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Constraint C1 can be implemented during reachability testing by associating each receiving event r in variant V with a color that is either black or white If the sending partner of r is changed to derive variant V, r s color is set to black, and this color is inherited by r in any sequences collected from V Black receiving events are excluded from race tables (even though they may have nonempty race sets), which prevents the sending partners of black receiving events from being changed again For example, in Fig 730a, variant V1 was derived by changing the sending partner of r4 (see Fig 729) Therefore, the color of r4 in V1 will be black, and this color will be inherited by r4 in Q1.

Because the interface will accept inputs from the user, we will need to create controls to accept these inputs. HTML forms elements are used to create such controls. The next chapter is dedicated to the importance and working of HTML forms. As a result, we will use the form-specific tags to create the controls in the user interface. Let s list the inputs for this page that will be accepted from the user. The user interface for the login page screen will accept the following data:

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Thus, r4 will be excluded from the heading of Q1 s race table, preventing the sending partner of r4 from being changed again when deriving race variants from Q1 Constraint C2 Each edge along a path must reconcile as many differences as possible A difference between source sequence n and target sequence n refers to a receiving event r that exists in both sequences but has different sending partners in each sequence In terms of these differences, reachability testing can be viewed as the process of transforming, through one or more variants, sequence n into sequence n Each variant resolves one or more differences between n and n Constraint C2 says that if there are differences that can be reconciled by an edge (eg, the sending partner of r in n is in the race set of r in n), these differences should be reconciled by this edge.

Statistical Dependence of Double Differences. We have seen how double differences can be linearly dependent. The problem we now address is that double differenced observations that involve a common receiver and common satellite are 21 statistically dependent. For example, at a given epoch, double differences LAB, 23 24 2 LAB and LAB are correlated due to the single differenced data in common, LAB. Any measurement error in this single difference will contribute exactly the same 21 error to each of the double differences. Therefore, a positive error in LAB is sta23 tistically more likely to be accompanied by a positive error in LAB. As another example, if we are processing a network using a reference satellite j and reference receiver A, all double differences in the linearly independent set will be staj tistically dependent because of the data in common, LA.

If no changes are found, the parent versions are updated to re ect the data in the leaf UOW, else application-speci c resolution managers are invoked, and they apply the business logic necessary to resolve con icts and arrive at the desired parent data state (in a similar way to what the manual procedures accomplish in a single-threaded multiuser system) Resolution managers are provided by the application developer and can be plugged in the framework The second concurrency mechanism uses the concept of predicates and transforms The application programmer has to code invocations in terms of predicates and transforms (eg, a debit transform is predicated by a check for positive balance) The predicates and transforms are implicitly logged during the UOW rehearsal phase as invocations are made on the objects versions it owns.

This means that when deriving a variant V, if there are receiving events whose sending partners can be changed but are not changed, these unchanged receiving events cannot be changed afterward in any sequences derived from V Recall that it is common for a variant to leave some receiving events unchanged since all combinations of changed and unchanged receiving events are enumerated in the race table Constraint C2 ensures that a particular set of changes occurs in only one variant To illustrate Constraint C2, consider sequence Q0 and its three variants in Fig 731 A variant and the sequence that was collected from it are shown in the same space-time diagram, with the variant above the dashed line and the rest of the sequence below it The SV-graph and spanning tree for these sequences are also shown in Fig 731.

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