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A road system, for example, can be represented with a graph: cities are nodes, and roads are edges Trees and hierarchies are special cases of graphs In a relation, we commonly model a graph with the adjacency list model In this model, we consider the graph s edges as directed edges from one vertex to another, and we represent these directed edges as tuples The nodes connected by an edge (which can be viewed as adjacent by virtue of the edge connecting them) are represented by attributes of the edge tuple Only nodes with a connection are represented The problem with the adjacency list model comes when you have to query it For example, if you need to nd all possible paths from city A to city B in the road system, your query must involve some kind of loop.



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(The loop can be hidden in a recursive common table expression, but it s still a loop) To make such queries faster and simpler, we can use a new relational operator, the.

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TClose operator. This unary operator returns the transitive closure of the original relation. The result is a relation with the same heading type as the original relation, but it includes tuples for all pairs of nodes with unbroken paths between them. Querying such a resulting relation is much simpler. You ll learn more about graphs, trees, hierarchies, and also how to compute the transitive closure of a graph in 12, Graphs, Trees, Hierarchies, and Recursive Queries. For now, just look at the graphical representation of the operator in Figure 3-9.

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For the sake of completeness, I ll add two more well-known operators that deal with relations with temporal data: Unpack and Pack. Although this book does not deal with temporal problems, many books do, such as Inside Microsoft SQL Server 2008: T-SQL Programming by Itzik Ben-gan et al. (Microsoft Press, 2009). Imagine that each tuple in a relation has an attribute representing the time interval for which the tuple is valid. Pretend that you have a time-interval type in your type collection, either system de ned or user de ned. A tuple with such a validity interval might look like this: {A, d4:d6} Without explicitly de ning the header of this tuple, let s say the proposition here says that supplier A is under contract (is a valid supplier) during the period from the point in time d4 to the point in time d6 and that points in time are discrete: d1, d2, d3, and so on, like calendar days, for example. You could also have additional tuples for the same supplier, like so: {A, d5:d7} {A, d8:d8}

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Here, the three tuples for supplier A have overlapping and abutting validity intervals. How can you nd the number of distinct time points supplier A was under a contract How can you combine tuples with adjacent and overlapping intervals into a single tuple that represents that supplier A was under contract continuously for one longer interval without interruptions Let s de ne the Unpack operator as a unary relational operator that returns a relation with all distinct valid time points projected over a set of input operators, the way the Summarize operator projects over input attributes. However, Unpack is doing the opposite of Summarize in terms of tuples returned; the relation returned is exploded to include tuples for all distinct valid time points. In the case of propositions from the example, the only input attribute for which time points can be unpacked is the supplier. The Pack operator does the opposite: it returns a relation with input attributes for which intervals are packed and intervals that are a union of all intervals from the source tuples for the same input attributes that overlap or meet. Note that union here is not a relational Union operator; it is an interval union, de ned only for intervals that overlap or meet. Figure 3-10 shows the Unpack and Pack operators graphically.

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