162,379 research outputs found
Call for Papers, Issue 5/2021 Process Mining at the Enterprise Level
No full textMendling, J (reprint author), Vienna Univ Econ & Business, Vienna, Austria.
[email protected]
A new notational framework for declarative process modeling
No full textIn order to capture flexible scenarios, a declarative approach to business process modeling describes constraints that limit a process' behavior instead of specifying all its allowed enactments. However, current graphical notations for declarative processes are tough to understand, thus hampering a widespread usage of the approach. To overcome this issue, we present a novel notational framework for representing declarative processes, devised in compliance with well-known notation design principles
Evaluation of Algorithmic Contributions to Business Process Management
No full textAlgorithms play an important role for business process management. Indeed, all of the 27 papers accepted for BPM 2023 refer to algorithms in one way or the other. Some of these research papers propose new algorithms, such as the paper on the Agent Miner [8], they compose analytical pipelines, such as the paper on process mining video data [5], or they discuss the evaluation of algorithms, such as the paper on sustainability dimensions [3]. So far, however, the BPM community has not developed explicit guidelines for evaluating algorithmic contributions.
This tutorial describes an overarching methodological framework of how algorithms can be researched. In turn, we discuss its foundations in three dimensions. First, we describe four ontological entities including real-world problem, algorithmic task, algorithm design, and algorithm implementations. Second, we distinguish knowledge contributions along two dimensions, that is a) knowledge relating to tasks and to designs and b) knowledge of and knowledge about. Third, we discuss how methodologically new or better knowledge is established. The objective of this tutorial is to present specific strategies how algorithms can be systematically researched for early career researchers. These are illustrated by good examples of BPM papers.The research of the author was supported by Einstei, Foundation Berlin under grant EPP-2019-524, by the German Federal Ministry of Education and Research under grant 16DII133, and by Deutsche Forschungsgemeinschaft under grant ME 3711/2-
Evaluation of Algorithmic Contributions to Business Process Management
No full textAlgorithms play an important role for business process management. Indeed, all of the 27 papers accepted for BPM 2023 refer to algorithms in one way or the other. Some of these research papers propose new algorithms, such as the paper on the Agent Miner [8], they compose analytical pipelines, such as the paper on process mining video data [5], or they discuss the evaluation of algorithms, such as the paper on sustainability dimensions [3]. So far, however, the BPM community has not developed explicit guidelines for evaluating algorithmic contributions.
This tutorial describes an overarching methodological framework of how algorithms can be researched. In turn, we discuss its foundations in three dimensions. First, we describe four ontological entities including real-world problem, algorithmic task, algorithm design, and algorithm implementations. Second, we distinguish knowledge contributions along two dimensions, that is a) knowledge relating to tasks and to designs and b) knowledge of and knowledge about. Third, we discuss how methodologically new or better knowledge is established. The objective of this tutorial is to present specific strategies how algorithms can be systematically researched for early career researchers. These are illustrated by good examples of BPM papers.The research of the author was supported by Einstei, Foundation Berlin under grant EPP-2019-524, by the German Federal Ministry of Education and Research under grant 16DII133, and by Deutsche Forschungsgemeinschaft under grant ME 3711/2-
Configuring SQL-based process mining for performance and storage optimisation
No full textProcess mining is the area of research that embraces the automated discovery, conformance checking and enhancement of process models. Declarative process mining approaches offer capabilities to automatically discover models of flexible processes from event logs. However, they often suffer from performance issues with real-life event logs, especially when constraints to be discovered go beyond a standard repertoire of templates. By leveraging relational database performance technology, a new approach based on SQL querying has been recently introduced, to improve performance though still keeping the nature of discovered constraints customisable. In this paper, we provide an in-depth analysis of configuration parameters that allow for a speed-up of the answering time and a decrease of storage space needed for query processing. Thereupon, we provide configuration recommendations for process mining with SQL on relational databases
[Report to Chief J. E. Curry, by an unknown author #1]
No full textReport to Chief J. E. Curry, by an unknown author. The report contains a list of officers who gave depositions to the United States Attorney
[Report to Chief J. E. Curry, by an unknown author #2]
No full textReport to Chief J. E. Curry, by an unknown author. The report contains a list of officers who gave depositions to the United States Attorney
Who-does-what: A knowledge base of people's occupations and job activities
No full textWe present a novel resource called "Who-Does-What" (WDW), which provides a knowledge base of activities for classes of people engaged in a wide range of different occupations. WDWis semi-automatically created by automatically extracting structured job activity descriptions from theWeb (we use here the O∗Net website). These descriptions are used to populate the taxonomic backbone provided by the manually-created Standard Occupational Classification (SOC) of the US Department of Labor
Process discovery and exploration
No full textProcess mining, and in particular process discovery, have gained traction as a technique for analysing actual process executions from event data recorded in event logs. Process discovery aims to automatically derive a model of the process. Current process discovery techniques either do not provide executable semantics, do not guarantee to return models without deadlocks, or do not achieve a right balance between quality criteria
Exploring processes and deviations
No full textIn process mining, one of the main challenges is to discover a process model, while balancing several quality criteria. This often requires repeatedly setting parameters, discovering a map and evaluating it, which we refer to as process exploration. Commercial process mining tools like Disco, Perceptive and Celonis are easy to use and have many features, such as log animation, immediate parameter feedback and extensive filtering options, but the resulting maps usually have no executable semantics and due to this, deviations cannot be analysed accurately. Most more academically oriented approaches (e.g., the numerous process discovery approaches supported by ProM) use maps having executable semantics (models), but are often slow, make unrealistic assumptions about the underlying process, or do not provide features like animation and seamless zooming. In this paper, we identify four aspects that are crucial for process exploration: zoomability, evaluation, semantics, and speed. We compare existing commercial tools and academic workflows using these aspects, and introduce a new tool, that aims to combine the best of both worlds. A feature comparison and a case study show that our tool bridges the gap between commercial and academic tools
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