ean 13 excel 2010 Coding in Software

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If both zeros and unde ned values occur, the bit vector can be used to describe the three choices: 0 zero value 10 undefined value 11 actual nonzero value to be stored The bit vector for the same record, now without u stored as data, becomes: 01100011000111100000110102 : 14,15,3,3,423; In this case there is no bene t; where undefined values are plentiful the e ect will be felt This last bit vector is more di cult to manage, since its size will change depending on the values in the record In an application where values repeat frequently, four choices can be coded: 00 zero value 01 repeated value 10 undefined value 11 stored value so that the xed-size bit vector now reads 0 0110000001100000011010000000000001100102 : 14,15,3,423;
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This four-choice encoding for compression was applied to a le system which supported multiple databases using a total of more than three diskpacks and gave a reduction of 44% when applied to all les and 45% when applied only to records which became smaller Some large statistical les were reduced by more than 80%
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When the range of numeric values is small, the numbers can be placed into a smaller space It is unfortunately di cult to indicate boundaries within a bit string, so that the variable space assignment is awkward to implement If numeric data are coded as decimal digits of four bits each, one of the 24 = 16 codes can be used to provide a boundary Such a code can be generated using the four low order bits of the ASCII character code (Fig 14-1) for the ten digits, leaving space for six symbols, perhaps: * + - , and US For binary values the conversion to xed-size decimal digits does not make sense; it is better to use a xed size for a record based on the values to be stored A scan of the record can be used to determine the largest required space, and this size can be kept as another pre x with the record Using again the same values we nd that the largest data value ts into 9 bits, so that the record will have as contents 0 0110000000001100102 : 9,014|015|003|423; using | to indicate the boundaries of the 9-bit units Here one word plus 36 bits is required instead of four words The method is defeated by the presence of a single large value in the record An alternate input for compression can be a schema which speci es the range of values Then the record can be formatted appropriate to the range in any column In Example 8-7 the years were speci ed to range from 0 to 20, allowing storage of these values in elds of size log2 20 = 5 bits each Low-order positions of oating-point numbers can be truncated if the required precision is low Such a decision would also depend on the speci cations provided through the schema Instrumentation data is often of low precision, and 16-bit oating-point formats with 10-bit or three-digit precision and a range up to 109
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Sec 14-3
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have been devised If oating-point numbers are used to represent integers or fractions with a known binary point, then a representation using binary integers may save even more space If data elements represent attribute values which change slowly, it can be attractive to store the di erences of successive values A capability to store small numeric values e ciently is required to make this scheme e ective A sequence, 854,855,857,856,859,860,863; can be represented as 854,4,+1|+2|-1|+3|+1|+3; Four bits are su cient to store the successive di erences Unless we are dealing with transposed les, records containing sequential values tend to be rare The method could be applied to selected columns over many records, but then the record cannot be processed unless it is sequentially accessed 14-3-2 Compression of Character Strings Compression of character strings is more frequent than compression of numeric values One reason is that there are a number of large systems speci cally oriented toward textual data, and a pragmatic reason is that systems which have to support text have had already to support variable-length data elements and records, so that the additional complexity introduced by compression is less Most schemes to compress text limit themselves to compression of the text representation There is also a great deal of semantic redundancy within language sentences It is estimated that 50% of English text is redundant The redundant information, however, is not distributed in any regular fashion, so that automatic deletion of this redundancy is di cult In Sec 4-2 techniques for abbreviation of words in indexes were presented These techniques were e ective because the keys appeared in sorted order, so that the redundancies were easily eliminated The same techniques may at times be of use to compress data portions of les Low-order abbreviation, however, causes loss of information Abbreviations are frequently used independent of data-processing They may range from
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to M for Monsieur ADCOMSUBORDCOMAMPHIBSPAC for Administrative Command, Amphibious Forces, Paci c Fleet, Subordinate Command
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Such abbreviations present a degree of data compression prior to data entry Abbreviations can reduce data-entry volume as well as storage costs When abbreviations are developed speci cally to deal with the database, their e ect should be carefully considered Compression of terms within the computer may be more economical than the abbreviations which simplify manual processing, but a reduction of manual data-processing errors is extremely worthwhile
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