Uipath Inc.

Magnaview B.v.

Roeland Johannus Scheepens

Dennis Brons

Robin Johannes Pieter Mennens

Martijn Copier

Ruud Andriessen

Roeland Augustinus Maria Vliegen

Bulat Aydarovich Gafurov

Dirk Fahland

Celine Verhoef

Bart Martinus Cornelis De Bie

Systems and methods for automatically assigning labels to one or more types of non-conforming behavior of execution of a process are provided. An aligned process defining non-conforming behavior of execution of a process is received. One or more types of the non-conforming behavior of the execution of the process is identified from the aligned process. Labels identifying the one or more types are assigned to the non-conforming behavior. The labels assigned to the non-conforming behavior are output.

Automatically assigning natural language labels to non-conforming behavior of processes

Systems and methods for object centric process mining are provided. Execution data of a process having a plurality of entities is received. A plurality of object networks representing relationships between objects of the plurality of entities are generated based on the execution data. A set of transitions is determined for each of the plurality of object networks. A process graph of execution of the process is generated based on the sets of transitions. The process graph is output.

Object centric process mining algorithm

Systems and methods for determining conformance of a process based on a process model of the process and an event log of an execution of the process are provided. The process model is divided into one or more control regions and reachable nodes are determined for each node in the process model. Conformance of the process is determined by comparing transitions from source activities to destination activities in the event log with the reachable nodes based on the one or more control regions.

Visual conformance checking of processes

A method is provided. The method is executed by a dependency identification engine implemented as a computer program within a computing environment. The method includes generating a data structure that represents a recorded process by the dependency identification engine from data. The method includes identifying activities of the data structure, determining connectivity between the activities, and determining, based on the connectivity, eligible activity pairs from the activates by the dependency identification engine. The method includes extending, based on the data, the data structure with annotations that represent long-term dependencies between the activities and generating a visualization of the data structure with the annotations by the dependency identification engine.

Extracting long-term dependencies within processes

Some embodiments are directed towards a graph layout device for determining a layout of a graph. The graph being induced by a set of paths through a set of nodes. The nodes of the graph are comprised in the set of nodes. The set of paths is a subset of a global set of paths through the set of nodes. The layout comprises a position of each node of the graph. The graph layout device computes a global rank assignment, filters the global set of paths to obtain the set of paths, and computes the layout of the graph based on the global rank assignment.

Stable graph layout determination

Systems and methods for generating a process tree of a process are provided. An event log of the process is received. It is determined whether a base case applies to the event log and, in response to determining that the base case applies to the event log, one or more nodes are added to the process tree. In response to determining that the base case does not apply to the event log, the event log is split into sub-event logs and one or more nodes are added to the process tree. The steps of determining whether a base case applies and splitting the event log are repeatedly performed for each respective sub-event log using the respective sub-event log as the event log until it is determined that the base case applies to the event log. The process tree is output. The process may be a robotic process automation process.

Process tree discovery using a probabilistic inductive miner

Systems and methods for analyzing an event log for a plurality of instances of execution of a process to identify a bottleneck are provided. An event log for a plurality of instances of execution of a process is received and segments executed during one or more of the plurality of instances of execution are identified from the event log. The segments represent a pair of activities of the process. For each particular segment of the identified segments, a measure of performance is calculated for each of the one or more instances of execution of the particular segment based on the event log, each of the one or more instances of execution of the particular segment is classified based on the calculated measures of performance, and one or more metrics are computed for the particular segment based on the classified one or more instances of execution of the particular segment. The identified segments are compared with each other based on the one or more metrics to identify one of the identified segments that is most likely to have a bottleneck.

Bottleneck detection for processes

A method and system are provided in which predictions are generated, using one or more machine learning-based prediction models, for one or more process parameters associated with a running process. Explanation-oriented data elements are generated that correspond to the generated predictions and include confidence indicators associated with the generated predictions. The explanation-oriented data elements are presented in one or more dashboards of a visualization platform. The explanation-oriented data elements are representative of an explanation framework for explaining the predicted business process parameters generated by a machine learning-based prediction model and in a manner so that a user can understand and trust the basis for the predictions to facilitate effective and appropriate intervention in a running process.

Explainable process prediction

Systems and methods for splitting an event log into sub-event logs are provided. The event log of a process is received. An activity relation score for a parallel relationship operator is calculated for each respective pair of activities of a plurality of pairs of activities in the event log based on 1) a frequency of occurrence of a first activity of the respective pair of activities between occurrences of a second activity of the respective pair of activities and 2) a frequency of occurrence of the second activity between occurrences of the first activity. A cut location in the event log is determined based on the activity relation scores. The event log is split into the sub-event logs based on the cut location.

Systems and methods for generating a process tree of a process are provided. An event log of the process is received. It is determined whether a base case applies to the event log and, in response to determining that the base case applies to the event log, one or more nodes are added to the process tree. In response to determining that the base case does not apply to the event log, the event log is split into sub-event logs based on a frequency of directly follows relations and a frequency of strictly indirectly follows relations for pairs of activities in the event log and one or more nodes are added to the process tree. The steps of determining whether a base case applies and splitting the event log are repeatedly performed for each respective sub-event log using the respective sub-event log as the event log until it is determined that the base case applies to the event log. The process tree is output. The process may be a robotic process automation process.

Systems and methods for creating a unified model of a data transformation graph to facilitate maintenance of the data transformation graph are provided. A user model of a data transformation graph is received and stored for each of a plurality of users. Creation of a unified model of the data transformation graph is facilitated based on the user models. The unified model of the data transformation graph is displayed.

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Eindhoven, Netherlands

Roeland Johannus Scheepens