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At a lower level of aggregation there will be more individual entries, increasing the cost of collection, storage, and processing There may also be a greater variety of entities, and a greater variety of appropriate attributes The bene t of having very detailed data may be hard to quantify In the case of a bill-of-materials problem, there will be little bene t in obtaining control of minor standard parts: bolts, nuts, washers, and lubricants, so that these will only be registered as aggregate supplies Other parts at the same hierarchical level may be expensive or specialized and warrant speci c record keeping When a database is envisaged to serve the needs of users who need di erent levels of aggregation, data at the most detailed level has to be collected Periodic or immediate updating of aggregate measures can be used to keep data at higher levels available When the entities and their attributes to be collected are de ned, the speci cation of what constitutes membership of an entity class has to be further re ned It is, for instance, not obvious whether the paint on a bicycle is a part or an attribute value Often there is an established basis which can be used to determine membership Rules of auditing, for instance, specify in detail the types of income and expenses which are to be summed into ledger categories The outcome of a taxonomic decision requires documentation to be used at data entry:
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The number of children is based on the children of either parent, living in the same household, and less than 18 years old
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An alternative speci cation can be by reference:
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The number of children is as de ned for federal tax purposes
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It is obvious that without such statements, di erent data collectors will record di erent information An extended schema provides a central repository for these speci cations 14-1-2 Classi cation When the entities and their attributes have been selected, the observation choices have to be classi ed Sometimes the decision is simple:
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The number of children of a mother is speci ed by the integers 0, 1, 2, The weight of the children The hair color of the children
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More frequently the observed data are not classi ed this simply Classi cation of
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requires further decisions We will list some classi cation choices below and then comment on their usage The classi cation of a type of observation is not always obvious Once a data element type is classi ed, however, important aspects of the domain for the attribute values are established and an appropriate representation can be selected Whenever a natural order exists, the data values can be ranked into a sequence, which will be exploited in coding and data-processing When there is no such natural order in the observations, an arti cial order may be speci ed by convention
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Sec 14-1
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Table 14-1 Sortability
Representation of Knowledge Classi cation of Attribute Values
Measurability Value choices Sample domain
Ranked
Metric Ordinal
Continuous Integer
weight number in stock friendliness
Unlimited name Limited haircolor Existential female A schema which provides a ranking speci cation was shown in Example 8-6; SOCRATE allows a domain to be speci ed by a list, (eg hair color(black, brown, red, blond)), and the order in the list de nes the ranking It remains, however, risky to formulate queries comparisons having GREATER or LESS operations unless the order de nition or collating sequence is clear to the user If the values are metric, the data values are determined by measurement Arithmetic operations can be applied to these data The values within a domain weight can be added or subtracted as well as compared This also means that operations which generate a mean or a standard deviation make sense When data is continuous, decisions about range and accuracy have to be made The limits imposed by computer hardware makes the choices often seem simple The fact, however, that a computer retains six-decimal-digit precision does not mean that this degree of precision is warranted in data collection Subsequent calculations can produce numbers of apparent accuracy which was not intrinsic to the original data
The response to a query: Joe weighs 680385 kg does not correspond in meaning with the data as they were entered: Joe s weight is 150 pounds Documentation in the schema of the accuracy of data entered can be used here to provide an appropriate truncation: 680 kg
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