Improving business process intelligence by observing object state transitions

Herzberg, Nico; Meyer, Andreas; Weske, Mathias

doi:10.1016/j.datak.2015.07.008

Cited by 15 publications

(6 citation statements)

References 29 publications

(49 reference statements)

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“…In [26], instance-spanning constraints are discovered from event logs, which regulate the start of the process instances. In [27], object-state transitions are proposed to improve business process intelligence. Although all these approaches are useful for conformance checking, they do not result in a comprehensive process model.…”

Section: Other Approachesmentioning

confidence: 99%

OC-PM: analyzing object-centric event logs and process models

Berti

Aalst

2022

Int J Softw Tools Technol Transfer

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Object-centric process mining is a novel branch of process mining that aims to analyze event data from mainstream information systems (such as SAP) more naturally, without being forced to form mutually exclusive groups of events with the specification of a case notion. The development of object-centric process mining is related to exploiting object-centric event logs, which includes exploring and filtering the behavior contained in the logs and constructing process models which can encode the behavior of different classes of objects and their interactions (which can be discovered from object-centric event logs). This paper aims to provide a broad look at the exploration and processing of object-centric event logs to discover information related to the lifecycle of the different objects composing the event log. Also, comprehensive tool support (OC-PM) implementing the proposed techniques is described in the paper.

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Section: Other Approachesmentioning

confidence: 99%

OC-PM: analyzing object-centric event logs and process models

Berti

Aalst

2022

Int J Softw Tools Technol Transfer

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“…Moreover, an approach for monitoring processes and prediction their progress with data state transition events is presented in [8]. In [9], progress in activity-centric processes is improved by utilising object state transition as described in [8].…”

Section: Related Workmentioning

confidence: 99%

A One-Dimensional Kalman Filter for Real-Time Progress Prediction in Object Lifecycle Processes

Arnold¹,

Breitmayer²,

Reichert³

2021

2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW)

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Real-time monitoring of business processes offers promising perspectives to discover problems and optimisation potentials. Early detection is a key part in this endeavour. One crucial aspect of real-time monitoring is to determine the current progress of a running business process. This is particularly challenging for business processes that consist of a multitude of loosely coupled, smaller processes that interact with each other, like object lifecycle processes in data-centric approaches to business process management. In this paper, an approach to predict the remaining portion of the process path to be still executed in relation to the overall process is proposed. This prediction is based on a one-dimensional Kalman Filter. As a major benefit of this approach, real-time progress determination can start directly with the first run of the process, i.e., without need for comprehensive event log data. This becomes possible due to the procedure applied by the Kalman Filter, which requires no log data. A quantitative study with 250 progress estimations for large object lifecycle processes results in a deviation of the average estimated progress from the real progress, calculated after the completion of the process, of about 5%. This emphasises that reasonable progress predictions are possible even in the absence of an event log, as it is the case when deploying new or changed processes to the run-time system.

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“…Examples include tool support for the design of spatial databases and management of context models (Cipriani et al, 2011) or Provop, an approach for configuring context-based process variants based on static context information (Hallerbach et al, 2008). Herzberg et al (2015) present an approach to use events of object state transitions to observe process progress but do not consider external context.…”

Section: The Notion Of Context and Context-aware Information Systemsmentioning

confidence: 99%

Enhancing business process execution with a context engine

Janiesch

Kuhlenkamp

2018

BPMJ

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Purpose Changes in workflow relevant data of business processes at run-time can hinder their completion or impact their profitability as they have been instantiated under different circumstances. The purpose of this paper is to propose a context engine to enhance a business process management (BPM) system’s context-awareness. The generic architecture provides the flexibility to configure processes during initialization as well as to adapt running instances at decision gates or during execution due to significant context change. Design/methodology/approach The paper discusses context-awareness as the conceptual background. The technological capabilities of business rules and complex event processing (CEP) are outlined in an architecture design. A reference process is proposed and discussed in an exemplary application. Findings The results provide an improvement over the current situation of static variable instantiation of business processes with local information. The proposed architecture extends the well-known combination of business rules and BPM systems with a context engine based on CEP. Research limitations/implications The resulting architecture for a BPM system using a context engine is generic in nature and, hence, requires to be contextualized for situated implementations. Implementation success is dependent on the availability of context information and process compensation options. Practical implications Practitioners receive advice on a reference architecture and technology choices for implementing systems, which can provide and monitor context information for business processes as well as intervene and adapt the execution. Originality/value Currently, there is no multi-purpose non-proprietary context engine based on CEP or any other technology available for BPM, which facilitates the adaptation of processes at run-time due to changes in context variables. This paper will stimulate a debate between research and practice on suitable design and technology.

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Improving business process intelligence by observing object state transitions

Cited by 15 publications

References 29 publications

OC-PM: analyzing object-centric event logs and process models

OC-PM: analyzing object-centric event logs and process models

A One-Dimensional Kalman Filter for Real-Time Progress Prediction in Object Lifecycle Processes

Enhancing business process execution with a context engine

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