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KES - IADSP 2024 : KES 2024 -- Invited Session on Interdisciplinary Approaches in Data Science Practice


When Sep 11, 2024 - Sep 13, 2024
Where Seville
Submission Deadline Apr 22, 2024
Notification Due May 20, 2024
Final Version Due Jun 3, 2024
Categories    computer science   data science   machine learning   business intelligence

Call For Papers


Invited Session on Interdisciplinary Approaches in Data Science Practice (IADSP 2024)
URL: (IS19),
Deadline for Paper submissions to: 8th of April, 2024

Organized within the framework of the
28th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2024)

11 - 13 September, 2024



One of the hot issues in many organization systems is how to transform large amounts of daily collected operational data into the useful knowledge from the perspective of declared company goals and expected business values. The main concerns of this invited session are Data Science paradigms, as a set of theories, methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information, knowledge, and value. Various interdisciplinary oriented Data Science approaches may provide organizations the ability to use their data to improve quality of business, increase financial efficiency and operational effectiveness, conduct innovative research and satisfy regulatory requirements. Applications of appropriate Data Science implementation methodologies together with outcomes related to collaborative and interdisciplinary approaches are inevitable when applying Data Science approaches to large and complex organization systems. For many years, such interdisciplinary approaches were used in analyzing big data gathered from not only business sectors, but also public, non-profit, and government sectors.

The main goal of the session is to attract researchers from all over the world who will present their contributions, interdisciplinary approaches or case studies in the area of Data Science. The focus in Data Science may be set to various aspects, such as: data warehousing, reporting, online analytical processing, data analytics, data mining, process mining, text mining, predictive analytics and prescriptive analytics, as well as various aspects of machine learning, big data and time series analysis. We express an interest in gathering scientists and practitioners interested in applying Data Science approaches in public and government sectors, such as healthcare, education, or security services, as well as in industry sectors and Industry 4.0 approaches. Experts from all other sectors are also welcomed.

Submissions are expected from, but not limited to the following topics:

* Artificial Intelligence, Machine Learning, Deep Learning Theoretical and practical aspects
* Data quality assessment and improvement: preprocessing, cleaning, and missing data
* Data Science and Analytics for Healthcare and other Public Sectors
* Educational Data Science
* Impacts of Business Analytics for the performance of profit or non-profit organizations
* Implications of Blockchain for Data Science
* Information Visualization and Visual Analytics
* Process Mining, Pattern Mining, and Swarm Intelligence
* Semi-structured or unstructured data in Business Intelligence systems
* Social network Data Analysis
* Statistical analysis and characterization, predictive analytics and prescriptive analytics
* Teaching new approaches of Data Science in academic and industrial environments
* Theoretical and practical aspects, Applications and Industry Experience in Data Science
* Web survey methods in Business Intelligence and Data Science


* Papers will be refereed and accepted on the basis of their scientific merit and relevance to the conference.
* The required full paper length is 8 to 10 pages.
* Call for papers and detailed information for the authors can be found at


* Paper submission: 22 April,2024
* Acceptance notification: 20 May, 2024
* Final paper submission: 3 June, 2024
* Conference: 11 - 13 September, 2024


* Ivan Lukovic, University of Belgrade, Serbia
* Ralf-Christian Haerting, University of Aalen, Germany

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