=Paper=
{{Paper
|id=Vol-2420/paperDT8
|storemode=property
|title=Nirdizati 2.0: New Features and Redesigned Backend
|pdfUrl=https://ceur-ws.org/Vol-2420/paperDT8.pdf
|volume=Vol-2420
|authors=Williams Rizzi,Luca Simonetto,Chiara Di Francescomarino,Chiara Ghidini,Tõnis Kasekamp,Fabrizio Maria Maggi
|dblpUrl=https://dblp.org/rec/conf/bpm/RizziSFGKM19
}}
==Nirdizati 2.0: New Features and Redesigned Backend==
https://ceur-ws.org/Vol-2420/paperDT8.pdf
Nirdizati 2.0: New Features and
Redesigned Backend
Williams Rizzi1,2 , Luca Simonetto2 , Chiara Di Francescomarino2 ,
Chiara Ghidini2 , Tõnis Kasekamp3 , and Fabrizio Maria Maggi3
1
Free University of Bozen-Bolzano, Bozen, Italy
2
Fondazione Bruno Kessler, Trento, Italy
{wrizzi, dfmchiara, ghidini}@fbk.eu
3
University of Tartu, Tartu, Estonia
toniskasekamp@gmail.com, f.m.maggi@ut.ee
Abstract. Nirdizati is a dedicated tool for Predictive Process Moni-
toring, a field of Process Mining that aims at predicting how an ongoing
execution of a business process will develop in the future using past
process executions recorded in event logs. Nirdizati is a web applica-
tion supporting users in building, comparing, and analyzing predictive
models that can then be used to perform predictions on the future of
an ongoing case. By providing a rich set of different state-of-the-art ap-
proaches, Nirdizati offers BPM researchers and practitioners a useful
and flexible instrument for investigating and comparing Predictive Pro-
cess Monitoring techniques. In this paper, we present a Nirdizati version
with a redesigned backend, which improves its modularity and scalabil-
ity, and with new features, which further enrich its capability to support
researchers and practitioners to deal with different monitoring tasks.
Keywords: Predictive Process Monitoring · Process Mining · Machine
Learning.
1 Introduction
Nirdizati [10] is an open-source, web-based Predictive Process Monitoring [13]
tool supporting a wide range of state-of-the-art approaches and providing re-
searchers and practitioners with a highly flexible instrument for the construction,
comparison, analysis, and selection of predictive models. The previous version
of Nirdizati supported different feature encodings and a variety of learning
algorithms thus enabling the user to select the best predictive model providing
accurate outcome-based (e.g., whether an ongoing trace will produce a certain
outcome eventually in the future) and numerical (e.g., the remaining time of an
ongoing trace) predictions related to a currently running process execution.
In this demo paper, we introduce a new version of Nirdizati that has
been enhanced with: a completely redesigned backend to allow faster process-
ing and better usage of resources; new algorithms to build the predictive mod-
els; and new encoding techniques to feed the algorithms with a richer set of
2 W. Rizzi et al.
Fig. 1. Nirdizati architecture. The system follows a client-server architecture. The
server exposes APIs to communicate with the client. When the server receives a new
job, it puts it in a Redis Queue. When a new job is available in the Redis Queue, a
worker takes care of the job. Workers, whose number can vary according to the needs,
share access to the Redis Queue, to the cache, and to the database.
features. The backend redesign has introduced a new cache system in the pre-
processing steps, and a database to store the status of the Nirdizati com-
ponents and to track the evolution of the system as a whole. The new fea-
tures include: (i) incremental learning algorithms, which are able to update
the predictive models at runtime by leveraging new cases, as soon as they
complete; (ii) time series prediction algorithms, which enable the prediction of
the sequence of future activities; and (iii) intercase feature encoding, for tak-
ing into account not only the current ongoing case when making predictions
on the current case, but also other concurrent ongoing cases. The current ver-
sion of the tool is online at http://research.nirdizati.org/, while a tuto-
rial and a video are available at https://drive.google.com/drive/folders/
1sG69DabBhmuPsWAOPA4yNLjFbEFVknY0.
2 Redesigned Backend
By design, Nirdizati4 supports the multiprocessing capabilities of nowadays
processors, using a master-slave setting (see Fig. 1). In this setting, the mas-
ter - the server in Fig. 1 - creates and orchestrates the jobs, and the slaves -
the workers in Fig. 1 - take care of performing the jobs. When using such a
setting for comparing different algorithms on the same task, most of the pro-
cessing time is taken for the pre-processing phases that are repeated over and
over for each different configuration. To overcome this time and resource con-
sumption, Nirdizati has now been equipped with a cache system that avoids
pre-processing the data multiple times when evaluating multiple approaches on
a shared task and dataset.
When using caching in the Nirdizati architecture, however, two or more
slaves performing the same pre-processing step concurrently can generate dupli-
cate cache files. A database system able to track in a granular way the status of
4
Source code available at https://github.com/nirdizati-research/
Nirdizati 2.0: New Features and Redesigned Backend 3
each object of the tool, i.e., the set of elements used in a given task, has been
introduced. For instance, the object representing the task of splitting a log into
a training and a validation set is composed of a foreign key to the object rep-
resenting the log, the training and the validation set sizes, the type of ordering
used for the split, the name of the split, and a unique key. Storing each object
of the tool in a table of the database allows, on the one hand, an easy retrieval
of the required information and, on the other hand, keeping in memory only
the database keys instead of the objects themselves, resulting in a lighter foot-
print on the memory. From the database it is possible to retrieve the current jobs
and their status, the available cached information, the previously trained models
and the corresponding accuracy metrics. By relying on a stateful architecture
and capitalizing the work carried out, Nirdizati is able to optimally exploit
multiprocessing.
3 New Features
Two new families of algorithms have been added to the tool, namely, incremental
learning and time series prediction algorithms. Furthermore, a new family of
intercase encodings has been made available in Nirdizati.
While the previous version of Nirdizati mainly focused on predictive mod-
els for outcome-based and time-related predictions, this version of the tool also
supports the creation and comparison of models predicting sequences of next
activities. To this aim, the tool has been equipped with time series prediction
based on two recurrent neural network algorithms: (i) Long Short Term Mem-
ory Networks (LSTM), and (ii) Gated Recurrent Unit (GRU), which have been
proven to give competitive results in Predictive Process Monitoring [16].
Nirdizati has also been enriched with incremental algorithms for carrying
out classification tasks. In particular, (i) Multinomial Naı̈ve Bayes, (ii) Stochastic
Gradient Descent (SGD) Classifier, (iii) Perceptron, and (iv) Neural Networks
have been introduced. We decided to include Multinomial Naı̈ve Bayes, since
it is a fairly common algorithm that is rather simple, yet generally working
pretty well on datasets with different characteristics [14]. SGD Classifier and
Neural Networks showed to be rather effective techniques in different fields,
while Perceptron is a particular case of SGD [19].
Finally, in order to define intercase encodings, the following intercase features
can now be used: (i) executed events per day, (ii) resources used per day, and
(iii) new cases per day. Eliciting and leveraging such features has shown to be
rather effective in improving the accuracy of the trained predictive models [15].
Extensive improvements on the usability of the tool have been carried out:
• Log feature visualization. Once the user has uploaded an event log, the log
feature visualization functionality allows accessing information regarding the
log characteristics (e.g., the number of events and resources per day).
• Dataset splitting, encoding and labeling. These functionalities enable the user
to split an event log into training and validation set by selecting the preferred
case order and sizes of the sets; label the cases, choosing among a predefined
4 W. Rizzi et al.
set of labeling criteria, and check the distribution of labels in the log; and
select the preferred encoding(s) to be used.
• Hyperparameter optimization. Hyperparameter optimization approaches sup-
port users in tuning the hyperparameters to be used for training the pre-
dictive models [3, 4]. Two possible hyperparameter optimization strategies
are available in Nirdizati, i.e., (i) Tree of Parzen Estimators (TPE), and
(ii) Random Search algorithm.
• Result visualization. The results can be filtered and visualized using vari-
ous interactive plotting strategies. The results page contains four elements:
(i) the configuration table, (ii) the results table, (iii) the prefix plot, and
(iv) the bubble chart plots. The configuration table summarizes, for each
predictive model, the configuration that originated the model, while the re-
sults table reports the accuracy of the model (in terms of well-known accu-
racy metrics, e.g., F-score and AUC) in tabular format. The results are also
provided in the prefix plot, which shows the trend of each metric for different
prefix lengths, and in the bubble chart plots, which enable the comparison
of the different predictive models with respect to various perspectives (e.g.,
the comparison of different encodings or classification algorithms).
4 Concluding Remarks
This paper presented the latest advancements of Nirdizati. Specifically, its
backend has been redesigned to provide a reliable and efficient framework that
allows an easy comparison of different state-of-the-art Predictive Process Moni-
toring approaches. Moreover, new features have been added, so that Nirdizati is
now able to run the most recent state-of-the-art techniques, such as incremen-
tal learning algorithms, time series prediction algorithms, and intercase feature
encodings.
The recent stream of publications in the Predictive Process Monitoring field [2,
5, 12, 13, 15, 17] shows the need for tools able to support researchers and users
in analyzing, comparing and selecting the techniques that are the most suit-
able for them. This need is also reflected in the growth of Predictive Process
Monitoring plug-ins in well known process mining tools such as ProM [6] and
Apromore [11, 18]. Nirdizati is a completely dedicated tool for running a very
rich set of Predictive Process Monitoring techniques and its latest advancements
make it even more robust, scalable and usable.
We assess the current Technology Readiness Level of Nirdizati to be 5. This
release offers indeed a well-defined structure of the software and code documen-
tation;5 moreover, it is equipped with a very large test suite, and a Continuous
Integration deployment pipeline. The tool has been extensively used and its
features exercised on both simulated and real data, in the medical [1], admin-
istrative [7] and financial domain [8, 9]. We believe that all these reasons make
Nirdizati a mature and useful instrument for the BPM community.
5
https://nirdizati-research.readthedocs.io/
Nirdizati 2.0: New Features and Redesigned Backend 5
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