We proposed architecture has the capability of dividing, load balancing, and parallel processing of only useful data. Thus, it results in efficiently analyzing real-time remote sensing Big Data using earth observatory system. Furthermore, the proposed architecture has the capability of storing incoming raw data to perform offline analysis on largely stored dumps, when required. Finally, a detailed analysis of remotely sensed earth observatory Big Data for land and sea area are provided using .NET. In addition, various algorithms are proposed for each level of RSDU, DPU, and DADU to detect land as well as sea area to elaborate the working of architecture.
Big Data process high-speed, large amount of real-time remote sensory image data using our proposed architecture. It works on both DPU and DADU by taking data from medical application.
Our architecture for offline as well online traffic, we perform a simple analysis on remote sensing earth observatory data. We assume that the data are big in nature and difficult to handle for a single server.
The data are continuously coming from a satellite with high speed. Hence, special algorithms are needed to process, analyze, and make a decision from that Big Data. Here, in this section, we analyze remote sensing data for finding land, sea, or ice area.
We have used the proposed architecture to perform analysis and proposed an algorithm for handling, processing, analyzing, and decision-making for remote sensing Big Data images using our proposed architecture.
HARDWARE & SOFTWARE REQUIREMENTS:
v Processor – Pentium –IV
- Speed – 1 GHz
- RAM – 256 MB (min)
- Hard Disk – 20 GB
- Floppy Drive – 44 MB
- Key Board – Standard Windows Keyboard
- Mouse – Two or Three Button Mouse
- Monitor – SVGA
- Operating System : Windows XP or Win7
- Front End : Microsoft Visual Studio .NET 2008
- Script : C# Script
- Back End : MS-SQL Server 2005
- Document : MS-Office 2007