Download Compact Data Structures. A Practical Approach by Gonzalo Navarro PDF

By Gonzalo Navarro

Compact information buildings aid symbolize facts in diminished area whereas permitting it to be queried, navigated, and operated in compressed shape. they're crucial instruments for successfully dealing with huge quantities of knowledge through exploiting the reminiscence hierarchy. in addition they lessen the assets wanted in allotted deployments and make greater use of the constrained reminiscence in low-end units. the sphere has built speedily, attaining a degree of adulthood that permits practitioners and researchers in software parts to profit from using compact info buildings. this primary accomplished ebook at the subject specializes in the buildings which are so much proper for functional use. Readers will find out how the constructions paintings, the way to select the proper ones for his or her software state of affairs, and the way to enforce them. Researchers and scholars within the sector will locate within the ebook a definitive advisor to the cutting-edge in compact facts constructions.

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Additional resources for Compact Data Structures. A Practical Approach

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Throughout the book we will also use bitsread and bitswrite to operate on single computer words, as if they were arrays of length 1. Programming We mapped A to W via a bitvector B, and using the highest bit first, for pedagogical reasons.

Sort S[1, σ ] by increasing S[i]. s, L. f ← S[i]. next, N. l. r. next. f ≤ N. 618 is the golden ratio. In particular, if the probabilities are obtained from the observed frequencies in a sequence of n symbols, then pmin ≥ 1n , and the maximum possible Huffman code length is logφ n. Thus, a 32-bit word can for sure hold the code of any file of size up to n = φ 32 > 222 (4 megabytes, if we assume one byte per symbol); this rises to 16 terabytes with 64-bit words. Thus, a few computer words suffice in all the conceivable cases to manipulate Huffman codes and write them to the output in constant time (Chapter 3 offers more details on handling the bits inside computer words).

Gagie (2006) gives more insights on the limits of the kth-order empirical entropy measure. Huffman codes. Huffman (1952) found the well-known algorithm to build an optimal prefix code. Schwartz and Kallick (1964) introduced canonical Huffman codes. Katona bibliography 37 and Nemetz (1976) showed that a symbol with probability p is assigned a Huffman codeword of length at most logφ (1/p) . Liddell and Moffat (2006) describe some further improvements over the fast decoding method we have presented.

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