Answer:
It we were asked to develop a new data compression tool, it is recommended to use Huffman coding since it is easy to implement and it is widely used.
Explanation:
The pros and the cons of Huffman coding
Huffman coding is one of the most simple compressing encoding schemes and can be implemented easily and efficiently. It also has the advantage of not being patented like other methods (e.g. arithmetic codingfor example) which however are superior to Huffman coding in terms of resulting code length.
One thing not mentioned so far shall not be kept secret however: to decode our 96 bit of “brief wit” the potential receiver of the bit sequence does need the codes for all letters! In fact he doesn’t even know which letters are encoded at all! Adding this information, which is also called the “Huffman table” might use up more space than the original uncompressed sentence!
However: for longer texts the savings outweigh the added Huffman table length. One can also agree on a Huffman table to use that isn’t optimized for the exact text to be transmitted but is good in general. In the English language for example the letters “e” and “t” occur most often while “q” and “z” make up the least part of an average text and one can agree on one Huffman table to use that on average produces a good (=short) result. Once agreed upon it doesn’t have to be transmitted with every encoded text again.
One last thing to remember is that Huffman coding is not restricted to letters and text: it can be used for just any symbols, numbers or “abstract things” that can be assigned a bit sequence to. As such Huffman coding plays an important role in other compression algorithms like JPG compression for photos and MP3 for audio files.
The pros and the cons of Lempel-Ziv-Welch
The size of files usually increases to a great extent when it includes lots of repetitive data or monochrome images. LZW compression is the best technique for reducing the size of files containing more repetitive data. LZW compression is fast and simple to apply. Since this is a lossless compression technique, none of the contents in the file are lost during or after compression. The decompression algorithm always follows the compression algorithm. LZW algorithm is efficient because it does not need to pass the string table to the decompression code. The table can be recreated as it was during compression, using the input stream as data. This avoids insertion of large string translation table with the compression data.
World Wide Web (WWW)? I honestly don't know.
Answer:
The answer to the following question is "false".
Explanation:
In computer science, ROM stands for read-only memory. ROM is an electronic device that stores data. It is non-volatile which means that the data is saved even if the part drops power. Once information has been signed into a ROM, it cannot be removed and can only be read. So the answer to this question is "false".
Answer:
The difference between the centralized and the distributed database architecture are as follows:
- The centralized database is the type of database which works on the single file in the database. Whereas, the distributed database works with the multiple files in the database.
- The centralized database refers to the proper planning and the helps in the decision making in the management system. Whereas, in the distributed database system the data are mainly distributed in the multiple files and the user can easily access the near by file in the database system.
- In the centralized database, if the database are get fail then the overall system come to halt. On the other hand in the distributed system, if the component get failed then the performance of the system are get reduced.
