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When several transactions execute concurrently in the database, the consistency of data may no longer be preserved It is necessary for the system to control the interaction among the concurrent transactions, and this control is achieved through one of a variety of mechanisms called concurrency-control schemes To ensure serializability, we can use various concurrency-control schemes All these schemes either delay an operation or abort the transaction that issued the operation The most common ones are locking protocols, timestampordering schemes, validation techniques, and multiversion schemes A locking protocol is a set of rules that state when a transaction may lock and unlock each of the data items in the database The two-phase locking protocol allows a transaction to lock a new data item only if that transaction has not yet unlocked any data item The protocol ensures serializability, but not deadlock freedom In the absence of information concerning the manner in which data items are accessed, the two-phase locking protocol is both necessary and suf cient for ensuring serializability The strict two-phase locking protocol permits release of exclusive locks only at the end of transaction, in order to ensure recoverability and cascadelessness of the resulting schedules The rigorous two-phase locking protocol releases all locks only at the end of the transaction A timestamp-ordering scheme ensures serializability by selecting an ordering in advance between every pair of transactions A unique xed timestamp is associated with each transaction in the system The timestamps of the transactions determine the serializability order Thus, if the timestamp of transaction Ti is smaller than the timestamp of transaction Tj , then the scheme ensures that the produced schedule is equivalent to a serial schedule in which transaction Ti appears before transaction Tj It does so by rolling back a transaction whenever such an order is violated A validation scheme is an appropriate concurrency-control method in cases where a majority of transactions are read-only transactions, and thus the rate of con icts among these transactions is low A unique xed timestamp is associated with each transaction in the system The serializability order is determined by the timestamp of the transaction A transaction in this scheme is never delayed It must, however, pass a validation test to complete If it does not pass the validation test, the system rolls it back to its initial state There are circumstances where it would be advantageous to group several data items, and to treat them as one aggregate data item for purposes of working, resulting in multiple levels of granularity We allow data items of various sizes, and de ne a hierarchy of data items, where the small items are nested within larger ones Such a hierarchy can be represented graphically as a tree.

FDs for 1-M relationships assert an FD in the child-to-parent direction of a 1-M relationship Do not assert an FD for the parent-to-child direction because each LHS value can be associated with at most one RHS value

The McGraw Hill Companies, 2001

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SPEED AND DISTANCE LIMITATIONS STP and UTP cables have different distance limitations UTP cabling has a maximum distance limitation of 100 meters STP cabling has a maximum distance limitation of 500 meters The Token Ring protocol can operate at 4 Mbps and 16 Mbps ring speeds, but all devices on the ring must agree on the speed

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Although functional dependencies derived from statements about uniqueness are easy to identify, functional dependencies derived from statements about 1-M relationships can be confusing to identify When y o u see a statement about a 1-M relationship, the functional de pendency is derived from the child-to-parent direction, not the parent-to-child direction For example, the statement, "A faculty teaches many offerings but an offering is taught by one faculty" defines a functional dependency from a unique column o f offering to a unique col umn o f faculty such as OfferNo > FacNo N o v i c e designers sometimes incorrectly assert that FacNo determines a collection o f OfferNo values This statement is not correct because a functional dependency must allow at most one associated value, not a collection o f values Functional dependencies in which the L H S is not a primary or candidate key can also be difficult to identify These F D s are especially important to identify after converting an E R D to a table design You should carefully look for F D s in w h i c h the L H S is not a candidate key or primary key You should also consider F D s in tables with a combined primary or can didate key in which the LHS is part o f a key, but not the entire key The presentation o f nor mal forms in Section 72 explains that these kinds o f F D s can lead to modification anomalies

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