Welcome to the PADS Blog!

This post is by Kefang Ding, Scientific Assistant in the Process And Data Science group at RWTH Aachen University. Contact her via email for further inquiries.

ProM is a scientific open-source platform for process mining techniques, which is popular among researchers. Many algorithms in process mining are implemented as ProM plugins. However, users interact with those plugins separately. It makes it difficult and time-consuming to conduct analyses which require multiple plugins or tests which require repeated execution of the same sequence of plugins.

Workflow management systems are software tools designed to create, perform and monitor a defined sequence of tasks. Among the current workflow management systems, KNIME is a free and open-source data analytics platform, which is implemented in Java but also allows for wrappers to run Java, Python, Perl and other frameworks.

In order to overcome the drawbacks of ProM on workflow management, as well as to enable the use of process mining techniques in KNIME, we have developed a project called PM4KNIME. PM4KNIME integrates the ProcessMining tools from ProM into KNIME platform by wrapping ProM plugins as nodes in KNIME.

To conduct tasks in Process Mining with PM4KNIME, nodes are connected to compose a workflow. Then the workflow can be executed multiple times by one click. For example, to complete the task on checking the performance, fitness and precision of an event log and a Petri net, the workflow in KNIME is shown below.

With the same tasks, compared to ProM, PM4KNIME allows an easy configuration with less interaction, easy reuse and sharing with the workflow.

The current PM4KNIME extensions implement the nodes for frequent-used process mining techniques. The PM4KNIME taxonomy is listed below.

Click on the link (https://github.com/pm4knime/pm4knime-document/wiki) to learn more about PM4KNIME! If you are interested in PM4KNIME, please contact us with kefang.ding@pads.rwth-aachen.de

This post is by Marco Pegoraro, Scientific Assistant in the Process And Data Science group at RWTH Aachen University. Contact him via email for further inquiries.

In previous posts on this blog, we talked about uncertainty in process mining, both in the context of conformance checking and process discovery. In an uncertain process trace, the attributes of an event are not recorded as a precise value but as a range or a set of alternatives.

If we consider the case of timestamps represented as ranges, the total order usually present among events in a trace is lost; time relationships exist in a partial order instead, where the order between events that have overlapping timestamps is undefined. The directed acyclic graph that graphically represents this partial order is called a behavior graph.

A straightforward way to obtain a behavior graph is to check all pairs of events, connecting the ones that have a well-defined time relationship, then perform a transitive reduction to remove unnecessary edges. This runs in a cubic time with respect to the number of events. In the paper “Efficient Construction of Behavior Graphs for Uncertain Event Data” (Pegoraro, Uysal, van der Aalst) we present a method to build behavior graphs in quadratic time, leading to a more efficient analysis of uncertain data in process mining.

This post is by Prof. Dr. Wil van der Aalst, Chairholder in the Process And Data Science group at RWTH Aachen University. Contact him via email for further inquiries.

Conformance checking aims to uncover differences between a process model and an event log. Initially, process mining focused on discovering process models from event data, but in recent years, the use and importance of conformance checking increased. Many conformance checking techniques and measures have been proposed. Typically, these take into account the frequencies of traces in the event log, but do not consider the probabilities of these traces in the model. This asymmetry leads to various complications. A novel way to approach this, is to assume probabilities and subsequently use the Earth Movers’ Distance (EMD) between stochastic languages representing models and event logs.

The Earth Movers’ Distance (EMD) provides and simple and intuitive conformance notion. The typical problems related to precision vanish immediately! Moreover, the approach is extensible to other perspectives (including time and resources) and can also be applied to concept drift detection and comparative process mining. This blog post summarizes part of my presentation given on 19-11-2019 in the weekly PADS Seminar Series (slides are attached at the bottom of the page).

What is the problem?

To explain the problem, let us consider the following process model and five event logs (L₁ – L₅).

L1	= [〈a,b,d,e〉490,〈a,d,b,e〉490,〈a,c,d,e〉10,〈a,d,c,e〉10]
L2	= [〈a,b,d,e〉245,〈a,d,b,e〉245,〈a,c,d,e〉5,〈a,d,c,e〉5,〈a,b,e〉500]
L3	= [〈a,b,d,e〉489,〈a,d,b,e〉489,〈a,c,d,e〉10,〈a,d,c,e〉10,〈a,b,e〉2]
L4	= [〈a,b,d,e〉500,〈a,d,b,e〉500]
L5	= [〈a,c,d,e〉500,〈a,d,c,e〉500]

Each trace in L₁ matches a trace of the model and vice versa. Hence, all existing recall and precision measures tend to give a high score (i.e., good conformance). Half of the traces in L₂ do not fit the model (〈a,b,e〉is impossible according to the model, but occurs 500 times). Hence, all existing recall measures will report a low recall score for L₂. However, these measures will report a high score for recall when L₃ is considered. The reason is that in L₃, 99.8% of the traces are fitting (〈a,b,e〉occurs only twice). Existing recall measures tend to give high scores when L₄ and L₅ are considered since the model can reproduce all traces observed. However, both L₄ and L₅ are only covering two of the four traces allowed by the process model. Hence, existing precision measures tend to give a lower score for L₄ and L₅. Moreover, due to symmetry, there is no reason to consider L₄ and L₅ to be different from a precision point of view.

The above analysis of existing recall measures shows that frequencies matter. L₂ and L₃ have the same sets of traces, but 50% of the traces of L₂ are fitting and 99.8% of the traces of L₃ are fitting. Hence, most recall measures will consider L₃ to conform much better than L₂. The logical counterpart of frequencies in event logs are routing probabilities in process models. However, almost all existing measures ignore such routing probabilities. This leads to an asymmetry. 

Therefore, we argue that also probabilities matter.

Probabilities matter!

We start by adding probabilities to the process model introduced before.

The numbers attached to transitions can be interpreted as weights. The probability of trace〈a,d,b,e〉is 0.5×0.98 = 0.49, the probability of trace〈a,d,c,e〉is 0.5 ×0.02 = 0.01, etc. Hence, the model describes a so-called stochastic language:

M	= [〈a,b,d,e〉0.49,〈a,d,b,e〉0.49,〈a,c,d,e〉0.01,〈a,d,c,e〉0.01]

Similarly, we can convert trace frequencies into probabilities:

L1	= [〈a,b,d,e〉0.49,〈a,d,b,e〉0.49,〈a,c,d,e〉0.01,〈a,d,c,e〉0.01]
L2	= [〈a,b,d,e〉0.245,〈a,d,b,e〉0.245,〈a,c,d,e〉0.005,〈a,d,c,e〉0.005,〈a,b,e〉0.5]
L3	= [〈a,b,d,e〉0.489,〈a,d,b,e〉0.489,〈a,c,d,e〉0.01,〈a,d,c,e〉0.01,〈a,b,e〉0.002]
L4	= [〈a,b,d,e〉0.5,〈a,d,b,e〉0.5]
L5	= [〈a,c,d,e〉0.5,〈a,d,c,e〉0.5]

By converting event logs and process models to stochastic languages, conformance is reduced to the problem of comparing stochastic languages.

Consider model M and the five event logs L₁, L₂, L₃, L₄, and L₅. Obviously, L₃ is closer to M than L₂. Similarly, L₄ is closer to M than L₅. We propose to use the so-called Earth Mover’s Distance (EMD) to compare stochastic languages. If the probabilities of traces are considered as piles of sand, then EMD is the minimum cost of moving the sand from one distribution to another. EMD requires a distance notion. For our Earth Movers’ Stochastic Conformance notion, we provided several distance notions, e.g., the normalized edit distance between two traces.

Earth Movers’ Stochastic Conformance

If we assume the normalized edit distance between traces, then the EMD distance is a number between 0 (identical, i.e., fully conforming) and 1 (worst possible conformance). For our model M and logs L₁,L₂, …, L₅ we find the following distances: 0 for L₁, 0.125 for L₂, 0.0005 for L₃, 0.005 for L₄, and 0.245 for L₅. Note that distance is the inverse of similarity, i.e., for model M and logs L₁,L₂, …, L₅ we find the following Earth Movers’ Stochastic Conformance similarity measures: 1 for L₁, 0.875 for L₂, 0.9995 for L₃, 0.995 for L₄, and 0.755 for L₅. Hence, given M, L₁ has the best conformance, L₃ is much better than L₂, and L₄ is much better than L₅. This matches our intuition, e.g., L₅ does not have any executions of b although, according to the model, b should be executed for 98% of cases. Note that there is just one conformance measure and not two separate measures for recall and precision. This makes sense considering that increasing the probability of one trace should coincide with lowering the probabilities of other traces.

Just the starting point!

The approach is very promising and has been implemented in ProM. This was mostly done by Sander Leemans from QUT. Next, to these conformance measures, we also defined various types of diagnostics to identify conformance problems in both log and model. In addition, challenges related to infinite loops, duplicate activities, and silent activities have been addressed. Recently, also Tobias Brockhoff joined the team and is focusing on using the EMD notion to concept drift, i.e., detecting when and how process change. Moreover, he also extended the above techniques with time. This allows us to see how performance is changing. These techniques are being applied in the Internet of Production (IoP) Cluster of Excellence at RWTH. The work of Tobias can be used to find and diagnose performance problems and uncover changes in routing and delays in production systems.

Learn More?

Check out the slides of the PADS Seminar Series.

1. S.J.J. Leemans, A.F. Syring, and W.M.P. van der Aalst. Earth Movers’ Stochastic Conformance Checking. In T.T. Hildebrandt, B.F. van Dongen, M. Röglinger, and J. Mendling, editors, Business Process Management Forum (BPM Forum 2019), volume 360 of Lecture Notes in Business Information Processing, pages 127-143. Springer-Verlag, Berlin, 2019.
2. W.M.P. van der Aalst, A. Adriansyah, and B. van Dongen. Replaying History on Process Models for Conformance Checking and Performance Analysis. WIREs Data Mining and Knowledge Discovery, 2(2):182-192, 2012.

This post is by Alessandro Berti, Software Engineer in the Process And Data Science group at RWTH Aachen University. Contact him via email for further inquiries.

Process mining is a branch of data science that includes a wide set of different techniques for automated process discovery, compliance/conformance checking, process simulation and prediction. The tools support for process mining has developed in the recent years in different directions: standalone tools, libraries for the most known programming languages, cloud solutions that store and analyze event data. All of these require the support of different standards, numeric calculus and optimization techniques, visualizations. Examples are the XES standard (XML) for the storage of event logs, the support of Petri nets and their importing/exporting from PNML (XML) files, the inclusion of LP/ILP solvers for process discovery (ILP-based process discovery) and conformance checking (A* alignments).

The inclusion of a vast set of functionalities in the more advanced tools/libraries (like ProM 6.x or PM4Py) has therefore led to an uncontrollable growth in the amount of memory required to run the tool/library. As per now, simply opening ProM 6.9 requires more than 650 MB of RAM, and importing PM4Py along with its dependencies requires more than 80 MB of RAM. On the other hand, cloud solutions such as the Celonis IBC or MyInvenio require an active Internet connection for the transmission of event data and the delivery of process mining analysis/dashboards.

This cuts the possibility to apply process mining on the most numerous class of computers in the world: the microcontrollers. As example, a modern car contains from 30 to 70 microcontrollers. Microcontrollers are usually adopted to control the operational status of a system (for example, the acceleration, temperature, magnetic field, humidity). For minimizing idle/light sleep power consumption, these come with a very limited amount of RAM (usually from 32 KB to 512 KB) and a very low power CPU (examples are the Cortex M0/M4/M7 CPUs for microcontrollers). Applying existing process mining tools/libraries on these is simply impossible.

Hence, a new library (MicroPM4Py) is being developed in the MicroPython language (www.micropython.org), that is a complete reimplementation for microcontrollers of a basic set of features of Python 3.4. The aim of the MicroPython language is not being faster than the normal Python (indeed, Python is much faster than MicroPython for the same script) but to minimize the memory footprint of the application. The goal of MicroPM4Py is to enable some process mining features directly at the microcontroller level:

• Full support for Petri nets without invisible transitions: a memory-efficient data structure is deployed that supports the semantics of a Petri net, the verification of the fitness of a trace, PNML importing/exporting.
• Basic support (A1 import; A1 export) for the XES standard for event logs: the traces can be loaded from the XES event log with different modalities (full DFG; distinct variants; full list of traces in memory; iterator trace by trace).
• Basic support for importing/exporting logs in the CSV format.
• Basic support for process discovery (discovery of a DFG, DFG mining, Alpha Miner algorithm) on top of XES/CSV event logs
• Generation of the DOT (Graphviz) visualization of a DFG / Petri net.

All the data structures have been optimized in order to minimize the memory consumption. As example, the following table estimates the RAM occupation (in bytes) of the three log structures in MicroPM4Py reading the DFG from the XES; reading the variants; reading all the traces from the log. Generally, the numbers are competitive and among the most efficient XES importers ever done (remind the limitation: only the case ID and the activity is read).

Log name	DFG obj size	Variants obj size	Loaded log obj size
running-example	3200	1568	2016
receipt	17112	22072	213352
roadtraffic	10696	37984	19392280
LevelA1.xes	6456	16368	154792
BPIC17.xes	29240	7528144	11474448
BPIC15_4.xes	453552	488200	587656
BPIC17 – Offer log.xes	3768	2992	6051880
BPIC13_incidents.xes	2696	380216	1333200
BPIC15_3.xes	600304	600312	743656
BPIC15_1.xes	541824	534216	652696
BPIC15_5.xes	555776	589688	703712
BPIC12.xes	20664	1819848	3352352
BPIC13_closed_problems.xes	2320	33504	226856
BPIC15_2.xes	551656	457496	531176
BPIC13_open_problems.xes	1872	16096	122672

The following table estimates the memory usage of an use case of MicroPM4Py: from a log, the DFG is obtained and the DFG mining technique is applied to obtain a Petri net. Then, an iterator is created on the log, in order to iterate over the single traces of the log. The memory usage of the MicroPM4Py module and data structure is then measured. The following aspects are taken into account (estimations were done on a X86-64 Debian 9 with Miniconda Python 3.7):

• The maximum size of the iterator+current trace (in bytes)
• The size of the DFG-mined Petri net (in bytes)
• The size of the MicroPM4Py Python module (in bytes)
• An estimated overapproximated size (16 KB) of a kernel + Micropython interpeter running on a microcontroller (in bytes)

The values are summed to get an estimation of the memory usage of the application for such use case, for several real-life logs. Except for the BPIC 2015 logs, the memory allocation is always under 128 KB! 🙂

Log name	Max XES iterable size	DFG mining net size	MicroPM4Py module size	MicroPython size (est.max.)	MAX EST. SIZE
running-example	2512	6384	20624	16384	45904
receipt	6312	27328	20624	16384	70648
roadtraffic	3416	18104	20624	16384	58528
LevelA1.xes	3656	9952	20624	16384	50616
BPIC17.xes	6664	41352	20624	16384	85024
BPIC15_4.xes	49560	757680	20624	16384	844248
BPIC17 – Offer log.xes	2392	6760	20624	16384	46160
BPIC13_incidents.xes	2696	5352	20624	16384	45056
BPIC15_3.xes	49912	1084872	20624	16384	1171792
BPIC15_1.xes	51592	993800	20624	16384	1082400
BPIC15_5.xes	54048	1027648	20624	16384	1118704
BPIC12.xes	6368	30744	20624	16384	74120
BPIC13_closed_problems.xes	2056	5160	20624	16384	44224
BPIC15_2.xes	54080	1012512	20624	16384	1103600
BPIC13_open_problems.xes	1864	4480	20624	16384	43352

While it is impossible on such level (microcontrollers) to support the wide set of features of other tools, it is still possible to apply some process mining algorithms on top of microcontrollers. MicroPM4Py can also be deployed on old workstations or other kinds of low-power computers (such as the Raspberry Pis).

As future work, the library will include some other process models (e.g. transition systems, NFA, continuous time markov chains).

This post is by Marco Pegoraro, Scientific Assistant in the Process And Data Science group at RWTH Aachen University. Contact him via email for further inquiries.

When applying process mining in real-life settings, the need to address anomalies in data recording when performing analyses is omnipresent. A number of such anomalies can be modeled by using the notion of uncertainty: uncertain event logs contain, alongside the event data, some attributes that describe a certain level of uncertainty affecting the data.

Uncertainty can be addressed by filtering out the affected events when it appears sporadically throughout an event log. Conversely, in situations where uncertainty affects a significant fraction of an event log, filtering away uncertain events can lead to information loss such that analysis becomes very difficult. In this circumstance, it is important to deploy process mining techniques that allow to mine information also from the uncertain part of the process.

In the paper “Discovering Process Models from Uncertain Event Data” (Marco Pegoraro, Merih Seran Uysal, Wil M.P. van der Aalst) we present a methodology to obtain Uncertain Directly-Follows Graphs (UDFGs), models based on directed graphs that synthesize information about the uncertainty contained in the process. We then show how to convert UDFGs in models with execution semantics via filtering on uncertainty information and inductive mining.

This post is by Junxiong Gao, Scientific Assistant in the Process And Data Science group at RWTH Aachen University. Contact him via email for further inquiries.

Robotic Process Automation (RPA) recently gained a lot of attention, both in industry and academia. RPA embodies a collection of tools and techniques that allow business owners to automate repetitive manual tasks. The intrinsic value of RPA is beyond dispute, e.g., automation reduces errors and costs and thus allows us to increase overall business process performance.

However, adoption of current-generation RPA tools requires a manual effort w.r.t. identification, elicitation and programming of the to-be-automated tasks. At the same time, several techniques exist that allow us to track the exact behavior of users, in great detail.

Therefore, in this line of research, we present a novel end-to-end structure design that allows for completely automated, algorithmic automation-rule deduction, on the basis of captured user behavior.

This post is by Marco Pegoraro, Scientific Assistant in the Process And Data Science group at RWTH Aachen University. Contact him via email for further inquiries.

Let’s consider the analysis of a specific class of event logs: the logs that contain uncertain event data. Uncertain events are recordings of executions of specific activities in a process which are enclosed with an indication of uncertainty in the event attributes. Specifically, let’s consider the case where the attributes of an event are not recorded as a precise value but as a range or a set of alternatives. In the case of the common control-flow attributes of an event log, the case id and activity are represented by a set of alternatives, and the timestamp as a range.

We can think of this uncertain trace as a set of “classic” traces obtained by every possible choices of the values for the attributes; this is called the realization set.

In the paper “Mining Uncertain Event Data in Process Mining“, published on the first International Conference on Process Mining (ICPM), we laid down a taxonomy of possible kinds of uncertainty; we also explore an example of application, i.e. the computation of upper and lower bound for conformance cost of an uncertain trace against a reference process model. This is obtained by modeling a Petri net able to reproduce the behavior of an uncertain trace; this enables to compute the conformance exploiting a model-to-model comparison.

This post is by Majid Rafiei, Scientific Assistant in the Process And Data Science group at RWTH Aachen University. Contact him via email for further inquiries.

To gain novel and valuable insights into the actual processes executed within a company, process mining provides a variety of powerful data-driven analyses techniques ranging from automatically discovering process models to detecting and predicting bottlenecks, and process deviations.

On the one hand, recent breakthroughs in process mining resulted in powerful techniques, encouraging organizations and business owners to improve their processes through process mining. On the other hand, there are great concerns about the use of highly sensitive event data. Within an organization, it often suffices that analysts only see the aggregated process mining results without being able to inspect individual cases, events, and persons. When analysis is outsourced also the results need to be encrypted to avoid confidentiality problems.

Surprisingly, little research has been done toward security methods and encryption techniques for process mining. The PADS team presented a novel approach that allows us to hide confidential information in a controlled manner while ensuring that the desired process mining results can still be obtained. In the following you can find a simplified version of our proposed framework.

The paper which introduces this framework, and the connector method to preserve the sequence of activities securely has been selected as the best paper by SIMPDA 2018 (http://ceur-ws.org/Vol-2270/paper1.pdf). In this paper, we provide a sample solution for process discovery based on the above-mentioned framework.

This post is by Lisa Mannel, Scientific Assistant in the Process And Data Science group at RWTH Aachen University. Contact her via email for further inquiries.

Our paper „Finding Complex Process-Structures by Exploiting the Token-game“ has been accepted for publication by the 40th International Conference on Applications and Theory of Petri Nets and Concurrency.

In our paper we present a novel approach to process discovery. In process discovery, the goal is to find, for a given event log, the model describing the underlying process. While process models can be represented in a variety of ways, in our paper we focus on the representation by Petri nets. Using an approach inspired by language-based regions, we start with a Petri net without any places, and then insert the maximal set of places considered fitting with respect to the behavior described by the log. Traversing and evaluating the whole set of all possible places is not feasible, since their number is exponential in the number of activities. Therefore, we propose a strategy to drastically prune this search space to a small number of candidates, while still ensuring that all fitting places are found. This allows us to derive complex model structures that other discovery algorithms fail to discover. In contrast to traditional region-based approaches this new technique can handle infrequent behavior and therefore also noisy real-life event data. The drastic decrease of computation time achieved by our pruning strategy, as well as our noise handling capability, is demonstrated and evaluated by performing various experiments.

You can download the full text here.

This post is by Mohammadreza Fani Sani, Scientific Assistant in the Process And Data Science group at RWTH Aachen University. Contact him via email for further inquiries.

The main aim of process mining is to increase the overall knowledge of business processes. This is mainly achieved by 1) process discovery, i.e. discovering a descriptive model of the underlying process, 2) conformance checking, i.e. checking whether the execution of the process conforms to a reference model and 3) enhancement, i.e. the overall improvement of the view of the process, typically by enhancing a process model. In each case the event data, stored during the execution of the process, is explicitly used to derive the corresponding results.

Many process mining algorithms assume that event data is stored correctly and completely describes the behavior of a process. However, real event data typically contains noisy and infrequent behaviour. The presence of outlier behaviour makes many process mining algorithms, in particular, process discovery algorithms, result in complex, incomprehensible and even inaccurate results. Therefore, to reduce these negative effects, in process mining projects, often a preprocessing step is applied that aims to remove outlier behaviour and keep good behaviour. Such preprocessing phase increases the quality and comprehensiveness of possible future analyses. Usually this step is done manually, which is costly and time-consuming and also needs business/domain knowledge of the data.

In this paper, we focus on improving process discovery results by applying an automated event data filtering, i.e., filtering the event log prior to apply any process discovery algorithm, without significant human interaction. Advantaging from sequential rules and patterns, long distance and indirect flow relation will be considered. As a consequence, the proposed filtering method is able to detect outlier behaviour even in event data with lots of concurrencies, and long-term dependency behaviour.  The presence of this type of patterns is shown to be hampering the applicability of previous automated general purpose filtering techniques.

By using the ProM based extension of RapidMiner, i.e., RapidProM, we study the effectiveness of our approach, using synthetic and real event data. The results of our experiments show that our approach adequately identifies and removes outlier behaviour, and, as a consequence increases the overall quality of process discovery results. Additionally, we show that our proposed filtering method detects outlier behaviour better compared to existing event log filtering techniques for event data with heavy parallel and long-term dependency.