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👨💻 Currently studying "Informatics" at FMI.
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⤴️ Writing mostly C on a daily basis. -
📖 Right now I am reading Minko Markov's "Лекции по Алгоритми"
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🥇 Proud winner of Hack TUES ♾️, 2021/22.
System for Data Storing and Retrieving Based on Large Graph Processing
License: MIT License
👨💻 Currently studying "Informatics" at FMI.
📖 Right now I am reading Minko Markov's "Лекции по Алгоритми"
🥇 Proud winner of Hack TUES ♾️, 2021/22.
Support an efficient mechanism for inserting multiple entries. This has multiple versions. The first one is the ability to create a tree from a "stream" of entries, whereas the other form is, adding a set of entries into an already instantiated tree index.
FIXME
Create a decompression algorithm for decompressing previously compressed files with Huffman coding. Steps will be:
One of the core subsystems on which the whole system depends deals with indexing data. The information stored in Eugene is part of the index. This data structure is represented by a B-tree [1], [3] (multiple actually).
The generally accepted B-tree definition is as follows:
Def. B-tree is a self-balancing tree data structure that maintains sorted data and allows searches, sequential access, insertions, and deletions in logarithmic time.
Also, other requirements which the tree has to meet in order to qualify as B-tree are that:
Then, we can call this tree a B-tree of order m (also encountered as a branching factor).
The major advantages of using b-trees over other data structures typically are:
B-trees dramatically outperform other types of data structures when it comes to reading and writing relatively large blocks of data, i.e from consistent storage devices. Thus, it is a common choice for databases and filesystems.
A variation of b-trees, called b*-trees may have an additional improvement to the performance of the tree. [2]
[1] Bayer, R.; McCreight, E. (July 1970). "Organization and maintenance of large ordered indices"
[2] Comer, Douglas (June 1979), "The Ubiquitous B-Tree"
[3] Bayer, Rudolf (1971), Binary B-Trees for Virtual Memory
Realize algorithm for compression. Compression algorithms are normally used to reduce the size of a file without removing information. This can increase their entropy and make the files appear more random because all of the possible bytes become more common. The compression algorithms can also be useful when they're used to produce mimicry by running the compression functions in reverse.
For now, there are 2 compression algorithms that we want to realize with their basic functionalities for compression and decompression. Namely sparse column and sparse row.
For alternatives, I'm not sure but there are some good Huffman codding and encoding algorithms. There is also encoding that uses binary trees in trie format.
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