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Compression of 40% to 60% is reported on strings ranging from 400 to 1500 characters using up to 10 patterns in the lexicon The same technique is possible using arbitrary bit strings In this case the record can contain numeric or string data, as well as results from previous compression algorithms A Hu man code could also be generated dynamically, but here the code tree would have to be appended to the record The code tree is di cult to represent in a compact form A general decoding program for variable character codes will also be larger and slower than a program oriented to a particular Hu man encoding
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14-3-3 Implemention of Compression Compression schemes can be best evaluated by applying them to a representative sample of the data Several parameters have to be measured The simplest measurement is the reduction of storage requirements Changes in data transfer time are another bene t which should be evaluated The processing cost for compression and expansion can be signi cant and should not be neglected Since data retrieval is more frequent than update, complex compression schemes which generate encoding that are simple to expand are preferable Evaluation requires that the retrieval to update ratio be known More than one method can be used in combination with others In that case, the order in which compression algorithms are applied will make a di erence Replacement should precede Hu man coding, for instance, but if replacement leads to a more uniform use of character codes, subsequent Hu man coding will lose its advantage
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Processing Point for Compression Compression or expansion can be performed at various points in the data acquisition, storage, and retrieval process If compression is done soon after data entry and expansion is done just before information presentation, the volume of data to be handled throughout the system is less Data elements which only control selection of other data elements will not be expanded at all, so that processing time for expansion will be saved On the other hand, extensive usage of compressed data will require that all processing routines can manage compressed data If computational use of the data requires expansion, the data elements fetched may be expanded many times during processing Especially statistical-analysis programs tend to require data elements that are of consistent size and cannot cope with compressed elements
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The alternative is to assign data compression entirely to the le system File systems can be equipped with compression algorithms through the use of database procedures or le-system escape procedures, so that compression and expansion becomes fully transparent to the user Now the degree of compression performed can be adjusted by a database administrator according to storage versus CPU cost ratios applicable to a particular system or system era The automatic management of compression by the system can lessen the involvement of the user with the hardware and reduce binding of programs to system features A user who is worried about storage costs is apt to adopt the use of representation techniques which are not optimal from the human interface point of view Compression of numeric values in storage can be used to let all expanded numbers used in processing be of equal precision and size, without sacri cing storage space The use of xed data-element sizes in processing programs can increase the sharing of standard programs Hierarchical les, through their multiple record formats and lack of array structure, tend to bene t less from compression than les which are formatted as tables There are instances, as shown in Sec 4-5, where the bene ts of dense storage are a major factor in the decision to use a hierarchical approach The availability of compression can reduce the importance of this particular factor
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