Remote Synchronization Using Differential Compression
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Fundamental Data Structures Contents
Fundamental Data Structures Contents 1 Introduction 1 1.1 Abstract data type ........................................... 1 1.1.1 Examples ........................................... 1 1.1.2 Introduction .......................................... 2 1.1.3 Defining an abstract data type ................................. 2 1.1.4 Advantages of abstract data typing .............................. 4 1.1.5 Typical operations ...................................... 4 1.1.6 Examples ........................................... 5 1.1.7 Implementation ........................................ 5 1.1.8 See also ............................................ 6 1.1.9 Notes ............................................. 6 1.1.10 References .......................................... 6 1.1.11 Further ............................................ 7 1.1.12 External links ......................................... 7 1.2 Data structure ............................................. 7 1.2.1 Overview ........................................... 7 1.2.2 Examples ........................................... 7 1.2.3 Language support ....................................... 8 1.2.4 See also ............................................ 8 1.2.5 References .......................................... 8 1.2.6 Further reading ........................................ 8 1.2.7 External links ......................................... 9 1.3 Analysis of algorithms ......................................... 9 1.3.1 Cost models ......................................... 9 1.3.2 Run-time analysis -
Data Structures
Data structures PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Thu, 17 Nov 2011 20:55:22 UTC Contents Articles Introduction 1 Data structure 1 Linked data structure 3 Succinct data structure 5 Implicit data structure 7 Compressed data structure 8 Search data structure 9 Persistent data structure 11 Concurrent data structure 15 Abstract data types 18 Abstract data type 18 List 26 Stack 29 Queue 57 Deque 60 Priority queue 63 Map 67 Bidirectional map 70 Multimap 71 Set 72 Tree 76 Arrays 79 Array data structure 79 Row-major order 84 Dope vector 86 Iliffe vector 87 Dynamic array 88 Hashed array tree 91 Gap buffer 92 Circular buffer 94 Sparse array 109 Bit array 110 Bitboard 115 Parallel array 119 Lookup table 121 Lists 127 Linked list 127 XOR linked list 143 Unrolled linked list 145 VList 147 Skip list 149 Self-organizing list 154 Binary trees 158 Binary tree 158 Binary search tree 166 Self-balancing binary search tree 176 Tree rotation 178 Weight-balanced tree 181 Threaded binary tree 182 AVL tree 188 Red-black tree 192 AA tree 207 Scapegoat tree 212 Splay tree 216 T-tree 230 Rope 233 Top Trees 238 Tango Trees 242 van Emde Boas tree 264 Cartesian tree 268 Treap 273 B-trees 276 B-tree 276 B+ tree 287 Dancing tree 291 2-3 tree 292 2-3-4 tree 293 Queaps 295 Fusion tree 299 Bx-tree 299 Heaps 303 Heap 303 Binary heap 305 Binomial heap 311 Fibonacci heap 316 2-3 heap 321 Pairing heap 321 Beap 324 Leftist tree 325 Skew heap 328 Soft heap 331 d-ary heap 333 Tries 335 Trie -
Tobiesen Ole.Pdf (2.195Mb)
Faculty of Science and Technology MASTER’S THESIS Study program/ Specialization: Spring semester, 2016 Master of Science in Computer Science Open Writer: Ole Tobiesen ………………………………………… (Writer’s signature) Faculty supervisor: Reggie Davidrajuh Thesis title: Data Fingerprinting -- Identifying Files and Tables with Hashing Schemes Credits (ECTS): 30 Key words: Pages: 145, including table of contents Data Fingerprinting and appendix Fuzzy Hashing Machine Learning Enclosure: 4 7z archives Merkle Trees Finite Fields Mersenne Primes Stavanger, 15 June 2016 Front page for master thesis Faculty of Science and Technology Decision made by the Dean October 30th 2009 Abstract INTRODUCTION: Although hash functions are nothing new, these are not lim- ited to cryptographic purposes. One important field is data fingerprinting. Here, the purpose is to generate a digest which serves as a fingerprint (or a license plate) that uniquely identifies a file. More recently, fuzzy fingerprinting schemes — which will scrap the avalanche effect in favour of detecting local changes — has hit the spotlight. The main purpose of this project is to find ways to classify text tables, and discover where potential changes or inconsitencies have happened. METHODS: Large parts of this report can be considered applied discrete math- ematics — and finite fields and combinatorics have played an important part. Ra- bin’s fingerprinting scheme was tested extensively and compared against existing cryptographic algorithms, CRC and FNV. Moreover, a self-designed fuzzy hashing algorithm with the preliminary name No-Frills Hash has been created and tested against Nilsimsa and Spamsum. NFHash is based on Mersenne primes, and uses a sliding window to create a fuzzy hash. -
String Hashing for Collection-Based Compression
String Hashing for Collection-Based Compression Andrew Gregory Peel orcid.org/0000-0001-6710-7513 Submitted in total fulfilment of the requirements of the degree of Doctor of Philosophy December 2015 Department of Computing and Information Systems Melbourne School of Engineering The University of Melbourne Produced on archival quality paper i Abstract Data collections are traditionally stored as individually compressed files. Where the files have a significant degree of similarity, such as genomes, incremental backup archives, versioned repositories, and web archives, additional compression can be achieved using references to matching data from other files in the collection. We describe compression using long-range or inter-file similarities as collection- based compression (CBC). The principal problem of CBC is the efficient location of matching data. A common technique for multiple string search uses an index of hashes, referred to as fingerprints, of strings sampled from the text, which are then compared with hashes of substrings from the search string. In this thesis, we investigate the suitability of this technique to the problem of CBC. A CBC system, cobald, was developed which employs a two-step scheme: a preliminary long-range delta encoding step using the fingerprint index, fol- lowed by a compression of the delta file by a standard compression utility. Tests were performed on data collections from two sources: snapshots of web crawls (54 Gbytes) and multiple versions of a genome (26 Gbytes). Using an index of hashes of fixed-length substrings of length 1 024 bytes, significantly improved compression was achieved. The compression of the web collection was six times more than the compression achieved by gzip and three times 7-zip.