Real-Time Big Data Analytical Architecture for Remote Sensing Application:

Point to remember:

1. What is remote sensing?2. What is big data?3. What is data analytical architecture?4. What is data processing?

Problem statement:

1. Scalability issues, which refer to the application, are likely to be running on large scale.

2. Extraction transformation loading method from low, raw data to well thought-out data up to certain extent;

3. Scalable data management has been a vision for more than three decades and much research has focused on large scale data management in traditional enterprise setting.

4. The run-time system takes care of the details of partitioning the input data, scheduling the program’s execution across a set of machines, handling machine failures, and managing the required inter-machine communication.

5. Difficult to handle & maintain huge amount in remote sensing application.

Proposed architectures:

1. Remote sensing big data acquisition unit.(RSDU)

Description of RSDU:

It collects the raw data from the earth atmosphere and send it to the ground station via downlink channel.

2. Data processing unit (DPU).

Description of RSDU:

The collected raw data information are separated with the help of filtration and load balancing algorithm , useful data for analysis since it only allows useful information, whereas the rest of the data are blocked and are discarded.

3. Data analytics decision unit (DADU).

Description of DADU:

DADU, which is responsible for compilation, storage of the results, and generation of decision based on the results received from DPU.

Proposed algorithms:

1. Filtration and Load Balancing Algorithm.

Description:

This algorithm takes satellite data or product and then filters and divides them into segments and performs load-balancing algorithm.

2. Processing and Calculation Algorithm.

Description:

The processing algorithm computes the results for dissimilar restrictions against each incoming block and sends them to the next level.

3. Aggregation and Compilation Algorithm.

Description:

It collects the results from each processing servers against each and then combines, organizes, and stores these results in RDBMS database.

4. Decision-making algorithm.

Description:

The algorithm varies from requirement to requirement and depends on the analysis needs.