Exploring Height-Bounded Lempel-Ziv Encodings for Fast Access

المفاهيم الأساسية

Height-bounded LZ encodings offer efficient access to text positions with minimal space requirements.

الملخص

Height-bounded Lempel-Ziv (LZHB) encodings provide fast access to text positions with reduced space usage. Greedy algorithms efficiently find small LZHB representations. Theoretical bounds and practical experiments demonstrate the effectiveness of LZHB encodings in data compression.

الإحصائيات

We show that there exists a constant c such that the size ˆzHB(c log n) of the optimal (smallest) LZHB encoding whose height is bounded by c log n for any string of length n is O(ˆgrl).
Furthermore, we show that there exists a family of strings such that ˆzHB(c log n) = o(ˆgrl), thus making ˆzHB(c log n) one of the smallest known repetitiveness measures.
For example, for the encoding (1, a), (1, b), (3, 1), (1, c), (5, 2) of string ababacbabac, the heights are: 0, 0, 0, 1, 1, 1, 0, 1, 2, 2.
An LZ-like encoding induces an implicit referencing forest where each position references a previous occurrence.
The height of an optimal LZ-like encoding can be Θ(n).

اقتباسات

"We introduce height-bounded LZ encodings (LZHB), a new family of compressed representations."
"Any LZHB encoding whose referencing height is bounded by h allows access to an arbitrary position using O(h) predecessor queries."
"While computing the optimal LZHB representation seems difficult, linear and near linear time greedy algorithms efficiently find small representations."

الرؤى الأساسية المستخلصة من

Height-bounded Lempel-Ziv encodings

by Hideo Bannai... في arxiv.org 03-14-2024

https://arxiv.org/pdf/2403.08209.pdf

استفسارات أعمق

How do height-bounded encodings compare to other data compression techniques

Height-bounded encodings offer a unique approach to data compression by focusing on enabling fast access to arbitrary positions within the compressed data. This is achieved by restricting the height of the referencing forest in LZ-like encodings, allowing for efficient random access without complete decompression. In comparison to traditional data compression techniques like run-length encoding or grammar-based compressors, height-bounded encodings provide a balance between compression ratio and accessibility.
While run-length encoding excels in scenarios with repeated symbols but lacks efficient random access capabilities, grammar-based compressors offer strong compression ratios but may struggle with fast access requirements. Height-bounded encodings bridge this gap by providing relatively small parsing sizes while maintaining manageable heights for quick position retrieval. This makes them particularly suitable for applications where both compression efficiency and rapid data access are crucial.

What are the implications of NP-hardness in producing the smallest encoding

The NP-hardness associated with producing the smallest encoding poses significant challenges in practical implementations. The difficulty arises from determining the optimal configuration of phrases within an LZ-like encoding that minimizes size while adhering to specific constraints such as height bounds. As shown in the context provided, computing the optimal LZHB representation for any given height constraint is non-trivial and likely requires exponential time complexity.
The implications of NP-hardness mean that finding an exact solution for minimizing encoding size under certain constraints may not be feasible within reasonable time frames, especially as input sizes increase. In practice, this limitation necessitates heuristic approaches or approximation algorithms to generate suboptimal solutions efficiently. While these methods may not guarantee optimality, they can still provide effective results in real-world scenarios where computational resources are limited.

How can height constraints impact real-world applications beyond data compression

Height constraints have broader implications beyond data compression and can impact various real-world applications where repetitive structures exist or require efficient processing.
In bioinformatics, DNA sequences often exhibit repetitive patterns that can benefit from compressed representations with fast random access capabilities enabled by height-bounded encodings. By efficiently storing genetic information while allowing quick retrieval of specific gene sequences or motifs, researchers can streamline genomic analysis tasks such as sequence alignment or variant identification.
In network traffic analysis, packet payloads containing recurring signatures or protocols could leverage height-bounded encodings to reduce storage overhead while facilitating rapid content inspection at key points within network infrastructure. This enables faster anomaly detection, intrusion prevention measures, and forensic investigations without compromising on storage efficiency.
Moreover, multimedia applications dealing with images or videos featuring repetitive elements like textures or color gradients could utilize height-constrained encodings to achieve compact representations conducive to interactive browsing experiences and content manipulation tools requiring swift pixel-level access.