12 research outputs found
Preventing Object-centric Discovery of Unsound Process Models for Object Interactions with Loops in Collaborative Systems: Extended Version
Object-centric process discovery (OCPD) constitutes a paradigm shift in
process mining. Instead of assuming a single case notion present in the event
log, OCPD can handle events without a single case notion, but that are instead
related to a collection of objects each having a certain type. The object types
constitute multiple, interacting case notions. The output of OCPD is an
object-centric Petri net, i.e. a Petri net with object-typed places, that
represents the parallel execution of multiple execution flows corresponding to
object types. Similar to classical process discovery, where we aim for
behaviorally sound process models as a result, in OCPD, we aim for soundness of
the resulting object-centric Petri nets. However, the existing OCPD approach
can result in violations of soundness. As we will show, one violation arises
for multiple interacting object types with loops that arise in collaborative
systems. This paper proposes an extended OCPD approach and proves that it does
not suffer from this violation of soundness of the resulting object-centric
Petri nets. We also show how we prevent the OCPD approach from introducing
spurious interactions in the discovered object-centric Petri net. The proposed
framework is prototypically implemented
Interactive Process Identification and Selection from SAP ERP
SAP ERP is one of the most popular information systems supporting various
organizational processes, e.g., O2C and P2P. However, the amount of processes
and data contained in SAP ERP is enormous. Thus, the identification of the
processes that are contained in a specific SAP instance, and the creation of a
list of related tables is a significant challenge. Eventually, one needs to
extract an event log for process mining purposes from SAP ERP. This demo paper
shows the tool Interactive SAP Explorer that tackles the process identification
and selection problem by encoding the relational structure of SAP ERP in a
labeled property graph. Our approach allows asking complex process-related
queries along with advanced representations of the relational structure
Performance-preserving event log sampling for predictive monitoring
Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, most of the state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. Moreover, most of these methods require a hyper-parameter optimization that requires several repetitions of the training process which is not feasible in many real-life applications. In this paper, we propose an instance selection procedure that allows sampling training process instances for prediction models. We show that our instance selection procedure allows for a significant increase of training speed for next activity and remaining time prediction methods while maintaining reliable levels of prediction accuracy
Event Log Sampling for Predictive Monitoring
Predictive process monitoring is a subfield of process mining that aims to
estimate case or event features for running process instances. Such predictions
are of significant interest to the process stakeholders. However,
state-of-the-art methods for predictive monitoring require the training of
complex machine learning models, which is often inefficient. This paper
proposes an instance selection procedure that allows sampling training process
instances for prediction models. We show that our sampling method allows for a
significant increase of training speed for next activity prediction methods
while maintaining reliable levels of prediction accuracy.Comment: 7 pages, 1 figure, 4 tables, 34 reference
Prediction-based resource allocation using LSTM and minimum cost and maximum flow algorithm
Predictive business process monitoring aims at providing the predictions about running instances by analyzing logs of completed cases of a business process. Recently, a lot of research focuses on increasing productivity and efficiency in a business process by forecasting potential problems during its executions. However, most of the studies lack suggesting concrete actions to improve the process. They leave it up to the subjective judgment of a user. In this paper, we propose a novel method to connect the results from predictive business process monitoring to actual business process improvements. More in detail, we optimize the resource allocation in a non-clairvoyant online environment, where we have limited information required for scheduling, by exploiting the predictions. The proposed method integrates offline prediction model construction that predicts the processing time and the next activity of an ongoing instance using LSTM with online resource allocation that is extended from the minimum cost and maximum flow algorithm. To validate the proposed method, we performed experiments using an artificial event log and a real-life event log from a global financial organization.1
Quality-Aware Resource Model Discovery
Context-aware process mining aims at extending a contemporary approach with process contexts for realistic process modeling. Regarding this discipline, there have been several attempts to combine process discovery and predictive process modeling and context information, e.g., time and cost. The focus of this paper is to develop a new method for deriving a quality-aware resource model. It first generates a resource-oriented transition system and identifies the quality-based superior and inferior cases. The quality-aware resource model is constructed by integrating these two results, and we also propose a model simplification method based on statistical analyses for better resource model visualization. This paper includes tooling support for our method, and one of the case studies on a semiconductor manufacturing process is presented to validate the usefulness of the proposed approach. We expect our work is practically applicable to a range of fields, including manufacturing and healthcare systems
Analyzing Process-Aware Information System Updates Using Digital Twins of Organizations
Digital transformation often entails small-scale changes to information
systems supporting the execution of business processes. These changes may
increase the operational frictions in process execution, which decreases the
process performance. The contributions in the literature providing support to
the tracking and impact analysis of small-scale changes are limited in scope
and functionality. In this paper, we use the recently developed Digital Twins
of Organizations (DTOs) to assess the impact of (process-aware) information
systems updates. More in detail, we model the updates using the configuration
of DTOs and quantitatively assess different types of impacts of information
system updates (structural, operational, and performance-related). We
implemented a prototype of the proposed approach. Moreover, we discuss a case
study involving a standard ERP procure-to-pay business process
Sodium–Glucose Cotransporter-2 Inhibitors Could Help Delay Renal Impairment in Patients with Type 2 Diabetes: A Real-World Clinical Setting
This study compared the renoprotective effects of sodium–glucose cotransporter-2 (SGLT2) inhibitors and dipeptidyl peptidase-4 (DPP-4) inhibitors in patients with type 2 diabetes mellitus (T2DM). We performed a retrospective cohort study using electronic medical records of patients with T2DM. The primary outcome was the first occurrence of an estimated glomerular filtration rate (eGFR) <45 mL/min/1.73 m2 after the index date. We analyzed changes in repeatedly measured laboratory data, such as eGFR and serum uric acid (SUA). We included 2396 patients (1198 patients in each group) in the present study. The rate of renal events was significantly lower in the SGLT2 inhibitors group than that in the DPP-4 inhibitors group (hazard ratio, 0.46; 95% CI, 0.29 to 0.72; p = 0.0007). The annual mean change in the eGFR was significantly smaller in the SGLT2 inhibitors group than that in the DPP-4 inhibitors group, with a between-group difference of 0.86 ± 0.18 mL/min/1.73 m2 per year (95% CI, 0.49 to 1.23; p < 0.0001). Moreover, the mean change in SUA was lower in the SGLT2 inhibitors group. Considering the lower incidence of renal impairment, the slower decline in eGFR, and reduced SUA, SGLT2 inhibitors could help delay renal impairment in patients with T2DM