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Supporting Domain Data Selection in Data-Enhanced Process Models

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Jonas Cremerius, Mathias Weske

Databases · Submitted 13 Jan 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim This paper introduces three mechanisms to support domain data selection, allowing process analysts and domain experts to progressively reach their information of interest.

how it is stated · asserted 1.00 read the record →

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arXiv ID2201.07001 (version 1)
Pith IDRQCOVWPL, Pith's permanent record of this paper and its review
CitationJonas Cremerius, Mathias Weske. "Supporting Domain Data Selection in Data-Enhanced Process Models." arXiv:2201.07001 (2022). https://pith.science/paper/RQCOVWPL
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,867 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.18 · human participants 0.69 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.08 · compares with prior results 0.06 · mentions code or data availability 0.04.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Process mining bridges the gap between process management and data science by discovering process models using event logs derived from real-world data. Besides mandatory event attributes, additional attributes can be part of an event representing domain data, such as human resources and costs. Data-enhanced process models provide a visualization of domain data associated to process activities directly in the process model, allowing to monitor the actual values of domain data in the form of event attribute aggregations. However, event logs can have so many attributes that it is difficult to decide, which one is of interest to observe throughout the process. This paper introduces three mechanisms to support domain data selection, allowing process analysts and domain experts to progressively reach their information of interest. We applied the proposed technique on the MIMIC-IV real-world data set on hospitalizations in the US.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Process mining bridges the gap between process management and data science by discovering process models using event logs derived from real-world data. background 1.00 0.05 0.02 asserted 1.00
s1 Besides mandatory event attributes, additional attributes can be part of an event representing domain data, such as human resources and costs. background 1.00 0.06 0.02 asserted 0.77
s2 Data-enhanced process models provide a visualization of domain data associated to process activities directly in the process model, allowing to monitor the actual values of domain data in the form of event attribute aggregations. background 0.97 0.11 0.04 asserted 1.00
s3 However, event logs can have so many attributes that it is difficult to decide, which one is of interest to observe throughout the process. background 0.64 0.09 0.02 asserted 0.97
s4 This paper introduces three mechanisms to support domain data selection, allowing process analysts and domain experts to progressively reach their information of interest. contribution 1.00 0.86 0.28 asserted 1.00
s5 We applied the proposed technique on the MIMIC-IV real-world data set on hospitalizations in the US. method 0.87 0.96 0.04 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from analysis of existing data 0.92.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Process mining bridges the gap between process management and data science by discovering process models using event logs derived from real-world data. Besides mandatory event attributes, additional attributes can be part of an event representing domain data, such as human resources and costs. Data-enhanced process models provide a visualization of domain data associated to process activities directly in the process model, allowing to monitor the actual values of domain data in the form of event attribute aggregations. However, event logs can have so many attributes that it is difficult to decide, which one is of interest to observe throughout the process. This paper introduces three mechanisms to support domain data selection, allowing process analysts and domain experts to progressively reach their information of interest. We applied the proposed technique on the MIMIC-IV real-world data set on hospitalizations in the US.

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What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 4 sentences without a confident label

Cites as background 1 paper
  • In data-enhanced process models, domain data can be displayed at activities, where an event attribute was used or modified, allowing to observe how domain data is influenced by the process [ref] .

    Data-Enhanced Process Models in Process Mining · Introduction · 0.82

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