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notes refer to papers that describe where negation can be used in recursive Datalog programs The view relations of a recursive program that contains a set of rules R are de ned to contain exactly the set of facts I computed by the iterative procedure DatalogFixpoint in Figure 510 The recursion in the Datalog program has been turned into an iteration in the procedure At the end of the procedure, infer(R, I) = I, and I is called a xed point of the program Consider the program de ning empl-jones, with the relation manager, as in Figure 511 The set of facts computed for the view relation empl-jones in each iteration appears in Figure 512 In each iteration, the program computes one more level of employees under Jones and adds it to the set empl-jones The procedure terminates when there is no change to the set empl-jones, which the system detects by nding I = Old I Such a termination point must be reached, since the set of managers and employees is nite On the given manager relation, the procedure Datalog-Fixpoint terminates after iteration 4, when it detects that no new facts have been inferred You should verify that, at the end of the iteration, the view relation empl-jones contains exactly those employees who work under Jones To print out the names of the employees supervised by Jones de ned by the view, you can use the query empl-jones(N ) To understand procedure Datalog-Fixpoint, we recall that a rule infers new facts from a given set of facts Iteration starts with a set of facts I set to the facts in the database These facts are all known to be true, but there may be other facts that are true as well1 Next, the set of rules R in the given Datalog program is used to infer what facts are true, given that facts in I are true The inferred facts are added to I, and the rules are used again to make further inferences This process is repeated until no new facts can be inferred For safe Datalog programs, we can show that there will be some point where no more new facts can be derived; that is, for some k, Ik+1 = Ik At this point, then, we have the nal set of true facts Further, given a Datalog program and a database, the xed-point procedure infers all the facts that can be inferred to be true.

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If you recall the discussion from 7, you know that it is important to do this to make certain that your titles will be completely visible when your movie is viewed on a TV set

1 The word fact is used in a technical sense to note membership of a tuple in a relation Thus, in the Datalog sense of fact, a fact may be true (the tuple is indeed in the relation) or false (the tuple is not in the relation)

Solving this equation for the current might not be pleasant, but the Laplace transform greatly simpli es the situation Now we take the Laplace transform of each piece On the left-hand side, we simply have the Laplace transform of the input voltage: v i (t) Vi (s) On the right-hand side, using Table 13-1, as a guide we nd L di 1 + dt C

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The McGraw Hill Companies, 2001

5

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If a recursive program contains a rule with a negative literal, the following problem can arise Recall that when we make an inference by using a ground instantiation of a rule, for each negative literal notq in the rule body we check that q is not present in the set of facts I This test assumes that q cannot be inferred later However, in the xed-point iteration, the set of facts I grows in each iteration, and even if q is not present in I at one iteration, it may appear in I later Thus, we may have made an inference in one iteration that can no longer be made at an earlier iteration, and the inference was incorrect We require that a recursive program should not contain negative literals, in order to avoid such problems Instead of creating a view for the employees supervised by a speci c manager Jones, we can create a more general view relation empl that contains every tuple (X, Y ) such that X is directly or indirectly managed by Y , using the following program (also shown in Figure 57): empl(X, Y ) : manager(X, Y ) empl(X, Y ) : manager(X, Z), empl(Z, Y ) To nd the direct and indirect subordinates of Jones, we simply use the query empl(X, Jones ) which gives the same set of values for X as the view empl-jones Most Datalog implementations have sophisticated query optimizers and evaluation engines that can run the preceding query at about the same speed they could evaluate the view empl-jones The view empl de ned previously is called the transitive closure of the relation manager If the relation manager were replaced by any other binary relation R, the preceding program would de ne the transitive closure of R

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Equating this result to Vi (s) and solving for I (s), we nd I (s) = sVi (s) + s L i(0) Vi (s) + L i(0) = 1 Ls 2 + Rs + 1/C Ls + R + Cs (1312)

The McGraw Hill Companies, 2001

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