From Low-Level Events to Activities - A Pattern-Based Approach

Mannhardt, Felix; Leoni, Massimiliano de; Reijers, HA Hajo; Aalst, Wil M. P. van der; Toussaint, Pieter J.

doi:10.1007/978-3-319-45348-4_8

Cited by 63 publications

(52 citation statements)

References 16 publications

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“…In [16] a supervised event abstraction method was presented. This method takes an event log at a lower level of abstraction and transforms it to an event log at the desired level of abstraction, using behavioral activity patterns: sequence, choice, parallel, interleaving and repetition.…”

Section: Literature Reviewmentioning

confidence: 41%

Discovering High-Level Process Models from Event Logs

Begicheva¹,

Lomazova²

2017

Model. anal. inf. sist.

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Abstract. Process mining is a relatively new field of computer science, which deals with process discovery and analysis based on event logs. In this paper we consider the problem of discovering a high-level business process model from a low-level event log, i.e. automatic synthesis of process models based on the information stored in event logs of information systems. Events in a high-level model are abstract events, which can be refined to low-level subprocesses, whose behavior is recorded in event logs. Models synthesis is intensively studied in the frame of process mining research, but only models and event logs of the same granularity are mainly considered in the literature. Here we present an algorithm for discovering high-level acyclic process models from event logs and some specified partition of low-level events into subsets associated with abstract events in a high-level model.

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Section: Literature Reviewmentioning

confidence: 41%

Discovering High-Level Process Models from Event Logs

Begicheva¹,

Lomazova²

2017

Model. anal. inf. sist.

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“…The approach has been then extended in [44] by including natural-language processing techniques to compare the labels of events and activities in the model. Similarly, Mannhardt et al [45] rely on behavioral activity patterns that capture domain knowledge and tie events to activities. Then, they align logged events to process activities.…”

Section: Process Mining Uses Case Identification As a Basismentioning

confidence: 99%

From event streams to process models and back: Challenges and opportunities

Soffer

Hinze

Koschmider

et al. 2019

Information Systems

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The domains of complex event processing (CEP) and business process management (BPM) have different origins but for many aspects draw on similar concepts. While specific combinations of BPM and CEP have attracted research attention, resulting in solutions to specific problems, we attempt to take a broad view at the opportunities and challenges involved. We first illustrate these by a detailed example from the logistics domain. We then propose a mapping of this area into four quadrants -two quadrants drawing from CEP to create or extend process models and two quadrants starting $ This paper is an outcome of discussions and collaborations that were initiated at the Dagstuhl seminar 16341 on "Integrating Process-Oriented and Event-Based Systems" from a process model to address how it can guide CEP. Existing literature is reviewed and specific challenges and opportunities are indicated for each of these quadrants. Based on this mapping, we identify challenges and opportunities that recur across quadrants and can be considered as the core issues of this combination. We suggest that addressing these issues in a generic manner would form a sound basis for future applications and advance this area significantly.

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“…Our semantic-aware log format is indeed the target of a semantic lifting procedure [35,40] which is outside the scope of this paper. Recently, machine learning procedures have been adopted to automatically learn a mapping linking aggregated events with activities at the business process level [41,42].…”

Section: From Sensor Events To Semantics-aware Log Entriesmentioning

confidence: 99%

Dynamic Access Control to Semantics-Aware Streamed Process Logs

et al. 2019

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Business process logs are composed of event records generated, collected and analyzed at different locations, asynchronously and under the responsibility of different authorities. Their analysis is often delegated to auditors who have a mandate for monitoring processes and computing metrics but do not always have the rights to access the individual events used to compute them. A major challenge of this scenario is reconciling the requirements of privacy and access control with the need to continuously monitor and assess the business process. In this paper, we present a model, a language and a software toolkit for controlling access to process data where logs are made available as streams of RDF triples referring to some company-specific business ontology. Our approach is based on the novel idea of dynamic enforcement: we incrementally build dynamic filters for each process instance, based on the applicable access control policy and on the current prefix of the event stream. The implementation and performance validation of our solution is also presented.

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From Low-Level Events to Activities - A Pattern-Based Approach

Cited by 63 publications

References 16 publications

Discovering High-Level Process Models from Event Logs

Discovering High-Level Process Models from Event Logs

From event streams to process models and back: Challenges and opportunities

Dynamic Access Control to Semantics-Aware Streamed Process Logs

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