Data Compression Principles
This stuff is explained further down, the data compression principles.Data compression:
- Is it the conversion of the more-often repeating data items and non-alphanumeric characters to 2-bit code letters that are needed?
- Takes up less space, yet, the time required for the saving and extraction is prolonged.
- A level of success chosen depend on the type of data.
- Students can gain better understanding of spatial data that have low level of spatial variability and only a few possible values.
- Very insufficient for characterizing a size-variability data and continuous surfaces.
- Instead of keeping all the data, it only extracts the important data, and makes a new, smaller one, that has everything it needs.
The compression ratio is the ratio of the two file sizes. e.g., original image is 100MB, after compression, the new file is 10MB. Then the compression ratio is 10:1.
What is Lossy Compression in DBMS?
Data storage is an important component first of all of any DBMS. On the other hand data management systems, due to limiting amount of drives, may encounter some difficulties in storage. This is where the data compression methods are executed and play their part. They facilitate information shrinkage, whilst preserving its authenticity and availability of utilization. This piece fathoms the notion of lossy compression in DBMS, introducing the latter’s term definition, its key terms, and use cases.