Research in computing-intensive simulations for nature-oriented civil-engineering and related scientific fields, using machine learning and big data: an overview of open problems
Abstract This article presents a taxonomy and represents a repository of open problems in computing for numerically and logically intensive problems in a number of disciplines that have to synergize for the best performance of simulation-based feasibility studies on nature-oriented engineering in ge...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-05-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-023-00731-6 |
| _version_ | 1797817910624780288 |
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| author | Zoran Babović Branislav Bajat Vladan Đokić Filip Đorđević Dražen Drašković Nenad Filipović Borko Furht Nikola Gačić Igor Ikodinović Marija Ilić Ayhan Irfanoglu Branislav Jelenković Aleksandar Kartelj Gerhard Klimeck Nenad Korolija Miloš Kotlar Miloš Kovačević Vladan Kuzmanović Marko Marinković Slobodan Marković Avi Mendelson Veljko Milutinović Aleksandar Nešković Nataša Nešković Nenad Mitić Boško Nikolić Konstantin Novoselov Arun Prakash Ivan Ratković Zoran Stojadinović Andrey Ustyuzhanin Stan Zak |
| author_facet | Zoran Babović Branislav Bajat Vladan Đokić Filip Đorđević Dražen Drašković Nenad Filipović Borko Furht Nikola Gačić Igor Ikodinović Marija Ilić Ayhan Irfanoglu Branislav Jelenković Aleksandar Kartelj Gerhard Klimeck Nenad Korolija Miloš Kotlar Miloš Kovačević Vladan Kuzmanović Marko Marinković Slobodan Marković Avi Mendelson Veljko Milutinović Aleksandar Nešković Nataša Nešković Nenad Mitić Boško Nikolić Konstantin Novoselov Arun Prakash Ivan Ratković Zoran Stojadinović Andrey Ustyuzhanin Stan Zak |
| author_sort | Zoran Babović |
| collection | DOAJ |
| description | Abstract This article presents a taxonomy and represents a repository of open problems in computing for numerically and logically intensive problems in a number of disciplines that have to synergize for the best performance of simulation-based feasibility studies on nature-oriented engineering in general and civil engineering in particular. Topics include but are not limited to: Nature-based construction, genomics supporting nature-based construction, earthquake engineering, and other types of geophysical disaster prevention activities, as well as the studies of processes and materials of interest for the above. In all these fields, problems are discussed that generate huge amounts of Big Data and are characterized with mathematically highly complex Iterative Algorithms. In the domain of applications, it has been stressed that problems could be made less computationally demanding if the number of computing iterations is made smaller (with the help of Artificial Intelligence or Conditional Algorithms), or if each computing iteration is made shorter in time (with the help of Data Filtration and Data Quantization). In the domain of computing, it has been stressed that computing could be made more powerful if the implementation technology is changed (Si, GaAs, etc.…), or if the computing paradigm is changed (Control Flow, Data Flow, etc.…). |
| first_indexed | 2024-03-13T09:01:34Z |
| format | Article |
| id | doaj.art-5fc429a2f6b24396bfb370e39e6046ff |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-03-13T09:01:34Z |
| publishDate | 2023-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-5fc429a2f6b24396bfb370e39e6046ff2023-05-28T11:19:22ZengSpringerOpenJournal of Big Data2196-11152023-05-0110112110.1186/s40537-023-00731-6Research in computing-intensive simulations for nature-oriented civil-engineering and related scientific fields, using machine learning and big data: an overview of open problemsZoran Babović0Branislav Bajat1Vladan Đokić2Filip Đorđević3Dražen Drašković4Nenad Filipović5Borko Furht6Nikola Gačić7Igor Ikodinović8Marija Ilić9Ayhan Irfanoglu10Branislav Jelenković11Aleksandar Kartelj12Gerhard Klimeck13Nenad Korolija14Miloš Kotlar15Miloš Kovačević16Vladan Kuzmanović17Marko Marinković18Slobodan Marković19Avi Mendelson20Veljko Milutinović21Aleksandar Nešković22Nataša Nešković23Nenad Mitić24Boško Nikolić25Konstantin Novoselov26Arun Prakash27Ivan Ratković28Zoran Stojadinović29Andrey Ustyuzhanin30Stan Zak31Innovation Center of the School of Electrical EngineeringFaculty of Civil Engineering, University of BelgradeUniversity of BelgradeFaculty of Civil Engineering, University of BelgradeUniversity of BelgradeHarvard University Medical SchoolFlorida Atlantic UniversityEsperanto TechnologiesHDLDHMITPurdue UniversityUniversity of BelgradeUniversity of BelgradePurdue UniversityUniversity of BelgradeUniversity of BelgradeFaculty of Civil Engineering, University of BelgradeFaculty of Civil Engineering, University of BelgradeFaculty of Civil Engineering, University of BelgradeUniversity of Novi SadTechnionIndiana UniversityUniversity of BelgradeUniversity of BelgradeUniversity of BelgradeUniversity of BelgradeNational University of SingaporePurdue UniversityEsperanto TechnologiesFaculty of Civil Engineering, University of BelgradeNational University of SingaporePurdue UniversityAbstract This article presents a taxonomy and represents a repository of open problems in computing for numerically and logically intensive problems in a number of disciplines that have to synergize for the best performance of simulation-based feasibility studies on nature-oriented engineering in general and civil engineering in particular. Topics include but are not limited to: Nature-based construction, genomics supporting nature-based construction, earthquake engineering, and other types of geophysical disaster prevention activities, as well as the studies of processes and materials of interest for the above. In all these fields, problems are discussed that generate huge amounts of Big Data and are characterized with mathematically highly complex Iterative Algorithms. In the domain of applications, it has been stressed that problems could be made less computationally demanding if the number of computing iterations is made smaller (with the help of Artificial Intelligence or Conditional Algorithms), or if each computing iteration is made shorter in time (with the help of Data Filtration and Data Quantization). In the domain of computing, it has been stressed that computing could be made more powerful if the implementation technology is changed (Si, GaAs, etc.…), or if the computing paradigm is changed (Control Flow, Data Flow, etc.…).https://doi.org/10.1186/s40537-023-00731-6Computing paradigmsArtificial intelligenceControl flowData flowBig data |
| spellingShingle | Zoran Babović Branislav Bajat Vladan Đokić Filip Đorđević Dražen Drašković Nenad Filipović Borko Furht Nikola Gačić Igor Ikodinović Marija Ilić Ayhan Irfanoglu Branislav Jelenković Aleksandar Kartelj Gerhard Klimeck Nenad Korolija Miloš Kotlar Miloš Kovačević Vladan Kuzmanović Marko Marinković Slobodan Marković Avi Mendelson Veljko Milutinović Aleksandar Nešković Nataša Nešković Nenad Mitić Boško Nikolić Konstantin Novoselov Arun Prakash Ivan Ratković Zoran Stojadinović Andrey Ustyuzhanin Stan Zak Research in computing-intensive simulations for nature-oriented civil-engineering and related scientific fields, using machine learning and big data: an overview of open problems Journal of Big Data Computing paradigms Artificial intelligence Control flow Data flow Big data |
| title | Research in computing-intensive simulations for nature-oriented civil-engineering and related scientific fields, using machine learning and big data: an overview of open problems |
| title_full | Research in computing-intensive simulations for nature-oriented civil-engineering and related scientific fields, using machine learning and big data: an overview of open problems |
| title_fullStr | Research in computing-intensive simulations for nature-oriented civil-engineering and related scientific fields, using machine learning and big data: an overview of open problems |
| title_full_unstemmed | Research in computing-intensive simulations for nature-oriented civil-engineering and related scientific fields, using machine learning and big data: an overview of open problems |
| title_short | Research in computing-intensive simulations for nature-oriented civil-engineering and related scientific fields, using machine learning and big data: an overview of open problems |
| title_sort | research in computing intensive simulations for nature oriented civil engineering and related scientific fields using machine learning and big data an overview of open problems |
| topic | Computing paradigms Artificial intelligence Control flow Data flow Big data |
| url | https://doi.org/10.1186/s40537-023-00731-6 |
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