=Paper=
{{Paper
|id=Vol-3098/demo_205
|storemode=property
|title=Interactive Process Drift Detection: A Framework for Visual Analysis of Process Drifts (Extended Abstract)
|pdfUrl=https://ceur-ws.org/Vol-3098/demo_205.pdf
|volume=Vol-3098
|authors=Denise Maria Vecino Sato,Rafaela Mantovani Fontana,Jean Paul Barddal,Edson Emilio Scalabrin
}}
==Interactive Process Drift Detection: A Framework for Visual Analysis of Process Drifts (Extended Abstract)==
https://ceur-ws.org/Vol-3098/demo_205.pdf
Interactive Process Drift Detection: A Framework for
Visual Analysis of Process Drifts (Extended Abstract)
Denise Maria Vecino Sato Rafaela Mantovani Fontana Jean Paul Barddal Edson Emilio Scalabrin
Graduate Program in Informatics Department of Professional and Graduate Program in Informatics Graduate Program in Informatics
Pontifícia Universidade Católica Technological Education Pontifícia Universidade Católica Pontifícia Universidade Católica
do Paraná and Instituto Federal Universidade Federal do Paraná do Paraná do Paraná
do Paraná Curitiba, Brazil Curitiba, Brazil Curitiba, Brazil
Curitiba, Brazil 0000-0001-6350-4167 0000-0001-9928-854X 0000-0002-3918-179
0000-0003-1117-7082
Abstract—Interactive Process Drift Detection (IPDD) is a model. However, the most common perspective considered in
framework for visual analysis of process drifts. A process drift the available tools is the control flow. Identifying and
indicates a change in the process model occurred at some point in understanding the process drifts is relevant for business analysts
time. IPDD firstly generates process models for subparts of the because it improves their knowledge about the processes and
event log using a sliding window approach. Then, it detects the enhances the quality of process mining analysis. Even when
drifts by evaluating similarity metrics calculated between adjacent analysts perform offline process mining analysis, process drift
process models; a difference in some of the metrics indicates a detection can provide benefits, e.g., avoid complex discovered
drift. The current implementation of IPDD generates the process process models, improve conformance checking, or enhance
models using the directly-follows graph (DFG) and applies two
processes based on their current state.
metrics: nodes and edges similarity. The user interface shows the
drifts in the process models over time, allowing the user to visually Different tools for detecting process drifts from event logs
understand the model changes. Also, the user can easily change the have been proposed, but the accuracy of the detection is usually
hyperparameters for the analysis and verify the results on the related to the hyperparameter configuration [3]. The ProDrift
interface. The user interface of IPDD allows the user to evaluate plugin in Apromore [4], [5] and the ConceptDrift plugin in ProM
the detected drifts by calculating the F-score metric, which is [2] can detect different types of drifts (sudden and gradual);
useful when using artificial datasets. The underlying idea is to ease however, the focus is the change point and information about it.
the choice of a “good” value for the hyperparameter
The user has to complement the drift analysis by executing a
configuration, which is critical for almost any drift detection tool.
more exploratory mining slicing the event log based on the
Keywords— process drift detection, visual process analysis, reported change points to understand the evolution of the
process drift, concept drift process. A more recent tool, named VDD [6], detects the four
types of drifts and allows the user to explore the drift using the
I. INTRODUCTION process model. However, the tool is based on constraints mined
over Declare models, and it mixes DFGs with the constraints to
Process mining aims at creating valuable knowledge about
explain the dynamic of the process over time. None of the
business processes obtained from information systems event
identified tools calculate an accuracy metric in the user interface.
data. Usually, process mining techniques assume the processes
to be steady-state, i.e., the event data contains information from Tunning the hyperparameter configuration to enhance the
a unique version of the process. However, this assumption does detection accuracy imposes a challenge to the proposed tools
not reflect the reality of the business processes, which constantly because the different approaches are affected by the
adapt to new regulations, improve performance, or enhance user hyperparameter configuration. IPDD aims to overcome this
experience. The situation where a process changes while being issue by providing an interactive user interface where the user
analyzed is named concept drift or process drift [1]. quickly changes the parameter and visually evaluates the results.
The tool provides visual process drift detection analysis by
The change in the process can affect the ongoing instances,
showing the distinct process models over time, in what we can
sudden or gradually. A sudden drift occurs when all the ongoing
consider a “replay” of the process models. IPDD also provides
instances start to follow the new process model immediately. In
information about the differences against the previous model for
a gradual drift, there is a period of time where instances from
each process model, enhancing the analysis. IPDD’s current
both versions of the process model coexist. The process drifts
implementation detects sudden drifts in the control-flow
can also follow recurrent or incremental patterns. A recurrent
perspective offline, which is a limitation.
drift indicates that a replaced process model can occur again. In
an incremental drift, minor changes of the process model are II. IPDD MAIN FEATURES
implemented during some time. Sudden, gradual, incremental,
and recurring are considered process drift types [2]. The process The IPDD framework detects the process drifts by analyzing
drift can also affect one or more perspectives of the process the event log using a sliding window strategy. First, the user
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0
XXX-X-XXXX-XXXX-X/XX/$XX.00
International (CC BY 4.0). ©20XX IEEE
defines the window size based on the number of traces, and
IPDD splits the log using tumbling windows. Then, it generates
a model for each window and calculates the similarity metrics
between adjacent models. The idea is to compare models mined
from adjacent time slots using similarity metrics; when they are
not similar, IPDD identifies a drift and characterizes the change
based on the information provided by the metric.
The IPDD’s current implementation mines the DFGs
(process maps) from the traces in the time slots using the Pm4Py
[7]. Then, the adjacent derived graphs are compared using the
Nodes (NS) and Edges similarity (ES) metrics. NS is calculated
using Eq. 1 [2], where 𝑛𝑝 and 𝑛𝑞 are the number of activities in
the process maps 𝑃 and 𝑄 (derived from adjacent windows)
respectively, and 𝑛𝑐𝑠 indicates the number of common activities
between 𝑃 and 𝑄. ES is calculated using Eq. 2, similar to NS: 𝑒𝑝 Fig.1. Screenshot from the main window.
is the number of edges in 𝑃, 𝑒𝑞 is the number of edges in 𝑄, and
𝑒𝑐𝑠 indicates the number of common edges in both 𝑃 and 𝑄. to check different hyperparameter configurations to overcome
this challenge visually.
𝑁𝑆 = 2 ∗ 𝑛𝑐𝑠 = (𝑛𝑝 + 𝑛𝑞) (1)
The tool was presented to our research group in Curitiba
𝐸𝑆 = 2 ∗ 𝑒𝑐𝑠 = (𝑒𝑝 + 𝑒𝑞) (2) (Brazil), including researchers from three post-graduate
IPDD calculates both metrics, and if one or both is less than programs (Informatics, Production and Systems Engineering,
0, it marks the window as a drift. The F-score metric uses the and Health Technology). Firstly we have conducted a usability
True Positives (TP), False Positives (FP), and FN (False assessment for redesigning the user interface. Currently, we are
Negatives). A TP indicates a window reported as a drift working on a case study on a manufacturing scenario. The idea
containing a trace inputted as a real drift; an FP is counted when is to detect drifts in the temporal perspective of the process
a window reporting a drift does not contain any trace informed (sojourn time). The information about drifts will be used as input
as real drifts, and an FN is incremented when a window that does for planning the maintenance intervals on the production line.
not report a drift contains any traces inputted as actual drifts.
ACKNOWLEDGMENT
Fig. 1 shows the tool’s main screen, allowing users to easily
This study was financed in part by the Coordenação de
change parameters and visually check the results. The parameter
Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)
configuration panel is on top, where users must define the
- Finance Code 001 – Grant No.: 88887.321450/2019-00.
hyperparameter configuration before starting the analysis. After
clicking on “Analyze Process Drifts”, users can follow the REFERENCES
current status in the “Status” area below the parameters panel.
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3
1 https://github.com/denisesato/InteractiveProcessDriftDetectionFW Demonstration video at: https://youtu.be/8feKd6jr8Gs
2 http://visual-pro-drift.com.br:8050/
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0
International (CC BY 4.0).