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A tool in the developmemt of dependency theory is the universal relation All data is placed into a single abstract unnormalized relation representing all entities and all their attributes Many cells will be null The formal knowledge to manipulate the universal relation consists of a collection of various dependencies which relate the attributes In our structural model we avoid many of these issues by concentrating on the simpler view models The practical importance and su ciency of Boyce-Codd-normal form was presented by LeDoux82 Date in Tou74 expresses strongly the distinction between the database submodel and the database model Several related papers appear in King75 Integration of databases is presented by ElMasri79 , Hubbard81 , and Navathe82 It also appears as phase in Schkolnick78 and Lum79 Generalization of entities is addressed by Smith77 The concept of multiple linked databases is due to Litwin81L and tested within the POLYPHEME projectLanguages to describe the semantics have been de ned by Mylopoulos80 and Hammer81 Work by Earley71,73 is oriented toward language aspects of relational structures In order to construct relevant database models, the semantics of the interrrelational structure have played an important part in this chapter Work by Schmid in King75 and Manacher in Kerr75 was especially in uential ElMasri80 compares the how the structural concepts are modeled in alternative schemes Questions of semantic relevance of the structure of databases were analyzed in Langefors73 , Dehene e74 , Hainaut74 , Robinson in Douque76 , and Roussopoulos in Kerr75 Kent78 reports on experience with a conceptual modeling tool Integration of relations can lead to null entries Vassiliou80 , Goldstein81 , Lipski79 , and Zaniolo82 consider the problem of nulls, Imielinski81 extends the relational operations to deal with nulls Later work by Codd79 and Date82 extends the semantics of the original relational model The new model, RM/T, includes 5 referenced relation types and 8 integrity rules, which can be mappped into the structural model An exhaustive review is in Date82 Some of this work is in uenced by the concepts of semantic nets; a comprehensive reference is Schank73 Joins are extended by LaCroix76 Other types of models can be easily related to the concepts presented here Some of the models which are attracting attention are summarized here Entity models use the concept of the entity as their basic unit Data structures are composed of entities de ned by information requirements Entities have properties, which were in our model decomposed into attributes, subsets, or data elements Extending entities with the notion of relationships, as done by Chen76 , extends the descriptive capability, and this work has also spawned much interest, collections are found in Chen80 and successor proceedings Relationships may be simple (1 : n), as our ownership and reference connections, or m : n, as modelled by associations and other multiple connections Such relationships are essential, since the information is stored within them The guidance given by these models tends to be less formal than the rules based on relations Functional models formalize the relationships among entities Shipman81 provides a language to express them and Buneman82 provides a query language based on the functional concept An access-path model de nes the database as collections of sequential transformation sequences, required to obtain a physically coded entity in response to a logical query A rigorous hierarchy of the transformations allows the prediction of information loss and access cost at the various levels Entity and access-path models (DIAM) have been developed by Senko73 and compared with alternate approaches in Bachman75 and by Hall in Neuhold76 A level to model hardware functions is also available DIAM is shown to be capable of modeling many current concepts (Senko in Rustin72 , in Benci75 , in Kerr75 ,.

This is, in part, just a matter of tradition) Suppose you need to keep track of inventory for a book warehouse You expect to house up to 40,000 or even 50,000 copies of each book A signed short can only hold up to 32,767 values You might be tempted to use an unsigned short (which can hold up to 65,535 values), but it is easier and preferable to just use a signed int (with a maximum value of 2,147,483,647) That way, if you have a runaway bestseller, your program won t break (if you anticipate selling more than 2 billion copies of your book, perhaps you ll want to use a long!)..

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Throughout this book, we will use int wherever it works, even if short or byte might be usable alternatives. Memory is cheap these days, and programmer time is expensive. There are circumstances where the difference in memory usage would be significant (for example, if you are going to hold 1 billion values in memory), but we ll keep things simple by using the int type whenever possible.

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