Temporal Data is the temporary data that is valid only for a prescribed time. Temporal data becomes invalid or obsolete after a certain period of time.
For example, the current temperature of a particular region is temporal data as it keeps on updating and the validity of this temporal data (current temperature) becomes obsolete. Also, the data for the current seat availability in flight booking keeps on changing every minute. Such type of temporary data is called as temporal data.
Temporal Data is generally used in Real-time systems, for example, Railway Seat Reservation, Sensex, Weather forecasting, etc.
Real-time Systems use temporal consistency constrains on the data objects.
Temporal consistency ensures that the difference in the values which is stored in the database of the real-time systems and the real data is within some predefined limit.
Absolute Validity makes sure that there must be some consistency in the real data and the data stored in the database.
Relative Consistency makes sure that the set of data obtained when queried from the database of the real-time systems should lie within a certain fixed and predefined range. The relative values of the data obtained from the database should be close to each other and there must not be a huge difference in the values.
In the above diagram:
- x arrives at 0 and becomes obsolete at 8, because the validity time is 8
- y arrives at 6 and becomes obsolete at 13, because the validity time is 7
Data items in the Real-time System are stored in the form of a tuple:
(value, avi, timestamp)
- value: It represents the the data value.
- avi: It represents the validity time of the data.
- timestamp: It represents the generation time of the data instance.
For example, (786.34, 8 ms, 800 ms): means that the data value is 786.34 and it is generated at 800 ms and is valid from 800ms to 804ms.
Absolute Validity can be verified using the equation:
( current_time - dtimestamp ) <= davi
Relative Validity can be verified using the equation:
∀d, ∀ d' ∈ R : |dts - d'ts|
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