Try it. And catch it, finally.

While still in the middle of taking care of my momentarily sick little girls, I will today continue the reflections about the lifecycle of data and go through a sample of temporal data to further illustrate what I am speaking about. Bitemporality: this e.g. is how Martin Fowler (Patterns for things that change with time) describes the two time dimensions of Bitemporal Objects:

I think of the first dimension as actual time: the time something happened. The second dimension is record time, the time we knew about it. Whenever something happens, there are always these two times that come with it.

Foto credits to bobydimitrov according Creative Commons BY-SA 2.0

“Whenever something happens, there are always these two times that come with it.”: the information that something happened itself needs time until it reaches its recipients and maybe even more time until it is recorded in some database… and this of course does not just concern a lightning and the subsequent reception of the thunder within some miles, it does not just concern the recording of the lightnings in some weather forecasters database or the reader of a local newspaper covering this thunderstorm. It concerns every single profane fact of life.

Tracking Record Time

Let’s therefore focus on such a profane fact of life and investigate the relationship between a hotel room and it’s price. Let’s imagine a simplified Price Watcher Application tracking the Prices of the rooms of several hotels in a given city:

A better normalised real life database schema for such an application would of course look a bit different, but this should not bother us at the moment: with this simple table we can already answer the question “Where do I get the cheapest city hotel room at the moment?” sorting the table by price in descending order… but actually… can we answer this? To be slightly more precise – and we have to be precise for our topic – we can just answer the question “Where do we get to our current knowledge the cheapest city hotel room at the moment?”. After all, we don’t reflect reality here, we just reflect what we know about it…

If our Price Watcher App eventually becomes a relevant factor in the market, conflicts with hotel owners could occur more frequently (”Why do you update just my price cuttings so slowly?”). We therefore might want to add the information when exactly the specific price was recorded with us:

But thinking about this twice, wouldn’t it be wise not just to remember when we updated the price the last time, but to remember many such updates in order to be able to discuss single cases and to proove that we honestly treat all hotels equally? Well, then we may not update the table anymore but instead insert a new row whenever a price change occurs. Let’s for this purpose focus on “Bedtime Lodge” from here on:

Now, let’s back-pedal at this point a little bit by “denormalising” the information we already have here: we add another column, which serves to reflect until which point in time we considered the associated information to be valid: it is the moment when the price for one hotel changed and we therefore added a new row:

We realize: the new “Effective until” information is “unlimited” for new rows. And it is equal to the “Recorded At” Column of the new Row, the moment we “update” it by inserting a new row. Now: whenever we ask for current prices, we will want to filter out all the rows with outdated effectivity. But actually we should generalize this filtering procedure a bit: whenever we ask this table for a consistent set of data given at the current or a historic date, we always filter out rows for which this date is not in between “Recorded at” and “Effective until”. In order to make this even easier, we could decide to replace the “unlimited” with a date far enough in the future in order not to cause any Y2K problematic during our lifetime:

And by the way, watching this: wouldn’t it be nice to show to our users not just the current price, but also things like e.g. how the minimum room price for a given hotel developed in the past or which hotels frequently offer rooms for minimum prices? All the information we need for this already is in place! We would just have to think about the queries a little bit.

Tracking Actual Time

Now, question: what’s bitemporal about this data. Answer: so far, nothing. So far, we just cared about Record Time, meaning we just cared about the moment we learned about a price change, we did not care so much about when it actually happened. If we call this type of storing data “Record Temporal”, we can imagine that we can use a very similar mechanism to store “Actual Temporal” Data by changing the semantics of our two Time Columns and call them now Actual From and Actual Until:

What changes when giving the columns the semantics of Actual Time compared to Record Time? Well, when it comes to Record Time, it’s always an easily retrievable System Time we deal with: the moment some data is inserted/updated, we change the “Effective until” of the old row and the “Recorded at” of the new row to the current System Time and are finished. When it comes to Actual Time, things are different. Typically this point in time can just be provided by the human (or external system) using the database application. He/she/it is the only one who can tell: wait, now we update the information in your database, right, but actually the change in the real world already happened earlier. Which is why we have to ask ourselves, whether this kind of semantics would add value to our Price Watcher App? Probably not. But: the moment we change perspective on the given sample we find that somebody else will be interested in both Times: the “Bedtime Lodge”.

Bitemporality

The “Bedtime Lodge” will also have to maintain a room and price database with similar contents but just for their own rooms. The goal is now not to “watch” prices, but actually to “define” and “maintain” them. Of course, we don’t need the “Hotel” column anymore, because we definitely know, it’s us:

The requirement we face here is to be able to define and maintain Prices which come into effect somewhen in the future. My next post will therefore combine the Concepts of Record Time and Actual Time and complete the sample showing the full Bitemporality.