Coding Distribution of Names, Words and Word Types by Initial Letters in Software

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3051 9357 7267 4783 1883 3622 5103 7440 0387 2954 3938 4664 9448 1785 1436 4887 0175 5257 10194 3450 0238 1279 6287 0003 0555 0552
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When the identity of an individual has to be resolved, secondary information, such as address, birthdate, profession, birthplace, parents names may still be required Which parameters are appropriate will di er according to circumstance The month of one s birth, for instance, tends to be remembered more reliably (98%) than the day of birth (96%) or the year of birth (95%) The relative freedom with which individuals in the United States can change their name makes positive identi cation of an individual di cult The social bene ts of this data-processing di culty may outweigh the costs Concerns about protection of privacy everywhere are discouraging the use of personal identi cation numbers Some European countries are discontinuing their use The use of numbers as identi ers will not disappear Individuals will have many numbers as identi ers: tax accounts, license numbers, bank accounts, employee numbers, etc
Sec 14-3 14-3
Compression of Data
COMPRESSION OF DATA
Data storage remains an important cost factor in many applications Often the representation of the data causes unused space and redundant representation of data While some redundancy may be part of schemes to improve reliability, signi cant opportunities for data compression remain Compression is possible when any of the following conditions are true: High frequency of unde ned or zero elements High frequency of small integers in xed size words Low precision requirements for oating point numbers Values which range over a limited domain Values which change slowly Fixed length spaces for character strings Redundancy in character strings Character sizes in excess of character set needs
We will consider only schemes which allow complete reconstruction of the data when the compressed data are again expanded In Sec 4-2 we encountered abbreviation methods for index entries, which did not permit full reconstruction There is, of course, a trade-o between the degree of compression and the cost of processing time Compression will also reduce data-transfer requirements so that le operations may become faster The total trade-o to be considered is compression CPU time versus le access speed and storage cost 14-3-1 Compression of Numeric Data Elements For computational purposes, it is desirable that numeric values occupy full words Frequently the data form matrices in order to allow rapid computation across rows and columns Whenever these data represent observations of real world phenomena, it is not unusual that positions are allocated for values that have not been collected (u) or are of zero value By storing only nonzero data values with their coordinates the zero elements are compressed out of the le The example below applies this idea to a record having a = 20 numeric data spaces Original record: 0,14,0,0,0,15,0,0,0,3,3,0,0,0,0,0,0,423,0,u; Coordinate representation: 2:14,6:15,10:3,11:3,18:423,20:u; Now the record requires 2a = 12 data spaces This method is reminiscent of the record format shown for the pile- le organization presented in Sec 3-1 An alternative is the use of a descriptive bit vector One bit is used per eld, a 0 indicates the absence of a value Now the same record is represented by 010001000110000001012 : 14,15,3,3,423,u; or, if the bit vector is shown as a single decimal integer 280068: 14,15,3,3,423,u;
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