The.Algorithm.Design.Manual.Springer-Verlag.1998
The.Algorithm.Design.Manual.Springer-Verlag.1998 The.Algorithm.Design.Manual.Springer-Verlag.1998
Approximate String Matching Algorithms Mon Jun 2 23:33:50 EDT 1997 file:///E|/BOOK/BOOK5/NODE204.HTM (5 of 5) [19/1/2003 1:32:10]
Text Compression Next: Cryptography Up: Set and String Problems Previous: Approximate String Matching Text Compression Input description: A text string S. Problem description: A shorter text string S' such that S can be correctly reconstructed from S'. Discussion: Secondary storage devices fill up quickly on every computer system, even though their capacity doubles each year. Decreasing storage prices have only increased interest in data compression, since there is now more data to compress than ever before. Data compression is the algorithmic problem of finding alternative, space-efficient encodings for a given data file. With the rise of computer networks, a new mission for data compression has arisen, that of increasing the effective bandwidth of networks by reducing the number of bits before transmission. Data compression is a problem for which practical people like to invent ad hoc methods, designed for their particular applications. Sometimes these outperform general methods, but often they do not. The following issues arise in selecting the right data compression algorithm: ● Must we exactly reconstruct the input text after compression? - A primary issue in data compression algorithms is the question of lossy versus lossless encodings. Text applications typically demand lossless encodings, because users become disturbed whenever their data files file:///E|/BOOK/BOOK5/NODE205.HTM (1 of 4) [19/1/2003 1:32:11]
- Page 583 and 584: Point Location More recently, there
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- Page 589 and 590: Bin Packing Next: Medial-Axis Trans
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- Page 593 and 594: Medial-Axis Transformation Next: Po
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- Page 597 and 598: Polygon Partitioning number of piec
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- Page 601 and 602: Simplifying Polygons vertices and o
- Page 603 and 604: Shape Similarity Next: Motion Plann
- Page 605 and 606: Shape Similarity or how close it is
- Page 607 and 608: Motion Planning There is a wide ran
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- Page 611 and 612: Maintaining Line Arrangements Think
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- Page 615 and 616: Minkowski Sum where x+y is the vect
- Page 617 and 618: Set and String Problems Next: Set C
- Page 619 and 620: Set Cover Next: Set Packing Up: Set
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- Page 625 and 626: Set Packing Notes: An excellent exp
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- Page 629 and 630: String Matching and texts, I recomm
- Page 631 and 632: Approximate String Matching This sa
- Page 633: Approximate String Matching http://
- Page 637 and 638: Text Compression code string. ASCII
- Page 639 and 640: Cryptography Next: Finite State Mac
- Page 641 and 642: Cryptography ● How can I validate
- Page 643 and 644: Cryptography MD5 [Riv92] is the sec
- Page 645 and 646: Finite State Machine Minimization F
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- Page 649 and 650: Longest Common Substring than edit
- Page 651 and 652: Longest Common Substring include [A
- Page 653 and 654: Shortest Common Superstring Finding
- Page 655 and 656: Software systems Next: LEDA Up: Alg
- Page 657 and 658: LEDA Next: Netlib Up: Software syst
- Page 659 and 660: Netlib Algorithms Mon Jun 2 23:33:5
- Page 661 and 662: The Stanford GraphBase Next: Combin
- Page 663 and 664: Algorithm Animations with XTango Ne
- Page 665 and 666: Programs from Books Next: Discrete
- Page 667 and 668: Handbook of Data Structures and Alg
- Page 669 and 670: Algorithms from P to NP Next: Compu
- Page 671 and 672: Algorithms in C++ Next: Data Source
- Page 673 and 674: Textbooks Next: On-Line Resources U
- Page 675 and 676: On-Line Resources Next: Literature
- Page 677 and 678: People Next: Software Up: On-Line R
- Page 679 and 680: Professional Consulting Services Ne
- Page 681 and 682: Index A Up: Index - All Index: A ab
- Page 683 and 684: Index A artists steal ASA ASCII asp
Approximate String Matching<br />
<strong>Algorithm</strong>s<br />
Mon Jun 2 23:33:50 EDT 1997<br />
file:///E|/BOOK/BOOK5/NODE204.HTM (5 of 5) [19/1/2003 1:32:10]
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