Process Mining must embrace complex adaptive systems

On April this year I wrote a post about one of the challenges that Process Mining is not addressing properly: Big data. In the last process mining task force meeting, resulted that one the biggest concerns was about the effort of making XES a “de facto” standard in order event logs used in process mining could be system interoperable.

Despite the fact is important to assure that data can be exchanged across multiple systems and they can be interpreted the same manner (contrary what happens with bpmn diagrams), I tend to think that process mining is loosing ground regarding the near future reality:

  • Technology will be like electricity or if you prefer a commodity;
  • Cybernetics principles will emerge (finally) because society and technology is backing humans able to observe, sense, act, learn and adapt. Once organizations are made from humans, and despite humans belong to some kind of organizational unit, they are not anymore animals on a tree branch and are part of social networks that constitute the enterprise where everything happens.
  • Technology no longer shapes the way human perform. People do it. Change and adaptation encompasses clearly to think and find new business models. To think about innovation. If companies do not have humans capable to find access, understand and process information, how can companies evolve and survive?

Again what is the challenge of process mining?

Organizations live in a world where interdependence, self-organization and emergence are factors for agility, adaptability and flexibility plunged into networks. Software-based information systems go into a service oriented architecture direction and the same goes to Infrastructures where services are become structures available in networks. inspired into empirical studies of networked systems such as Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us structurally understand or predict the behavior of these systems. Those findings are characterized by been supported on the “complex networks” concepts [1].

Last week I stumbled into a great find based on a conversation about creating an Adaptive Enterprise Architecture framework to support business processes  that Process Sphere is sponsoring, in order to apply in real world implementations. Universidade do Minho is leading research in the field of understanding existing information systems can be characterized adopting the complex adaptive systems principles.

The approach is quite obvious (and at the same time so simple). Is about a black box (supplied by Palo Alto networks) connected to the enterprise network (the pipes where information flow, where today everything flows) and literally sucks all the information from the pipe to be analyzed and interpreted.  With this approach its possible to understand Who interacts an with Whom interacts, What information is accessed and processed, What and When, interaction occurs, What are the information services requested and (in my point of view) particularly important evaluating how information systems can cope with social iteration and can keep the pace of adaptability inside of organizations (something I’m keen regarding other project about requirements engineering using process mining).

The innovation of the approach is based into:

  • Analysis is carried based on complex network principles;
  • Contrary of one of process mining principles, that the staring point is to select data to make the analysis in order to prevent “data collection paranoia”, this project collects and analyses ALL the available data and digs into the networks that were formed (each network have a different analysis dimension depending on the field of analysis). Layered.

On one hand the method approach to interpret data it’s arguable among the scientific community that the one here presented is inferior of others used by other researchers that develop process mining methods available in tools (researchers tend to think they are above God), thus I’m not going to involve in that kind of discussion (by the way this one comes from physics), on the other hand, what I like most is the design approach. Putting a box so suck all the data to be explored (into the company veins) what could be called inline real time process mining to deal with complexity and adaptability.

References: [1] Modeling Organizational Information System Architecture Using “Complex Networks” Concepts, José L.R. Sousa, Ricardo J. Machado, J.F.F. Mendes, 2012 Eighth International Conference on the Quality of Information and Communications Technology


4 thoughts on “Process Mining must embrace complex adaptive systems

  1. Alberto, great post. As you know I have been writing about the same thing for years:

    I see one problem. As long as process management and thus process mining experts and researchers consider a process to be a flow it will lead nowhere and they can mine all the data and all the networks until the next Millenium and go nowhere.

    Big data is a silly idea that assumes that there is information hidden in lots of data. That is not true because it is already created by people who put the information there. If you do not collect data for processes then the data won’t be there. So to simply suck data raw from some systems is not going to work.

    Otherwise I am in agreement: We need to give people an infrastructure in which they can freely work, interact and collaborate towards well defined goals and while doing this the content of what they do with whom will tell the story, not some odd interaction traces.

    The main issue is that the network analysis must allow simple creation and use of ontologies and semantics as part of its operation. I have not seen yet that people grasp the concept behind it.

    We are no longer talking about flows we are ONLY talking about complex adaptive networks. The trick will be to take it from a scientific concept to a system that is as usable as an iPhone app.

  2. When I was at EDS we used a similar system. It works beautifully but takes some time to interpret quickly due to the volume of data. The brilliance of the machine is that it allows the analyst to avoid capturing poor requirements, ie people will always tell the analyst what they ‘should’ be doing and not what they ‘actually’ do. The analytics from data go towards finding out ‘hidden’ processes in the business.

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