Aligning Partially-Ordered Process-Execution Traces and Models Using Automated Planning

Leoni, Massimiliano de; Lanciano, Giacomo; Marrella, Andrea

doi:10.1609/icaps.v28i1.13911

Cited by 14 publications

(10 citation statements)

References 6 publications

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“…In [69], the authors adopt a partial order notion for the observed event data. Using this representation, the authors propose to use automated planning algorithms [70] and provide an algorithmic framework in the standardized Planning Domain Definition Language (PDDL) language.…”

Section: Certain Semanticsmentioning

confidence: 99%

Partial-order-based process mining: a survey and outlook

Leemans

Zelst

2022

Knowl Inf Syst

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The field of process mining focuses on distilling knowledge of the (historical) execution of a process based on the operational event data generated and stored during its execution. Most existing process mining techniques assume that the event data describe activity executions as degenerate time intervals, i.e., intervals of the form [t, t], yielding a strict total order on the observed activity instances. However, for various practical use cases, e.g., the logging of activity executions with a nonzero duration and uncertainty on the correctness of the recorded timestamps of the activity executions, assuming a partial order on the observed activity instances is more appropriate. Using partial orders to represent process executions, i.e., based on recorded event data, allows for new classes of process mining algorithms, i.e., aware of parallelism and robust to uncertainty. Yet, interestingly, only a limited number of studies consider using intermediate data abstractions that explicitly assume a partial order over a collection of observed activity instances. Considering recent developments in process mining, e.g., the prevalence of high-quality event data and techniques for event data abstraction, the need for algorithms designed to handle partially ordered event data is expected to grow in the upcoming years. Therefore, this paper presents a survey of process mining techniques that explicitly use partial orders to represent recorded process behavior. We performed a keyword search, followed by a snowball sampling strategy, yielding 68 relevant articles in the field. We observe a recent uptake in works covering partial-order-based process mining, e.g., due to the current trend of process mining based on uncertain event data. Furthermore, we outline promising novel research directions for the use of partial orders in the context of process mining algorithms.

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Section: Certain Semanticsmentioning

confidence: 99%

Partial-order-based process mining: a survey and outlook

Leemans

Zelst

2022

Knowl Inf Syst

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“…For tackling the segmentation issue, we rely on three main steps: (i) a frequent-pattern identification technique [11] to automatically derive the routine segments from a UI log (i.e., routine segments describe the different behaviours of the routine(s) under analysis, in terms of repeated patterns of performed user actions), (ii) a human-in-the-loop interaction to filter out those segments not allowed (i.e., wrongly discovered from the UI log) by any real-world routine execution by means of declarative constraints [13], and (iii) a routine traces detection component that leverages trace alignment in process mining [12] to cluster all user actions belonging to a specific routine segment into well-bounded routine traces (i.e., a routine trace represents an execution instance of a routine within a UI log). Such traces are finally stored in a dedicated routine-based log, which captures exactly all the user actions happened during many different executions of the routine.…”

Section: Smartrpa: From Ui Logs To Sw Robotsmentioning

confidence: 99%

Mastering Robotic Process Automation with Process Mining

Agostinelli

Marrella

Abb

et al. 2022

Lecture Notes in Computer Science

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Robotic Process Automation (RPA) is an emerging automation technology that creates software (SW) robots to partially or fully automate rule-based and repetitive tasks (aka routines) previously performed by human users in their applications' user interfaces (UIs). Successful usage of RPA requires strong support by skilled human experts, from the detection of the routines to be automated to the development of the executable scripts required to enact SW robots. In this paper, we discuss how process mining can be leveraged to minimize the manual and time-consuming steps required for the creation of SW robots, enabling new levels of automation and support for RPA. We first present a reference data model that can be used for a standardized specification of UI logs recording the interactions between workers and SW applications to enable interoperability among different tools. Then, we introduce a pipeline of processing steps that enable us to (1) semi-automatically discover the anatomy of a routine directly from the UI logs, and (2) automatically develop executable scripts for performing SW robots at run-time. We show how this pipeline can be effectively enacted by researchers/practitioners through the SmartRPA tool.

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“…For the experiments with synthetic data, we used the PLG2 [Burattin, 2015] tool to generate the event logs conforming to their respective model. We also generate four process models, with increasing sizes, according to the method used in [Leoni et al, 2018]. By size, we are talking about visible and invisible transitions.…”

Section: Synthetic Event Log and Process Modelmentioning

confidence: 99%

Using automatic planning to find the most probable alignment: A history-based approach

Almeida

Delgado

Peres

et al. 2022

Anais Do XIX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2022)

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In many organizational contexts, the existence of a normative process model makes it possible to verify if the actual execution of activities of a business process conforms to that model. Non-conforming behavior can be detected by aligning the actions recorded in the event log with a related normative process model. The alignment approach uses a cost-function to build an execution path that shows which actions do not conform to the model, and which are the expected activities for that trace. In this paper, we are interested on finding the optimal probable alignment using a history-base cost-function, i.e. a function based on the process execution history. For that, we use as a base implementation the planning-based approach, previously proposed in the literature, which demonstrates to find large processes faster when compared to A* for standard cost-functions. We incorporate in this tool the automatic generation of an history-base cost-function to find an optimal probable alignment. In addition, we evaluated our approach using data from both synthetic event logs and a real-life event log.

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Aligning Partially-Ordered Process-Execution Traces and Models Using Automated Planning

Cited by 14 publications

References 6 publications

Partial-order-based process mining: a survey and outlook

Partial-order-based process mining: a survey and outlook

Mastering Robotic Process Automation with Process Mining

Using automatic planning to find the most probable alignment: A history-based approach

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