By Keng Siau
Complicated subject matters in Database learn positive aspects the newest, state of the art examine findings facing all points of database administration, platforms research and layout and software program engineering. This ebook offers details that's instrumental within the development and improvement of conception and perform on the topic of details know-how and administration of knowledge assets.
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Extra resources for Advanced Topics in Database Research, Vol. 1
Insertion Constraints Let us consider the way the alternative storage structures (ternary vs. binary) allow insertion of similar tuples. We should keep in mind that we are comparing the ability of the 28 Jones & Song Table 3: Lossless and FD preserving decompositions Case # Ternary Binary Impositions Potential Lossless Cardinality Decomposition (X:Y:Z) Potential FD Preserving Decompositions 1:1:1 (X:Y) = (M:1) (XY)(XZ) None 2 1:1:1 (X:Y) = (1:1) (XY)(XZ) -or(XY)(YZ) (XY)(XZ) -or(XY)(YZ) 3 1:1:1 (X:Y) = (M:1) (Z:Y) = (M:1) (XY)(XZ) -or(XZ)(ZY) (XY)(XZ) -or(XZ)(ZY) 4 1:1:1 (X:Y) = (M:1) (X:Z) = (1:1) (XY)(XZ) -or(XZ)(ZY) (XY)(XZ) -or(XZ)(ZY) 5 M:1:1 (X:Y) = (M:1) (XY)(XZ) (XY)(XZ) 6 M:1:1 (Y:Z) = (M:1) (XY)(YZ) None 7 M:1:1 (Y:Z) = (1:1) (XY)(YZ) -or(XZ)(ZY) (XY)(YZ) -or(XZ)(ZY) 8 M:1:1 (X:Y) = (M:1) (Y:Z) = (1:1) (XY)(YZ) -or(XZ)(ZY) -or(XY)(XZ) (XY)(YZ) -or(XZ)(ZY) 9 M:1:1 (X:Y) = (M:1) (Y:Z) = (1:M) (XZ)(ZY) -or(XY)(XZ) (XZ)(ZY) 10 M:N:1 (X:Z) = (M:1) (XY)(XZ) (XY)(XZ) 11 M:N:1 (X:Z) = (M:1) (Y:Z) = (M:1) (XY)(XZ) -or(XY)(YZ) None 12 M:N:P Not Allowed None None TE AM FL Y 1 structures to ensure enforcement of all constraints present (typically identified by the implicit functional dependencies).
These problems are due to causes ranging from a difficulty in identifying legitimate ternary relationships in practical situations to the lack of understanding of the construct in relation to the basis of normalization upon which the relational model is grounded. Song, Evan, and Park (1995) provide a comparative analysis of conceptual modeling notations. While all of the notations had some allowance for ternary modeling, none of the CASE tools included in the study allowed for the use and translation of ternary relationships.
1. Setup: prepare 50 inputs to the operations (the setup is not timed); 2. Cold run: run the operation 50 times, on the 50 inputs precomputed in the setup phase; then, if the operation is an update, commit the changes once for all 50 operations; 3. Warm run: repeat the operation 50 times with the same input to test the effect of caching; again, perform a commit if the operation was an update. 38 Darmont & Schneider Figure 2: HyperModel database schema Parent/Children (Aggregation 1-N) * 1 * NODE RefTo/RefFrom (Association M-N) uniqueId ten hundred thousand million * * PartOf/Parts (Aggregation M-N) * (Specialization) • • • • • • • bitMap width height TE text FORM NODE AM FL Y TEXT NODE The 20 possible operations belong to seven different kinds: Name Lookup: retrieve one randomly selected node; Range Lookup: retrieve the nodes satisfying a range predicate based on an attribute value; Group Lookup: follow the relationships one level from a randomly selected starting node; Reference Lookup: reverse Group Lookup; Sequential Scan: visit all the nodes; Closure Traversal: Group Lookup up to a predefined depth; Editing: update one node.