Process mining methods analyze an organization's processes by using process execution data. During the handling of a process instance data about the execution of activities is recorded. Process mining uses such data to gain insights about the real execution of processes. In this thesis, we address research challenges in which a multi-perspective view on processes is needed and that look beyond the control- ow perspective, which de nes the sequence of activities of a process. We consider problems in which multiple interacting process perspectives | in particular control- ow, data, resources, time, and functions | are considered together. The contributed methods span several types of process mining: two are concerned with conformance checking, two are process discovery techniques, and one is a decision mining method. All methods have been implemented, evaluated, and applied in the context of four case studies.
The e cient and e ective handling of its processes is essential for the success
of an organization. This thesis [
Often, process mining methods solely use the activity names and the timestamps of events recorded in execution traces. Other aspects of the process execution are then overlooked. This thesis contributes process mining techniques that make use of additional data to analyze a process from multiple perspectives. Typical examples for additional data that is considered in this thesis are identi ers of resources that execute an activity (e.g., humans, machines), input data used to execute an activity (e.g., patient age, loan amount), output data generated data time control-flow resources function rse u N rtco o D iiltsa cp e S
Triage Color
C = white Register
C ≠ white
Every hour
Check Decide
Referral
Visit Diagnostic Medical examination
Same nurse
Prepare
Organize
Ambulance R = Tertiary
R ≠ Home
Observe R ≠ Home
Transfer R = Home
Discharge
by activity executions (e.g., decisions, outcomes), and information on the
relation between multiple events (e.g., activity lifecycles). Process models are also
not restricted to express only the control- ow of a process. Real-life activities
rarely are atomic constructs. Often, there is a hierarchy of activities: multiple
activities executed together form an activity on a higher level of abstraction.
Decision rules based on data associated to the process instance and contextual
information can be included (e.g., only certain patients require an ambulance).
The ve basic perspectives depicted in Figure 1 | the control- ow perspective,
the resource perspective, the data perspective, the time perspective, and the
function perspective | are often considered in the literature on BPM, process
modeling, and process mining [
Our main research goal was to develop discovery, enhancement, and conformance checking methods that consider the interaction of multiple perspectives on the process. We aimed to advance the use of multi-perspective information for all three types of process mining instead of focusing on one speci c type. Moreover, we targeted problems in which multiple perspectives on a process are viewed together, e.g., data objects that in uence the routing of activities, routing that in uences the possible resources, routing that depends on time constraints (e.g., fast vs. normal procedure). Starting from the premise that e cient, e ective and usable tools are essential to facilitate the adoption of research results, we aimed for the development of tools that can deal with realistic event logs in an e cient and e ective manner. Finally, we aimed to show the practical applicability of methods in real-world scenarios.
We categorize our ve main contributions along the three main types of process mining: conformance, enhancement, and discovery.
Conformance. We contribute two methods for multi-perspective conformance checking, i. e., the diagnosis and quanti cation of discrepancies between the real execution as recorded by information systems and the desired execution as speci ed by process models.
{ A method that computes an optimal, multi-perspective, balanced alignment.
The alignment relates the behavior modeled in a multi-perspective process
model with the behavior observed in an event log and enables to determine
a tness score between model and log. We denoted the method as balanced,
since it balances deviations on the di erent process perspectives and provides
an optimal explanation for the observed behavior in terms of an execution
trace of the multi-perspective process model. Deviations that occur on the
control- ow perspective may be explained by wrongly recorded data values
and vice versa. The technique enables to specify statements such as
"Skipping activity Check is more severe than executing activity Check too late"
and "Executing activity Decide by a di erent doctor than activity Visit is
less severe than sending patients with the triage color Red to their home".
The method has been published in [
Discovery. We contribute two multi-perspective process discovery and one
enhancement method. The proposed methods leverage the additional information
recorded in data attributes (also denoted as event payload) of the event log or use
domain knowledge on all process perspectives to discover better process models
and enhance existing models. Our methods discover integrated models in which
multiple perspectives on the process are intertwined with the control- ow.
{ A method for data-aware heuristic process discovery that aims to reveal
infrequent conditional behavior by using recorded data attributes. Data- and
control- ow are learned together. The proposed method employs classi
cation techniques to discover conditional dependencies based on the attribute
values recorded in the event log. It adds infrequent behavior to the
process model such as, e.g., characterized by the following statements "In a few
cases patients are assigned a white triage color and leave the hospital" and
"Sometimes as a speci c nurse reverses the order of the Diagnostics and Visit
activity". The method has been published in [
Enhancement. Regarding the enhancement category of process mining, we
contribute a method to discover potentially overlapping decision rules in process
models based on an event log. Existing techniques only return rules that assume
completely deterministic decisions. We observed that this assumption often does
not hold due to missing data relevant for the actual decision making is
unavailable or non-deterministic business rules. The method builds upon standard
classi cation techniques and makes an e ort to introduce overlap by reclassifying
instances that were previously misclassi ed. The method balances precision and
tness of a process model with regard to an event log. When rules are
overlapping two or more possible routing options can be chosen non-deterministically.
As result, the process model is less precise but ts the observations better.
Implementation and Applications. Next to their implementation in the open
source framework ProM, we integrated the functionality of the proposed methods
in two interactive tools: the Multi-perspective Process Explorer and the
Interactive Data-aware Heuristic Miner. Both tools reached a high level of maturity
and were published in the BPM demo track [