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Obtaining statistical data

Projects / Programmes source: ARIS

Computer Systems, Methodologies, and Intelligent Services

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Periods
January 1, 2020 - December 31, 2027
Research activity

Code Science Field Subfield
2.07.00  Engineering sciences and technologies  Computer science and informatics   

Code Science Field
T120  Technological sciences  Systems engineering, computer technology 

Code Science Field
1.02  Natural Sciences  Computer and information sciences 
Keywords
Digital twin, neuromuscular system, location intelligence, data structuring, feature learning, context representation, spatiotemporal regression, multi-objective dynamic optimization
Evaluation (rules)
source: COBISS
Evaluation of bibliographic research performance indicators according to ARIS methodology
Points
13,453.89
A''
2,163.26
A'
5,571.64
A1/2
8,482.08
CI10
20,465
CImax
2,039
h10
66
A1
46.69
A3
43.92
Data for the last 5 years (citations for the last 10 years) on April 25, 2024; A3 for period 2018-2022
Data for ARIS tenders ( 04.04.2019 – Programme tender , archive )
Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
source: WoS source: Scopus source: COBISS
Database Linked records Citations Pure citations Average pure citations
WoS  628  18,224  16,166  25.74 
Scopus  946  26,458  23,372  24.71 
Researchers (41)
no. Code Name and surname Research area Role Period No. of publications
1.  57243  David Bajs    Technical associate  2022 - 2024  0 
2.  55906  Mihael Baketarić  Computer science and informatics  Junior researcher  2021 - 2022  5 
3.  51049  Klemen Berkovič    Technical associate  2020 - 2024  19 
4.  23982  PhD Borko Bošković  Computer science and informatics  Researcher  2020 - 2024  230 
5.  16118  PhD Janez Brest  Computer science and informatics  Researcher  2020 - 2024  466 
6.  28691  Mario Casar    Technical associate  2020 - 2024  0 
7.  53590  PhD Jernej Cukjati  Computer science and informatics  Junior researcher  2020 - 2023  6 
8.  22707  PhD Matej Črepinšek  Computer science and informatics  Researcher  2020 - 2024  260 
9.  21537  PhD Matjaž Divjak  Computer science and informatics  Researcher  2020 - 2024  104 
10.  31054  PhD Iztok Fister  Computer science and informatics  Researcher  2020 - 2024  316 
11.  52032  PhD Aljaž Frančič  Systems and cybernetics  Junior researcher  2020 - 2022  26 
12.  54514  Jana Herzog  Computer science and informatics  Junior researcher  2020 - 2024  13 
13.  21301  PhD Aleš Holobar  Systems and cybernetics  Researcher  2020 - 2024  501 
14.  25672  Marjan Horvat    Technical associate  2020  23 
15.  37447  PhD David Jesenko  Computer science and informatics  Researcher  2020 - 2024  46 
16.  16259  PhD Simon Kolmanič  Computer science and informatics  Researcher  2020 - 2024  191 
17.  23454  PhD Tomaž Kosar  Computer science and informatics  Researcher  2020 - 2024  246 
18.  52447  Ivan Kovačič    Technical associate  2020 - 2022  16 
19.  53589  PhD Matej Kramberger  Computer science and informatics  Junior researcher  2020 - 2024  25 
20.  52029  Žiga Leber  Computer science and informatics  Junior researcher  2020 - 2024  11 
21.  21318  PhD Bogdan Lipuš  Computer science and informatics  Researcher  2020 - 2024  54 
22.  33709  PhD Niko Lukač  Computer science and informatics  Researcher  2020 - 2024  202 
23.  11191  PhD Marjan Mernik  Computer science and informatics  Researcher  2020 - 2024  690 
24.  36506  PhD Uroš Mlakar  Computer science and informatics  Researcher  2020 - 2024  64 
25.  29243  PhD Domen Mongus  Computer science and informatics  Researcher  2020 - 2024  278 
26.  21601  Jurij Munda    Technical associate  2020 - 2024  33 
27.  06823  PhD Milan Ojsteršek  Computer science and informatics  Researcher  2020 - 2024  526 
28.  15671  PhD David Podgorelec  Computer science and informatics  Researcher  2022 - 2024  214 
29.  58044  Jan Popič  Computer science and informatics  Junior researcher  2023 - 2024  0 
30.  15801  PhD Božidar Potočnik  Systems and cybernetics  Researcher  2020 - 2024  312 
31.  38213  PhD Miha Ravber  Computer science and informatics  Researcher  2020 - 2022  45 
32.  08638  PhD Krista Rizman Žalik  Computer science and informatics  Researcher  2020 - 2024  186 
33.  18726  PhD Damjan Strnad  Computer science and informatics  Researcher  2020 - 2024  231 
34.  56898  Niko Uremović  Computer science and informatics  Junior researcher  2022 - 2024  9 
35.  50649  PhD Filip Urh  Computer science and informatics  Junior researcher  2020 - 2021  29 
36.  28880  PhD Aleš Zamuda  Computer science and informatics  Researcher  2020 - 2024  226 
37.  32189  PhD Eva Zupančič  Computer science and informatics  Beginner researcher  2020  20 
38.  06671  PhD Borut Žalik  Computer science and informatics  Head  2020 - 2024  851 
39.  58043  Aljaž Žel  Computer science and informatics  Junior researcher  2023 - 2024  4 
40.  31475  Denis Žganec  Computer science and informatics  Technical associate  2020 - 2024  18 
41.  33994  PhD Danijel Žlaus  Computer science and informatics  Researcher  2020 - 2022  23 
Organisations (1)
no. Code Research organisation City Registration number No. of publications
1.  0796  University of Maribor, Faculty of Electrical Engineering and Computer Science  Maribor  5089638003  27,550 
Abstract
Increasing investments into Internet of things (IoT), big data analytics, and artificial intelligence propelled the development of digital replicas of real-world entities in a form of digital twins. These cyber-physical systems offer advanced monitoring, analytical, and predictive capabilities and have become a new major trend in computer science. Gartner ranks digital twins amongst ten key technologies of 2019, with expected 37% annual rate from the current 2 billion USD to 15 billion USD in 2023 and 26 billion USD by 2025. In this context, a particular attention is directed towards medical and health domains, due to the significance of their potential impacts. Digital twins can provide significant support in treatments of patients by predicting problems before they occur, help finding optimal solution, and, thus, reduce the risks as well as increase the effectiveness of rehabilitation. Today, the use of digital twins is limited to highly controlled environments and smart machines, whereas development of technologies to replicate more complex systems, such as those related to functions of human body, still faces significant challenges. These include: - Handling a variety of heterogeneous data streams that is required for learning the behaviour of monitored individuals requires significant improvements in automatic data alignment and structuring methodologies. - The existing medical data fusion and feature learning methods are still mainly focused on extracting features from individual data source and, thus, need to be significantly improved in order to be able to fully exploit complementarities in heterogeneous data streams. - Linking biomedical measurements with environmental and lifestyle factors that is required to bring laboratory studies into real-world environments calls for substantial advances in contextual feature extraction. - Approaches to monitoring microhabitats in which we live need to be improved as high scattering of environmental sensors results in large spatial and temporal information gaps. - The need for personalization of digital twins requires dynamic models to be optimized and adapted to monitored subjects, which is still beyond the capacity of contemporary optimisation algorithms. Within the proposed work program, we plan to build on our extensive past research in order to address these challenges and introduce a digital twin, capable of replicating functional parameters of the human neuromuscular system in the actual environment. With the aging society, neuromuscular diseases are becoming a major health related risk and the leading cause of work incapability with the total attributed costs in Slovenia reaching as high as 2% of its GDP. Our programme group joins leading experts in processing neuromuscular data streams with experts in spatiotemporal data analytics, semantic data processing and optimizations that will bring about this ambitious agenda through a streamlined iterative work plan in a co-creative manner.
Significance for science
The proposed programme focuses on the development of a technology stack that, under the umbrella of the focused domain of computer systems, methodologies and intelligent services, delivers the answers to some of the key challenges in the computer science. The latter are roughly summarized by the key objectives of the programme, namely the development of a digital twin of the »functional parameters of the human neuromuscular system in the actual environment«. These objectives open up the following potentials for the development in the computer science: • OBJECTIVE 1 - To develop a new methodology for automatic real-time semantic labelling of data streams using domain-specific ontologies and vocabularies. The developed system for automatic semantic data labelling using known domain-specific ontologies and vocabularies will allow automatic linking of data streams regardless of the data formats, timestamps, or their spatial resolutions. Due to the generality of the approach, the proposed system has the potential to improve development of intelligent systems as such. Obvious examples are support systems for autonomous driving (robots, cars, or drones), geographic information systems, and data mining, as they frequently need to align heterogeneous data streams. • OBJECTIVE 2 - To introduce new techniques that are capable of feature learning on several complementary data streams and can, therefore, overcome the time, space and information sparsity of individual streams, as well as improve the robustness, evaluation quality and complementarity of the acquired information. This will allow us to establish more reliable testing environments and reference values for the calibration and interpretation of functional state of tested subjects. While the conditions of the neuromuscular system represent only one, complex example of the applicability of such a generic approach, the developed methodology can be used in other domains as well, without particularly difficult adaptations. These include ambient and business intelligence as well as ensuring the autonomy of systems in applications, where efficiency depends on the ability to extract complementary information from several sources. • OBJECTIVE 3 - To advance in contextual data enrichment by implementing a new methodology for the integration of dynamic entities on demand using geo-fencing. Dynamic data integration on demand is one of the key problems in the field of fusion of sensor data. The development of an appropriate methodology thus enables improvement of intelligent systems that are not only limited to internal data streams, but are also aware of their own surroundings. In addition to the already presented potential in the context of health, the proposed methodology is crucial in the development of autonomous systems in general (for example, smart traffic signals that are able to adapt to the traffic, smart agricultural systems that are aware of weather conditions and natural phenomena such as drought, or smart infrastructure that is aware of the danger of possible environmental impacts). • OBJECTIVE 4 - To develop a new spatiotemporal regression method based on the integration of real-time sensor data with physical simulations. The method will enable supportive predictive analytics in decision-making systems as well as research-oriented environmental studies in the field of climatology (e.g. microclimate forecasting), construction (a search for an optimal construction and renovation of buildings), and energy (e.g. predicting energy potentials). • OBJECTIVE 5 - To upgrade dynamic optimization algorithms with multi-objective optimization and large-scale problem optimization and adaptations to handle a vast quantity of data. The evolutionary algorithms for multi-objective dynamic optimization and large-scale problem optimization. The algorithms, developed within this project, will also be useful in other areas, such as deep learning, where test data comes in real time, data clusteri
Significance for the country
The proposed program will develop the digital twin of “functional parameters of the human neuromuscular system in the actual environment” for predictive analytics of the human body parameters with regard to the environmental and lifestyle factors. As such, it brings key technological issues for creation of new products, innovations, and technological solutions. We have analysed the existing innovation partnerships and classified them according to possible common development of: • Products such as smart furniture and mobile applications for recreation. • Software solutions including data platforms and analytical tools. • Services for improved exercise processes or rehabilitation after injury While we have already developed a long-term cooperation with more than 10 companies and institutions, involved in development and marketing of the aforementioned products and services, we intend to expand the network of our partners during the programme. In doing so, we plan at least 10 new industrial partnerships, 12 new software products, 6 national and 1 international patent, and 15 other innovations – all in the next 6 years. Our society is facing problems of aging what decreases physical abilities of individuals. Although we are aware of negative effects of physical inactivity, we cannot objectively measure them. Instead, we often perceive them as a statistical threat that affects selected individuals only. The impacts of regular sport activities on the aging of the neuromuscular system and the impacts of the pollution of our microhabitats on the incidence of various diseases are also poorly understood. At the same time, we are exposed to increasing stress, which causes many mental and physical disorders. Unhealthy eating habits further contribute to the aforementioned socioeconomic problems. By controlling and intuitively presenting the functional state of the neuromuscular system in individuals, we expect positive effect of the proposed digital twin on overall human health and, consequently, reduction of the costs of their treatments and sick leaves. Today, the effect of regular recreation and sport activities on aging of individual’s neuromuscular system and the influence of the pollution of our microhabitats on development of various diseases are not well defined neither they are adequately measured. In the proposed programme, we intend to develop and improve existing methodologies for long-term tracking of neuromuscular activities in uncontrolled environments and for monitoring and predicting environmental impacts by introducing the concept of the digital twin of "functional parameters of the human neuromuscular system in the actual environment". For this purpose, we will introduce a unique infrastructure that will help to improve current diagnostic procedures and evaluate the risks to human health. Infrastructure will be tested on a limited number of healthy persons. Later on (after the end of this programme), we intend to expand its use to selected groups of patients with neurodegenerative diseases. Precise models and measurements of environmental parameters will also help to raise and personalize awareness about the harmful effects of pollution on society and on individuals. This will help to promote the protection of environment, especially our microhabitats. Finally, the results of the research will be included in the 1st and the 2nd degree of study programs “Computer Science and Information Technologies” and in the 3rd degree program “Computer Science and Informatics”. In this way, we will provide highly qualified professional staff needed to address a development and a socioeconomic progress of our society.
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