The Emergence of Rule Management Systems for End Users

Long envisioned in research and industry, they have silently emerged: rule management systems for end users to manage their personal (business) processes. End users use these systems to define rules that automate parts of their daily behavior to have more time for their life. I will explain in this blog entry new trends and challenges as well as business opportunities.

What is it about?

Our life is full of decisions. Let it be complex unique decisions, but also simple reoccurring ones, such as switching off the energy at home when we leave it or sending a message to your family that you left work. Another more recent example is taking a picture and uploading it automatically to Facebook and Twitter. Making and executing simple decisions take our time, which could be used for something more important. Hence, we wish to automate them as much as possible.

The idea is not new. More technical interested people have used computers or home automation systems since several years or even decades. However, they required a lot of time to learn them, complex to configure / required software engineering skills, were proprietary and it was very uncertain if the manufacturer will still support them in a few years.

This has changed. Technology is part of everybody’s life, especially since the smartphone and cloud boom. We use apps for our smartphone or as part of the cloud that help us automating our life and I will present in the following two apps in this area. These apps can be used by anyone and not only software engineers.

What products exist?


The first app that I am going to present is a cloud-based app called ifttt (if this then that), which has been created by a startup company. You can use a graphical tool to describe rules for integrating various cloud services, such as Facebook, Dropbox, Foursquare, Google Calendar, Stocks or Weather. These cloud services are called channels in ifttt.

A rule has the simple format “if this then that”. The “this” part refers to triggers that start actions specified by the “that” part. The ifttt application polls the channels every 15 minutes if they have any new data and evaluates if new data triggers actions specified in the “that” part.

Defining rules is an easy task, but a user does not necessarily have to do this. A huge community has already created a lot of so-called recipes, which are predefined rules that can be just used by any other user without any effort. Examples for these recipes are:


On(X) is a mobile and cloud application created by Microsoft Research. It leverages sensor information of your mobile for evaluating rules and triggering actions. Contrary to the aforementioned app it is thus not limited to data from other cloud platforms.

Rules are described using a JavaScript language (find some examples here). This means end users cannot use a graphical tool and have to have some scripting language knowledge. Luckily, the platform offers also some receipts:

We see that the receipts are more sophisticated, because they can leverage sensor information of the mobile (location/geofencing or movement).

What is in for business?

Using these personal rule engines for business purposes is an unexplored topic. I expect that they could lead to more efficiency and new business models. For example, consider the following business model:

Furthermore, we can envision improved service quality by using personal rule engines in various domains

  • Medicine: if user is in the kitchen then remind to take medicine, if it has not been taken yet for the day
  • Energy saving: If I leave my house then shut down all energy consumers except the heater.
  • Food delivery: If I am within 10 km range of my destination then start delivering the pizza I have ordered
  • Car sharing: If I leave work send a SMS to all my colleagues I share my car with
  • Team collaboration: We can evaluate if team members or members of different teams want to do the same actions or waiting for similar triggers. They can be brought together based on their defined rules to improve or split their work more efficiently.

Future research

The aforementioned applications are prototypes. They need to be enhanced and business models need to be defined for them. First of all, we need to be clear about what we want to achieve with automating simple decisions, e.g.

    • Cost savings
    • Categorizing for quicker finding and publishing information
    • Socializing

An important research direction is how we could mine the rules, e.g. for offering advertisement or bringing people together. Most of the mining algorithms to day focus on mining unstructured or unrelated data, but how can we mine rules of different users and make sense out of them?

Another technical problem is the time between rule evaluation and execution. For instance, ifttt only polls every 15 minutes its data sources to check if actions in a rule should be triggered. This can be too late in time critical situations or can lead to confusing actions.

From a business point of view, it would be interesting to investigate the integration of personal rule management into Enterprise Resource Planning (ERP) systems as well as to provide a social platform to optimize and share rules.

Finally, I think it is important to think about rules involving or affecting several persons. For example, let us assume that a user “A” defined the rule “when I leave work then inform my car sharing colleagues”. One of the car sharing colleagues has the rule “when a user informs me about car sharing then inform all others that I do need a seat in a car anymore”. If user “A” now cannot bring the car sharing colleague home then he or she has a problem.

A more simpler example would be if user “A” defines a rule “If user ‘B’ sends me a message then send him a message back” and user ‘B’ defines a rule “If user ‘A’ sends me a message then send him a message back”. This would lead to an indefinite message exchange between the users.

Here we need to be able to identify and handle conflicts.

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