Towards Automated Performance Optimization of BPMN Business Processes
August 29, 2015 Β· Declared Dead Β· π Symposium on Advances in Databases and Information Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Anastasios Gounaris
arXiv ID
1508.07455
Category
cs.DB: Databases
Cross-listed
cs.SE
Citations
13
Venue
Symposium on Advances in Databases and Information Systems
Last Checked
4 months ago
Abstract
Business Process Model and Notation (BPMN) provides a standard for the design of business processes. It focuses on bridging the gap between the analysis and the technical perspectives, and aims to deliver process automation. The aim of this technical report is to complement this effort by transferring knowledge from the related field of data-centric workflows aiming to provide automated performance optimization of the business process execution. Automated optimization lifts a burden from BPMN designers and increases workflow flexibility and resilience. As a key step towards this goal, the contribution of this work is to provide a methodology to map BPMNv2.0 models to annotated directed acyclic graphs, which emphasize the volume of the tokens exchanged and are amenable to existing automated optimization algorithms. In addition, concrete examples of mappings are given, while the optimization opportunities that are opened are explained, thus providing insights into the potential performance benefits and we discuss technical research issues.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Databases
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Untangling Blockchain: A Data Processing View of Blockchain Systems
R.I.P.
π»
Ghosted
Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades
R.I.P.
π»
Ghosted
BLOCKBENCH: A Framework for Analyzing Private Blockchains
R.I.P.
π»
Ghosted
Data Synthesis based on Generative Adversarial Networks
R.I.P.
π»
Ghosted
HoloClean: Holistic Data Repairs with Probabilistic Inference
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted