Engineering succinct DOM

Delpratt, O'Neil; Raman, Rajeev; Rahman, Naila

doi:10.1145/1352431.1352442

Cited by 5 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This presents several challenges: how to query the compressed data directly and efficiently, without the need for additional data structures (which can be many times larger than the compressed data), and how to retrieve the answers to the queries. In many practical cases, the naive approach of first decompressing the entire data and then processing it is completely unacceptable -for instance XML data compresses by an order of magnitude on disk [26] but expands by an order of magnitude when represented in-memory [22]; as we will shortly see, this approach is very problematic from an asymptotic perspective as well. Instead we want to support this functionality directly on the compressed data.…”

Section: Introductionmentioning

confidence: 99%

Random Access to Grammar-Compressed Strings and Trees

Bille¹,

Landau²,

Raman³

et al. 2015

SIAM J. Comput.

Self Cite

115

View full text Add to dashboard Cite

Abstract. Grammar based compression, where one replaces a long string by a small context-free grammar that generates the string, is a simple and powerful paradigm that captures (sometimes with slight reduction in efficiency) many of the popular compression schemes, including the Lempel-Ziv family, Run-Length Encoding, Byte-Pair Encoding, Sequitur, and Re-Pair. In this paper, we present a novel grammar representation that allows efficient random access to any character or substring without decompressing the string.Let S be a string of length N compressed into a context-free grammar S of size n. We present two representations of S achieving O(log N ) random access time, and either O(n·α k (n)) construction time and space on the pointer machine model, or O(n) construction time and space on the RAM. Here, α k (n) is the inverse of the k th row of Ackermann's function. Our representations also efficiently support decompression of any substring in S: we can decompress any substring of length m in the same complexity as a single random access query and additional O(m) time. Combining these results with fast algorithms for uncompressed approximate string matching leads to several efficient algorithms for approximate string matching on grammar compressed strings without decompression. For instance, we can find all approximate occurrences of a pattern P with at most k errors in time O(n(min{|P |k, k 4 + |P |} + log N ) + occ), where occ is the number of occurrences of P in S. Finally, we generalize our results to navigation and other operations on grammar-compressed ordered trees.All of the above bounds significantly improve the currently best known results. To achieve these bounds, we introduce several new techniques and data structures of independent interest, including a predecessor data structure, two "biased" weighted ancestor data structures, and a compact representation of heavy paths in grammars.Key words. grammar-based compression, straight-line program, approximate string matching, tree compression AMS subject classifications. 68P05, 68P301. Introduction. Modern textual or semi-structured databases, e.g. for biological and WWW data, are huge, and are typically stored in compressed form. A query to such databases will typically retrieve only a small portion of the data. This presents several challenges: how to query the compressed data directly and efficiently, without the need for additional data structures (which can be many times larger than the compressed data), and how to retrieve the answers to the queries. In many practical cases, the naive approach of first decompressing the entire data and then processing it is completely unacceptable -for instance XML data compresses by an order of magnitude on disk [25] but expands by an order of magnitude when represented in-memory [22]; as we will shortly see, this approach is very problematic from an

show abstract

Section: Introductionmentioning

confidence: 99%

Random Access to Grammar-Compressed Strings and Trees

Bille¹,

Landau²,

Raman³

et al. 2015

SIAM J. Comput.

Self Cite

115

View full text Add to dashboard Cite

show abstract

“…This presents several challenges: how to query the compressed data directly and efficiently, without the need for additional data structures (which can be many times larger than the compressed data), and how to retrieve the answers to the queries. In many practical cases, the naive approach of first decompressing the entire data and then processing it is completely unacceptable -for instance XML data compresses by an order of magnitude on disk [25] but expands by an order of magnitude when represented in-memory [22]; as we will shortly see, this approach is very problematic from an asymptotic perspective as well. Instead we want to support this functionality directly on the compressed data.…”

Section: Introductionmentioning

confidence: 99%

Random Access to Grammar-Compressed Strings

Bille¹,

Landau²,

Raman³

et al. 2011

Proceedings of the Twenty-Second Annual ACM-SIAM Symposium on Discrete Algorithms

Self Cite

View full text Add to dashboard Cite

Let S be a string of length N compressed into a contextfree grammar S of size n. We present two representations of S achieving O(log N ) random access time, and either O(n · α k (n)) construction time and space on the pointer machine model, or O(n) construction time and space on the RAM. Here, α k (n) is the inverse of the k th row of Ackermann's function. Our representations also efficiently support decompression of any substring in S: we can decompress any substring of length m in the same complexity as a single random access query and additional O(m) time. Combining these results with fast algorithms for uncompressed approximate string matching leads to several efficient algorithms for approximate string matching on grammar-compressed strings without decompression. For instance, we can find all approximate occurrences of a pattern P with at most k errors in time O(n(min{|P |k, k 4 + |P |} + log N ) + occ), where occ is the number of occurrences of P in S. Finally, we are able to generalize our results to navigation and other operations on grammar-compressed trees.All of the above bounds significantly improve the currently best known results. To achieve these bounds, we introduce several new techniques and data structures of independent interest, including a predecessor data structure, two "biased" weighted ancestor data structures, and a compact representation of heavy-paths in grammars.

show abstract

“…The ability to navigate efficiently in a tree is a basic prerequisite for most tree querying procedures. For instance, the DOM representation available in web browsers through JavaScript, provides tree navigation primitives (see, e.g., [9]). Tree navigation has been intensively studied in the context of succinct tree representations.…”

Section: Introductionmentioning

confidence: 99%