Competent Way to Publish Heterogeneous Satellite Data on Web Deploying Semantic Technologies

One of the main concerns of academics and publishers is the dynamic role that information technologies play in the dissemination and distribution of satellite data online. Interpretation, interoperability, and scalability of sensor-generated heterogeneous data are crucial tasks. This can be addressed with the help of Semantic Web (SW), which has a vast number of unstructured items represented by linked data. Compression techniques help to create a more compact representation of the data, which can reduce processing over large amounts of data. This work proposes an effective C3-GCD method for publishing copious Geospatial Climate Data (GCD), which consists of three stages: Collect, Convert, and Compress. Stage I: Involves with collection of real-time data from Indian Meteorological Satellite along the southeastern coastal areas of India consisting 170 files of 648 MB with 12,82,000 records, Stage II: A machine-readable format, most likely Resource Description Framework (RDF), is derived out of the acquired heterogeneous data. Large RD F volumes result in difficulty of processing, inadequate administration, and limited scalability hence, Stage 111: A unique HDT+ compression strategy is developed by revisiting the state-of-art binary encoding. The proposed approach conserves approximately 78.96% of storage space with compression ratio of 5.67: 1 on average, allowing for the publication of copious amounts of data. Due to the proposed method's reduction of syntactic verbosity and data redundancy, any user can exchange, publish and analyze the data from anywhere open on web.