Xiaoyong Du’s research while affiliated with Renmin University of China and other places

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Publications (1)


Conceptual modeling in the era of Big Data and Artificial Intelligence: Research topics and introduction to the special issue
  • Article

September 2021

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83 Reads

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8 Citations

Data & Knowledge Engineering

Juan Trujillo

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Karen C. Davis

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Xiaoyong Du

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[...]

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Since the first version of the Entity–Relationship (ER) model proposed by Peter Chen over forty years ago, both the ER model and conceptual modeling activities have been key success factors for modeling computer-based systems. During the last decade, conceptual modeling has been recognized as an important research topic in academia, as well as a necessity for practitioners. However, there are many research challenges for conceptual modeling in contemporary applications such as Big Data, data-intensive applications, decision support systems, e-health applications, and ontologies. In addition, there remain challenges related to the traditional efforts associated with methodologies, tools, and theory development. Recently, novel research is uniting contributions from both the conceptual modeling area and the Artificial Intelligence discipline in two directions. The first one is efforts related to how conceptual modeling can aid in the design of Artificial Intelligence (AI) and Machine Learning (ML) algorithms. The second one is how Artificial Intelligence and Machine Learning can be applied in model-based solutions, such as model-based engineering, to infer and improve the generated models. For the first time in the history of Conceptual Modeling (ER) conferences, we encouraged the submission of papers based on AI and ML solutions in an attempt to highlight research from both communities. In this paper, we present some of important topics in current research in conceptual modeling. We introduce the selected best papers from the 37th International Conference on Conceptual Modeling (ER’18) held in Xi’an, China and summarize some of the valuable contributions made based on the discussions of these papers. We conclude with suggestions for continued research.

Citations (1)


... Our approach lies in creating a graph representation of data coming from different sources to enable the execution of predictive Artificial Intelligence algorithms [2]. Achieving this objective requires appropriate data engineering considerations, including the definition of a conceptual model to help design, develop and run these artificial intelligence solutions [10][11][12]. New research fields are opening strong opportunities for the definition of conceptual models [13,14]. ...

Reference:

To prompt or not to prompt: Navigating the use of Large Language Models for integrating and modeling heterogeneous data
Conceptual modeling in the era of Big Data and Artificial Intelligence: Research topics and introduction to the special issue
  • Citing Article
  • September 2021

Data & Knowledge Engineering