Blog

By Bruce Silver

(Read Time: 3 Minutes)

By Trisotech

Read Time: 5 Minutes

Often, multiple methods can be used to solve the same business problem. In this blog we will briefly explore two different ways to create a DMN solution to the same decision problem and discuss the pros and cons of each solution.

As part of a regulatory process, a government agency wants to determine if an applicant is eligible for a resident permit.

The rule is simple enough. An applicant is eligible for a resident permit if the applicant has lived at an address while married and in that time period, they have shared the same address at least 7 of the last 10 years.

In terms of inputs to make that decision, we are provided with three lists:

1

A list of periods living at an address for applicant (From, To, Address)

2

A list of periods living at an address for spouse (From, To, Address)

3

A list of applicant and spouse marriage periods (From, To)

The agency suggests that we calculate the time in years, months and days, where the above time periods overlap, then evaluate if this condition has lasted more or equal than 7 of the last 10 years.

The Decision Requirements Diagram (DRD) below captures that method.

Eligibility DRD as stated

In the DRD above, the three provided lists are used as Data Inputs to a Decision that produced a collection of *Periods Overlaps* which are then submitted to a Decision of whether these Periods Overlaps add up to *Seven of the last ten years*.

Defining our Input and Output types

For our inputs, we first define a type *tPeriod* as follows:

The *List of applicant and spouse marriage periods* (From, To) is then defined simply defined as a Collection of *tPeriod*.

As for both the *List of periods living at an address for applicant* (From, To, Address) and the *List of periods living at an address for spouse* (From, To, Address), they are defined as a Collection of *tLivingAdress* that reuses our *tPeriod* type definition above.

Our *Period Overlaps* Decision will then lead to a Collection of *tPeriod* and finally our *Seven of the last ten years* Decision will provide us with a Boolean true or false.

Period Overlaps logic

To obtain the *Periods Overlaps* Decision which calculates the overlap periods where the Applicant was married and living at the same address as Spouse, we will progressively build a Boxed Context. This is done here to help more novice readers. Advanced users may skip to the completed Boxed Context at the end of this section.

Our first step is to find *Common Addresses* between the applicant and the spouse to filter the periods to only those that are on common addresses. By doing so, we eliminate processing any farther periods they were not living together.

To achieve this, we take the *Address* portion from each the *List of periods living at an address for applicant* and filter that list by retaining only those addresses that are also contained in the *Address* portion from each the *List of periods living at an address for spouse*. This provides us with a collection of Common Addresses for both the applicant and spouse. As you can see below, the expression language FEEL from DMN makes this logic quite simple to follow and author. Note that the natural language annotations above the double blue lines make the logic even more obvious to a novice decision modeler or reader.

Using the collection of *Common Addresses*, we will now filter out the Applicant Periods and the Spouse Periods to only be those that were at a common address.

We can now identify the *Cohabitation Periods*. To achieve this, we will iterate over the *Applicant Periods* and the *Spouse Periods* at a common address identified just before and only extract the subperiods the that overlaps. There is no prebuilt FEEL function that extracts overlapping subperiods, we therefore need to create ourselves a Business Knowledge Model (BKM) that will look at two periods and return either an overlap subperiod or null. Here is the logic of that BKM.

We can invoke this BKM from within our logic as per below. Note the *Overlap Interval* function invocation in the *Then* portion below.

The invocation of this new BKM also affects our DRD. As it turns out, we will also need it to decide on the *Seven of the last ten years* decision later. Here is the DRD now augmented with the two invocations.

We can now complete our Boxed Expression for the *Periods Overlaps* decision. Armed with the Collection of *Cohabitation Periods*, we can now look for overlaps with the Marriage periods and return a single Collection of Periods Overlap by flattening the Cohabitation in Marriage Collection while taken care of removing the null entries that our BKM may have introduce when there was no overlap.

Here is below the complete Boxed Expression for the *Periods Overlaps* decision.

Seven of the Last Ten Years Logic

Having obtained a Collection of *Periods Overlaps* for the applicant and Spouse we can now decide if these overlaps add up to *Seven of the last ten years*. This Decision simply returns TRUE if the applicant and spouse were living together and married for at least seven of the last ten years. We will not go into all the details this time as by now you can easily read the boxed context and annotations to guide yourself. We will only bring your attention to the invocation of our BKM Overlap Interval created before in the return portion of the Periods Overlaps in the last 10 years below.

1

Determine the period equating to the last 10 years using today’s date

2

Find the periods from previous *Periods Overlaps* decision that occurred during last 10 years

3

Covert those to the number of days for each period

4

Sum the days and return TRUE if number of days overlapped is greater than or equal to the number of days in 7 years.

In the end, our decision logic turned out somewhat complex when literally following the simple enough suggestions to calculate the time in years, months and days, where the time periods overlap, and then evaluate if this condition has lasted more or equal than 7 of the last 10 years. I wounder if there would be another way to tackle this problem?

Upon further analysis of the problem, another simpler model (Model 2) that produces the same results can be created. This decision model requires only a single decision context and is much easier to understand than the original model even though it does not use the explicit steps described in the problem statement.

For Model 2, rather than using periods as the major organizing principle, this second decision model is driven by iterating through each day in the last ten-year period and verifying each day if all the overlapping requirements are met.

Simplified Model DRD

Seven of the Last Ten Years Logic

Note that we use the same three lists as inputs maintaining their type definitions untouched. Model 2 differ in how we express the logic for the *Seven of the last ten years* decision.

A brief description of this approach goes like this:

1

Determine the interval of days that represents the *Last 10 years* using today’s date

2

For each day during the *Last 10 years*, validate if they were *Married*, and if they were living at the *Same Address*, if yes then count this day

3

Compute the total *Number of days Married and Living Together* by summing all the days that met all criteria from the previous step

4

Validate if the Number of days *Married and Living Together* is greater or equal to 7 years

Day in Period BKM (Function)

You probably noticed that we introduced a Business Knowledge Model (BKM) in Model 2 as well. This function is invoked by *Married, Applicant Address*, and *Spouse Address* in the above Context and returns a Boolean TRUE if the input day is in the input period.

Using this simple problem, we have presented two quite different ways to model the same decision. Each model has pros and cons to be considered.

In the original model (Model 1), we have created a solution that literally follows the description of the problem as provided. This typically shows the problem is clearly understood and leads to a solution that allows the business participants to follow the Decision Requirement Diagram (DRD) of the decision as they understand it. On the positive side, the approach taken in Model 1 is computationally efficient as we first discard all periods where the Applicant and Spouse were not living at the same address prior to doing any other checks. However, the resulting logic may seem quite complex for what seems like a simple problem. Some rather advanced list creation and comparison logic were needed, and the logic can look a little daunting.

In the second model (Model 2), while the DRD is not as literal to the problem statement as was the DRD of Model 1, we obtain a logic that is much simpler to understand and maintain. By checking for conditions at each day, it is now easy to see how changes in regulations introducing new overlapping condition requirements could be introduced into this logic. Which is not so obvious for the logic of Model 1. On the negative side, Model 2 will always loop over every single day whether there is overlap or not, making it less computationally efficient as Model 1.

There you have it, Model 1 leads to a DRD closer to the problem definition with a complex logic definition that is computationally efficient while Model 2 leads to a simpler logic that is easier to understand, maintain and modify while being less computationally efficient. The choice is yours.

There are often other real-word practical considerations to factor in. Perhaps the most important is resource usage when the model is automated and placed in production. Perhaps it is understandability and maintainability. Some models will perform much faster than others depending on the selection and structure of the context logic. Because DMN allows powerful operations with simple syntax, it is not always obvious how a specific model will perform when automated. This should be tested and optimized if high volumes of automated decisions are to be processed.

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