An E$-$R diagram usually models the state of an enterprise at a point in time. Suppose we wish to track temporal changes, that is$,$ changes to data over time. For example, Zhang may have been a student between September $2015$ and May $2019,$ while Shankar may have had instructor Einstein as advisor from May $2018$ to December $2018,$ and again from June $2019$ to January $2020.$ Similarly, attribute values of an entity or relationship, such as title and credits of course, salary, or even name of instructor, and tot cred of student, can change over time.

One way to model temporal changes is as follows: We define a new data type called valid time, which is a time interval, or a set of time intervals. We then associate a valid time attribute with each entity and relationship, recording the time periods during which the entity or relationship is valid. The end time of an interval can be infinity; for example, if Shankar became a student in September $2018,$ and is still a student, we can represent the end time of the valid time in$-$ terval as infinity for the Shankar entity. Similarly, we model attributes that can change over time as a set of values, each with its own valid time.

a$.$ Draw an E$-$R diagram with the student and instructor entities, and the ad$-$ visor relationship, with the above extensions to track temporal changes.

b$.$ Convert the E$-$R diagram discussed above into a set of relations.

It should be clear that the set of relations generated is rather complex, leading to difficulties in tasks such as writing queries in SQL$.$ An alternative approach, which is used more widely, is to ignore temporal changes when designing the E$-$R model $($in particular, temporal changes to attribute values$),$ and to modify the relations generated from the E$-$R model to track temporal changes.