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barcode generator in c# code project DATA REDUNDANCY AND DATABASE DESIGN in Font
CHAPTER 12 DATA REDUNDANCY AND DATABASE DESIGN Print QR Code ISO/IEC18004 In None Using Barcode creator for Font Control to generate, create QRCode image in Font applications. www.OnBarcode.comEncode PDF 417 In None Using Barcode creation for Font Control to generate, create PDF417 2d barcode image in Font applications. www.OnBarcode.com67 Encode QR In None Using Barcode creator for Font Control to generate, create QR image in Font applications. www.OnBarcode.comCode 39 Generation In None Using Barcode encoder for Font Control to generate, create USS Code 39 image in Font applications. www.OnBarcode.com6 6PLWK 6PLWK RQHV RQHV
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Recognizing Barcode In Java Using Barcode Control SDK for BIRT reports Control to generate, create, read, scan barcode image in BIRT reports applications. www.OnBarcode.comGS1128 Creator In ObjectiveC Using Barcode drawer for iPad Control to generate, create EAN / UCC  14 image in iPad applications. www.OnBarcode.comNormalization Is Not Enough
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5. Actually it s in sixth normal form (6NF) as well. See Example 11 in the section Other Kinds of Redundancy, later in this chapter. CHAPTER 12 DATA REDUNDANCY AND DATABASE DESIGN
Now I turn to my second point. Consider the following decomposition of relvar S: SNT { S#, SNAME, STATUS } KEY { S# } STC { S#, STATUS, CITY KEY { S# } } Sample values are shown in Figure 124. As the figure shows, this decomposition is hardly very sensible (in particular, the fact that any given supplier has a given status appears twice), and yet it satisfies all of the normalization principles both projections are in 5NF, the decomposition is nonloss (in the strong sense of satisfying the fourth principle as well as the first), and dependencies are preserved. 6 6 6 6 6 6
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Orthogonality (I) Intuitively, the problem with the design illustrated in Figure 124 is clear: It has the property that (to use an obvious shorthand notation for tuples) the tuple <s#,n,st> appears in SNT if and only if the tuple <s#,st,c> appears in STC; equivalently, the tuple <s#,st> appears in the projection of SNT on S# and STATUS if and only if that very same tuple <s#,st> appears in the projection of STC on S# and STATUS meaning the projections in question are equal. In other words, the design implies some redundancy. So the principle we re looking for is obviously going to say something along the lines of: Don t do that! Here s a first attempt: Let A and B be distinct projections arising from nonloss decomposition of some relvar R. Then there should not exist nonloss decompositions of A and B into projections A1, A2, ..., Am and B1, B2, ..., Bn, respectively, such that some Ai (i = 1, 2, ..., m) is identically equal to some Bj (j = 1, 2, ..., n). CHAPTER 12 DATA REDUNDANCY AND DATABASE DESIGN
Or equivalently (my reason for the following restatement should become clear in the section Orthogonality (II) later in this chapter): Let A and B be distinct projections arising from nonloss decomposition of some relvar R. Then there should not exist nonloss decompositions of A and B into projections A1, A2, ..., Am and B1, B2, ..., Bn, respectively, such that a given tuple appears in some Ai (i = 1, 2, ..., m) if and only if it appears in some Bj (j = 1, 2, ..., n). Now, we could think of this principle, if we liked, as a fifth principle of normalization ; in fact, however, it s really just a special case or a logical consequence, rather of The Principle of Orthogonal Design, which I ll be discussing in detail in the section Orthogonality (II). Be that as it may, it should be obvious right away that there s no need to limit our attention to the situation in which A and B are obtained via nonloss decomposition from the same relvar R it applies to pairs of relvars in general. More generally, therefore, we have: Let A and B be distinct relvars. Then there should not exist nonloss decompositions of A and B into projections A1, A2, ..., Am and B1, B2, ..., Bn, respectively, such that a given tuple appears in some Ai (i = 1, 2, ..., m) if and only if it appears in some Bj (j = 1, 2, ..., n). As an aside, it s interesting to note that Codd himself attempted to deal with the kind of redundancy under discussion here in his very first two papers. In the 1969 paper, he said this: A set of relations is strongly redundant if it contains at least one relation [that] is derivable from the rest of the [relations in the set]. And he tightened up this definition somewhat in the 1970 paper: A set of relations is strongly redundant if it contains at least one relation that possesses a projection [that] is derivable from other projections of relations in the set. I should explain that when Codd says a relation r is derivable from a set S of relations, he means r is equal to the result of applying some sequence of relational operations join, projection, and so forth to relations from S. I do have a few comments on his definitions, however: First, the term relation should be replaced by the term relvar throughout (of course, this latter term wasn t introduced until many years later, and Codd never used it at all). Second, I think we can ignore the qualifier strongly; Codd was distinguishing between strong redundancy and something he called weak redundancy, but I don t think that distinction is relevant for present purposes (I mean, I don t think we need to consider weak redundancy here at all). The 1969 definition is subsumed by the 1970 definition, of course, because as we know every relvar R is identically equal to a certain projection of R namely, the identity projection.

