Abstract
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Towards semantically-aided domain specific business process modeling
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| Nikolaos Lagos, Adrian Mos, Mario Cortes-Cornax |
| Journal of Data Technologies and Applications |
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@article{article,
author = {Lagos, Nikolaos and Mos, Adrian and Cortes Cornax, Mario},
year = {2018},
month = {07},
pages = {},
title = {Towards Semantically-Aided Domain Specific Business Process Modeling (Full text at https://www.emeraldinsight.com/eprint/KZFCESS3GHUYXBGVP3P8/full - Limited access)},
journal = {Program electronic library and information systems},
doi = {10.1108/DTA-01-2018-0007}
}
Structured Abstract:
Purpose – Domain-specific process modelling has been proposed in the literature as a solution to several problems in Business Process Management (BPM). The problems arise when using only the generic Business Process Model and Notation (BPMN) standard language for modelling. This language includes domain ambiguity and difficult long-term model evolution. Domain-specific modelling involves developing concept definitions, domain specific processes and eventually industry-standard BPMN models. This entails a multi-layered modelling approach, where any of these artefacts can be modified by various stakeholders and changes done by one person may influence models used by others. There is therefore a need for tool support to keep track of changes done and their potential impacts.
Design/methodology/approach – 1. We use a multi-context systems based approach to infer the impacts that changes may cause in the models; and 2. We incrementally map components of business process models to ontologies.
Findings – Advantages of our framework include: 1. Identifying conflicts/inconsistencies across different business modelling layers; 2. Expressing rich information on the relations between two layers; 3. Calculating the impact of changes taking place in one layer to the rest of the layers; and 4. Selecting incrementally the most appropriate semantic models on which the transformations can be based.
Research limitations/implications – Extensive usability tests would enable to confirm further the findings of the paper. We consider this work as one of the foundational bricks that will enable further advances towards the governance of multi-layer business process modelling systems.
Practical implications – The approaches described here should improve the maintainability, reuse and clarity of business process models. This can improve data governance in large organisations and for large collections of processes by aiding various stakeholders to understand problems with process evolutions, changes and inconsistencies with business goals.
Originality/value – This paper fulfils an identified gap to enabling semantically-aided domain specific process modelling.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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