Background
Alireza is an Associate Professor at the Department of Computer Science and Communication of Østfold University College. He received his Ph.D. in Computer Science within the field of Visual Analytics from the University of Oslo (2024). Alireza holds a bachelor's degree in information systems and engineering from MSRIT (2013). He then continued his studies by receiving his first master's degree in software engineering from Azad University (2018), and later by receiving his second master's degree in applied computer science from Østfold University College (2020). His PhD title is "Visual Analytics Modeling For Decision-Support Systems: A Framework Based on Model-driven Engineering". Alireza's main research interests are Visual Analytics, Information Systems, Explainable AI, and Machine Learning. As part of his PhD, he works in a project called DigiMat which aims to make the Norwegian food supply chain smart, transparent and sustainable.
Academic interests
- Visual Analytics
- Information Systems
- Machine Learning
- Software Engineering
- Business Intelligence
Courses taught
Supervision
Publications
-
Khakpour, Alireza; Vazquez-Ingelmo, Andrea; Colomo-Palacios, Ricardo; Garcia-Penalvo, Francisco J. & Martini, Antonio
(2024).
ModelViz: A Model-Driven Engineering Approach for Visual Analytics System Design.
IEEE Access.
12,
p. 42667–42682.
doi:
10.1109/ACCESS.2024.3379268.
Full text in Research Archive
-
Khakpour, Alireza; Colomo-Palacios, Ricardo; Martini, Antonio & Sánchez-Gordón, Mary
(2023).
The Use of Domain-Specific Languages for Visual Analytics: A Systematic Literature Review.
Technologies.
11(2).
doi:
10.3390/technologies11020037.
Full text in Research Archive
Show summary
Visual Analytics (VA) is a multidisciplinary field that requires various skills including but not limited to data analytics, visualizations, and the corresponding domain knowledge. Recently, many studies proposed creating and using Domain-Specific Languages (DSLs) for VA in order to abstract complexities and assist designers in developing better VAs for different data domains. However, development methods and types of DSLs vary for different applications and objectives. In this study, we conducted a systematic literature review to overview DSL methods and their intended applications for VA systems. Moreover, the review outlines the benefits and limitations of each of these methods. The aim is to provide decision support for both the research and development communities
to choose the most compatible approach for their application. We think the communication of this research delivers a broad figure of previous relevant research and assists with the transfer and adaptation of the results to other domains.
-
Khakpour, Alireza; Colomo-Palacios, Ricardo & Martini, Antonio
(2023).
Towards a framework for developing visual analytics in supply chain environments.
International Journal of Information Systems and Project Management.
ISSN 2182-7796.
11(1),
p. 52–71.
doi:
10.12821/ijispm110103.
Full text in Research Archive
-
Khakpour, Alireza; Colomo-Palacios, Ricardo & Martini, Antonio
(2021).
Visual Analytics for Decision Support: A Supply Chain Perspective.
IEEE Access.
9,
p. 81326–81344.
doi:
10.1109/ACCESS.2021.3085496.
Full text in Research Archive
Show summary
Supply chain (SC) activities generate huge amount of data that can be used in decision making processes. However, proper data analytics techniques are required to combine, organize, and analyze data from different sources and produce required insights available for decision makers. These techniques promote analytical reasoning of the events and patterns hidden in the data using visualizations, so-called Visual Analytics (VA). Although there is a large number of VA systems to facilitate the process of analysis and decision making, there is a lack of an adequate overview of what already exists in this area for SC management. To address that need, we conducted a systematic literature review to analyze the state of the art in SC VA systems. Particularly, we focus on use cases, the type of the decisions that a VA system intended to support, the type of visualizations employed, the type of analytics used, and the data that has been used for analysis. The goal of this study is to provide SC and VA researchers with an overview of the works carried out in the field of SC VA, helping them to observe latest trends and to recognize existing gaps that need further investigation. Consequently, a mapping between decisions of various SC business processes and their reciprocal visualization techniques and tactics have been provided. Adding to that, VA applications and use cases in SC are identified based on the SC Operation Reference (SCOR) model and underlying decision areas are recognized.
-
Khakpour, Alireza
(2021).
Information Sharing for Customized Dynamic Visual Analytics: A Framework.
CEUR Workshop Proceedings.
2906,
p. 89–97.
Full text in Research Archive
Show summary
Supply chain activities generate massive amount of data by several actors such as, suppliers, manufacturers, warehouses, distributers, and wholesalers. Visual analytics (VA) plays a key role in knowledge discovery and insight generation from this data and helps various players to enhance their operational and strategic decision making. This is more essential for Fast moving consumer goods (FMCG) industry, given the size of the industry and its sensitivity to the diverse market uncertainties. In this paper, we present a PhD research plan that responds to the requirements of a FMCG supply chain VA system by means of a comprehensive framework. In this regard, the information flow throughout the supply chain is a significant factor for developing a reliable and efficient VA solution and a proper information flow throughout the supply chain can be enhanced with the help of the framework consisting of modules including Data Generation, Data Integration and Management, Data Analytics, Data Visualization, and Data-driven decision making. The aim of the study is to explore the development of a VA framework that acts as a guideline for supply chain players to improve their analytical capabilities.
-
-
Khakpour, Alireza & Colomo-Palacios, Ricardo
(2020).
Convergence of Gamification and Machine Learning: A Systematic Literature Review.
Technology, Knowledge and Learning.
ISSN 2211-1662.
26,
p. 597–636.
doi:
10.1007/s10758-020-09456-4.
Full text in Research Archive
Show summary
Recent developments in human–computer interaction technologies raised the attention towards gamification techniques, that can be defined as using game elements in a non-gaming context. Furthermore, advancement in machine learning (ML) methods and its potential to enhance other technologies, resulted in the inception of a new era where ML and gamification are combined. This new direction thrilled us to conduct a systematic literature review in order to investigate the current literature in the field, to explore the convergence of these two technologies, highlighting their influence on one another, and the reported benefits and challenges. The results of the study reflect the various usage of this confluence, mainly in, learning and educational activities, personalizing gamification to the users, behavioral change efforts, adapting the gamification context and optimizing the gamification tasks. Adding to that, data collection for machine learning by gamification technology and teaching machine learning with the help of gamification were identified. Finally, we point out their benefits and challenges towards streamlining future research endeavors.
View all works in NVA
Published Dec. 5, 2019 12:23 PM
- Last modified Nov. 3, 2025 10:34 AM
