Hi
I was reading about temporal databases and it seems they have built in time aspects. I wonder why would we need such a model?
How different is it from a normal RDBMS? Can't we have a normal database i.e. RDBMS and say have a trigger which associates a time stamp with each transaction that happens? May be there would be a performance hit. But I'm still skeptical on temporal databases having a strong case in the market.
Does any of the present databases support such a feature?
cheers
My understanding of temporal databases is that are geared towards storing certain types of temporal information. You could simulate that with a standard RDBMS, but by using a database that supports it you have built-in idioms for a lot of concepts and the query language might be optimized for these sort of queries.
To me this is a little like working with a GIS-specific database rather than an RDBMS. While you could shove coordinates in a run-of-the-mill RDBMS, having the appropriate representations (e.g., via grid files) may be faster, and having SQL primitives for things like topology is useful.
There are academic databases and some commercial ones. Timecenter has some links.
A temporal database efficiently stores a time series of data, typically by having some fixed timescale (such as seconds or even milliseconds) and then storing only changes in the measured data. A timestamp in an RDBMS is a discretely stored value for each measurement, which is very inefficient. A temporal database is often used in real-time monitoring applications like SCADA. A well-established system is the PI database from OSISoft (http://www.osisoft.com/).
Apart from reading the Wikipedia article? A database that maintains an "audit log" or similar transaction log will have some properties of being "temporal". If you need answers to questions about who did what to whom and when then you've got a good candidate for a temporal database.
You can imagine a simple temporal database that just logs your GPS location every few seconds. The opportunities for compressing this data is great, a normal database you would need to store a timestamp for every row. If you have a great deal of throughput required, knowing the data is temporal and that updates and deletes to a row will never be required permits the program to drop a lot of the complexity inherit in a typical RDBMS.
Despite this, temporal data is usually just stored in a normal RDBMS. PostgreSQL, for example has some temporal extensions, which makes this a little easier.
As I understand it (and over-simplifying enormously), a temporal database records facts about when the data was valid as well as the the data itself, and permits you to query on the temporal aspects. You end up dealing with 'valid time' and 'transaction time' tables, or 'bitemporal tables' involving both 'valid time' and 'transaction time' aspects. You should consider reading either of these two books:
Another example of where a temporal database is useful is where data changes over time. I spent a few years working for an electricity retailer where we stored meter readings for 30 minute blocks of time. Those meter readings could be revised at any point but we still needed to be able to look back at the history of changes for the readings.
We therefore had the latest reading (our 'current understanding' of the consumption for the 30 minutes) but could look back at our historic understanding of the consumption. When you've got data that can be adjusted in such a way temporal databases work well.
(Having said that, we hand carved it in SQL, but it was a fair while ago. Wouldn't make that decision these days.)
Two reasons come to mind:
Consider your appointment/journal diary - it goes from Jan 1st to Dec 31st. Now we can query the diary for appointments/journal entries on any day. This ordering is called the valid time. However, appointments/entries are not usually inserted in order.
Suppose I would like to know what appointments/entries were in my diary on April 4th. That is, all the records that existed in my diary on April 4th. This is the transaction time.
Given that appointments/entries can be created and deleted etc. A typical record has a beginning and end valid time that covers the period of the entry and a beginning and end transaction time that indicates the period during which the entry appeared in the diary.
This arrangement is necessary when the diary may undergo historical revision. Suppose on April 5th I realise that the appointment I had on Feb 14th actually occurred on February 12th i.e. I discover an error in my diary - I can correct the error so that the valid time picture is corrected, but now, my query of what was in the diary on April 4th would be wrong, UNLESS, the transaction times for appointments/entries are also stored. In that case if I query my diary as of April 4th it will show an appointment existed on February 14th but if I query as of April 6th it would show an appointment on February 12th.
This time travel feature of a temporal database makes it possible to record information about how errors are corrected in a database. This is necessary for a true audit picture of data that records when revisions were made and allows queries relating to how data have been revised over time.
Most business information should be stored in this bitemporal scheme in order to provide a true audit record and to maximise business intelligence - hence the need for support in a relational database. Notice that each data item occupies a (possibly unbounded) square in the two dimensional time model which is why people often use a GIST index to implement bitemporal indexing. The problem here is that a GIST index is really designed for geographic data and the requirements for temporal data are somewhat different.
PostgreSQL 9.0 exclusion constraints should provide new ways of organising temporal data e.g. transaction and valid time PERIODs should not overlap for the same tuple.