Work place: Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, L-4370, Luxembourg
E-mail: taras.lukashiv@uni.lu
Website:
Research Interests:
Biography
Taras Lukashiv was born in Ukraine on March 20, 1983. He received his Master’s degree in Mathematics from the Faculty of Applied Mathematics at Yuriy Fedkovych Chernivtsi National University, Ukraine, in 2004. In 2010, he was awarded the scientific degree of Candidate of Physical and Mathematical Sciences (specialty 01.05.02 – Mathematical Modelling and Calculation Methods) by Yuriy Fedkovych Chernivtsi National University, Ukraine.
Since 2004, he worked as an assistant professor, and from 2019 to 2024 as an associate professor at Yuriy Fedkovych Chernivtsi National University.
From 2022 to 2024 - visitor scientist at the Luxembourg Institute of Health, Luxembourg. From 2024 - post- doctoral researcher at the University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Department of Clinical and Translational Informatics.
Dr. Lukashiv is membership Multidisciplinary Digital Publishing Institute (MDPI) editorial team, Journal of Economics and Management Sciences editorial team, and Natural & Mathematical Sciences in Medicine and Medical Education editorial team; American Mathematical Society, International Society for Computational Biology, and International Society for Clinical Biostatistics.
By Igor V. Malyk Yevhen Kyrychenko Mykola Gorbatenko Taras Lukashiv
DOI: https://doi.org/10.5815/ijitcs.2025.05.07, Pub. Date: 8 Oct. 2025
Efficient comparison of heterogeneous tabular datasets is difficult when sources are unknown or weakly documented. We address this problem by introducing a unified, type-aware framework that builds compact data represen- tations (CDRs)—concise summaries sufficient for downstream analysis—and a corresponding similarity graph (and tree) over a data corpus. Our novelty is threefold: (i) a principled vocabulary and procedure for constructing CDRs per variable type (factor, time, numeric, string), (ii) a weighted, type-specific similarity metric we call Data Information Structural Similarity (DISS) that aggregates distances across heterogeneous summaries, and (iii) an end-to-end, cloud-scalable real- ization that supports large corpora. Methodologically, factor variables are summarized by frequency tables; time variables by fixed-bin histograms; numeric variables by moment vectors (up to the fourth order); and string variables by TF–IDF vectors. Pairwise similarities use Hellinger, Wasserstein (p=1), total variation, and L1/L2 distances, with MAE/MAPE for numeric summaries; the DISS score combines these via learned or user-set weights to form an adjacency graph whose minimum-spanning tree yields a similarity tree. In experiments on multi-source CSVs, the approach enables accurate retrieval of closest datasets and robust corpus-level structuring while reducing storage and I/O. This contributes a repro- ducible pathway from raw tables to a similarity tree, clarifying terminology and providing algorithms that practitioners can deploy at scale.
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