Nirdizati is a dedicated tool for Predictive Process Monitoring, a eld 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 application 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 di erent state-of-the-art approaches, Nirdizati o ers BPM researchers and practitioners a useful and exible instrument for investigating and comparing Predictive Process Monitoring techniques. In this paper, we present a Nirdizati version with a redesigned backend, which improves its modularity and scalability, and with new features, which further enrich its capability to support researchers and practitioners to deal with di erent monitoring tasks.
Nirdizati [
In this demo paper, we introduce a new version of Nirdizati that has been enhanced with: a completely redesigned backend to allow faster processing and better usage of resources; new algorithms to build the predictive models; and new encoding techniques to feed the algorithms with a richer set of features. The backend redesign has introduced a new cache system in the preprocessing steps, and a database to store the status of the Nirdizati components and to track the evolution of the system as a whole. The new features 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 taking into account not only the current ongoing case when making predictions on the current case, but also other concurrent ongoing cases. The current version of the tool is online at http://research.nirdizati.org/, while a tutorial and a video are available at https://drive.google.com/drive/folders/ 1sG69DabBhmuPsWAOPA4yNLjFbEFVknY0. 2
By design, Nirdizati4 supports the multiprocessing capabilities of nowadays processors, using a master-slave setting (see Fig. 1). In this setting, the master - 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 di erent algorithms on the same task, most of the processing time is taken for the pre-processing phases that are repeated over and over for each di erent con guration. To overcome this time and resource consumption, 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 duplicate cache les. A database system able to track in a granular way the status of
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 representing 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 footprint 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
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
models 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
Memory Networks (LSTM), and (ii) Gated Recurrent Unit (GRU), which have been
proven to give competitive results in Predictive Process Monitoring [
Nirdizati has also been enriched with incremental algorithms for carrying
out classi cation tasks. In particular, (i) Multinomial Nave Bayes, (ii) Stochastic
Gradient Descent (SGD) Classi er, (iii) Perceptron, and (iv) Neural Networks
have been introduced. We decided to include Multinomial Nave Bayes, since
it is a fairly common algorithm that is rather simple, yet generally working
pretty well on datasets with di erent characteristics [
Finally, in order to de ne 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 e ective in improving the accuracy of the trained predictive models [
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 prede ned 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
support users in tuning the hyperparameters to be used for training the
predictive models [
Result visualization. The results can be ltered and visualized using various interactive plotting strategies. The results page contains four elements: (i) the con guration table, (ii) the results table, (iii) the pre x plot, and (iv) the bubble chart plots. The con guration table summarizes, for each predictive model, the con guration that originated the model, while the results table reports the accuracy of the model (in terms of well-known accuracy metrics, e.g., F-score and AUC) in tabular format. The results are also provided in the pre x plot, which shows the trend of each metric for di erent pre x lengths, and in the bubble chart plots, which enable the comparison of the di erent predictive models with respect to various perspectives (e.g., the comparison of di erent encodings or classi cation algorithms). 4
This paper presented the latest advancements of Nirdizati. Speci cally, its backend has been redesigned to provide a reliable and e cient framework that allows an easy comparison of di erent state-of-the-art Predictive Process Monitoring 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 incremental learning algorithms, time series prediction algorithms, and intercase feature encodings.
The recent stream of publications in the Predictive Process Monitoring eld [
We assess the current Technology Readiness Level of Nirdizati to be 5. This
release o ers indeed a well-de ned structure of the software and code
documentation;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 [