Towards a Data-Driven Framework for Measuring Process Performance

Kis, Isabella; Bachhofner, Stefan; Ciccio, Claudio Di; Mendling, Jan

doi:10.1007/978-3-319-59466-8_1

Cited by 11 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 3 also shows that over a fourth (18) of the publications propose performance indicators. These indicators allow the performance of a process to be analyzed by comparing ideal values with empirical values [76]. As an example, in [75], performance indicators to be used as a resource in the application of process mining within an emergency room are proposed.…”

Section: Methodologies For Joint Analysis Of Costs and Other Dimensio...mentioning

confidence: 99%

“…The consideration of the devil's quadrangle (DQ) for defining performance indicators was often observed. The DQ depicts the process performance according to four dimensions: time, cost, quality and flexibility [76], thus allowing to evaluate the process in a bounded way understanding the relationship between the four dimensions. Nearly a tenth (6) of the publications consider the devil's quadrangle.…”

Section: (12%)mentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Literature Review of the Analysis of Costs and Other Dimensions in Process Mining and Related Disciplines

Velásquez,

Gómez-Herrera,

Sepúlveda

2024

IEEE Access

View full text Add to dashboard Cite

Process mining allows the discovery, monitoring, and improvement of processes through records of their executions. When used for discovery, models depicting the process behavior can be obtained and then analyzed through a performance perspective, focusing on performance indicators for the dimension of interest. In process mining, there has been scarce research on the analysis of some dimensions, such as the cost dimension. A systematic literature review has been conducted to identify the methods used for analyzing the cost dimension in process mining and other process-oriented disciplines, the dimensions considered jointly with costs, and how such a joint analysis is carried out. The review identified 68 publications. The results indicate ample research within distinct disciplines, like business process management and cost management, regarding the costs of performing processes, their reporting and forecasting. However, there is a need to further enrich their visualization and analysis, and to provide cost-aware operational support. Moreover, the utility of further researching cost analysis jointly with other dimensions, like time, flexibility, and quality, is also observed. These results provide a structured focal point of research that can be considered when analyzing costs from a process perspective and the identification of research gaps within this domain.

show abstract

Section: Methodologies For Joint Analysis Of Costs and Other Dimensio...mentioning

confidence: 99%

Section: (12%)mentioning

confidence: 99%

A Systematic Literature Review of the Analysis of Costs and Other Dimensions in Process Mining and Related Disciplines

Velásquez,

Gómez-Herrera,

Sepúlveda

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The military can use this information to protect embassies in the case of an emergency or in the case of a natural disaster. Consequently, point clouds derived form tri-stereo satellite imagery can bring value for companies and public institutions alike, which can incorporate it in the life cycles of their business processes [77], in, e.g., process monitoring [78,79], process compliance [80], process conformance checking [81,82], process mining [83][84][85][86], and performance mining [87][88][89].…”

Section: Introductionmentioning

confidence: 99%

Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery

et al. 2020

Self Cite

View full text Add to dashboard Cite

We studied the applicability of point clouds derived from tri-stereo satellite imagery for semantic segmentation for generalized sparse convolutional neural networks by the example of an Austrian study area. We examined, in particular, if the distorted geometric information, in addition to color, influences the performance of segmenting clutter, roads, buildings, trees, and vehicles. In this regard, we trained a fully convolutional neural network that uses generalized sparse convolution one time solely on 3D geometric information (i.e., 3D point cloud derived by dense image matching), and twice on 3D geometric as well as color information. In the first experiment, we did not use class weights, whereas in the second we did. We compared the results with a fully convolutional neural network that was trained on a 2D orthophoto, and a decision tree that was once trained on hand-crafted 3D geometric features, and once trained on hand-crafted 3D geometric as well as color features. The decision tree using hand-crafted features has been successfully applied to aerial laser scanning data in the literature. Hence, we compared our main interest of study, a representation learning technique, with another representation learning technique, and a non-representation learning technique. Our study area is located in Waldviertel, a region in Lower Austria. The territory is a hilly region covered mainly by forests, agriculture, and grasslands. Our classes of interest are heavily unbalanced. However, we did not use any data augmentation techniques to counter overfitting. For our study area, we reported that geometric and color information only improves the performance of the Generalized Sparse Convolutional Neural Network (GSCNN) on the dominant class, which leads to a higher overall performance in our case. We also found that training the network with median class weighting partially reverts the effects of adding color. The network also started to learn the classes with lower occurrences. The fully convolutional neural network that was trained on the 2D orthophoto generally outperforms the other two with a kappa score of over 90% and an average per class accuracy of 61%. However, the decision tree trained on colors and hand-crafted geometric features has a 2% higher accuracy for roads.

show abstract

“…This information enables process monitoring [7], process compliance [32], and process conformance checking [23,26] via process mining [29,30]. In addition, the information is also valuable for the analysis of the efficiency and effectiveness of process executions, i.e., for performance mining [15,11,19,4].…”

Section: Introductionmentioning

confidence: 99%

Extracting Event Logs for Process Mining from Data Stored on the Blockchain

Mühlberger

Bachhofner

Ciccio

et al. 2019

Lecture Notes in Business Information Processing

Self Cite

View full text Add to dashboard Cite

The integration of business process management with blockchains across organisational borders provides a means to establish transparency of execution and auditing capabilities. To enable process analytics, though, non-trivial extraction and transformation tasks are necessary on the raw data stored in the ledger. In this paper, we describe our approach to retrieve process data from an Ethereum blockchain ledger and subsequently convert those data into an event log formatted according to the IEEE Extensible Event Stream (XES) standard. We show a proof-of-concept software artefact and its application on a data set produced by the smart contracts of a process execution engine stored on the public Ethereum blockchain network.

show abstract

Towards a Data-Driven Framework for Measuring Process Performance

Cited by 11 publications

References 17 publications

A Systematic Literature Review of the Analysis of Costs and Other Dimensions in Process Mining and Related Disciplines

A Systematic Literature Review of the Analysis of Costs and Other Dimensions in Process Mining and Related Disciplines

Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery

Extracting Event Logs for Process Mining from Data Stored on the Blockchain

Contact Info

Product

Resources

About