dc.contributor.author | Medvedev, Viktor | |
dc.contributor.author | Kurasova, Olga | |
dc.contributor.author | Bernatavičienė, Jolita | |
dc.contributor.author | Treigys, Povilas | |
dc.contributor.author | Marcinkevičius, Virginijus | |
dc.contributor.author | Dzemyda, Gintautas | |
dc.date.accessioned | 2023-09-18T16:53:11Z | |
dc.date.available | 2023-09-18T16:53:11Z | |
dc.date.issued | 2017 | |
dc.identifier.issn | 1569-190X | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/117711 | |
dc.description.abstract | The conventional technologies and methods are not able to store and analyse recent data that come from different sources: various devices, sensors, networks, transactional applications, the web, and social media. Due to a complexity of data, data mining methods should be implemented using the capabilities of the Cloud technologies. In this paper, a new web-based solution named DAMIS, inspired by the Cloud, is proposed and implemented. It allows making massive data mining simpler, effective, and easily understandable for data scientists and business intelligence professionals by constructing scientific workflows for data mining using a drag and drop interface. The usage of scientific workflows allows composing convenient tools for modelling data mining processes and for simulation of real-world time- and resource-consuming data mining problems. The solution is useful to solve data classification, clustering, and dimensionality reduction problems. The DAMIS architecture is designed to ensure easy accessibility, usability, scalability, and portability of the solution. The proposed solution has a wide range of applications and allows to get deep insights into the data during the process of knowledge discovery. | eng |
dc.format | PDF | |
dc.format.extent | p. 34-46 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | Zentralblatt MATH (zbMATH) | |
dc.relation.isreferencedby | Mathematical Reviews | |
dc.relation.isreferencedby | Scopus | |
dc.relation.isreferencedby | Science Citation Index Expanded (Web of Science) | |
dc.source.uri | https://doi.org/10.1016/j.simpat.2017.03.001 | |
dc.subject | IK01 - Informacinės technologijos, ontologinės ir telematikos sistemos / Information technologies, ontological and telematic systems | |
dc.title | A new web-based solution for modelling data mining processes | |
dc.type | Straipsnis Web of Science DB / Article in Web of Science DB | |
dcterms.references | 59 | |
dc.type.pubtype | S1 - Straipsnis Web of Science DB / Web of Science DB article | |
dc.contributor.institution | Vilniaus universitetas | |
dc.contributor.institution | Vilniaus universitetas Vilniaus Gedimino technikos universitetas | |
dc.contributor.faculty | Fundamentinių mokslų fakultetas / Faculty of Fundamental Sciences | |
dc.subject.researchfield | T 007 - Informatikos inžinerija / Informatics engineering | |
dc.subject.ltspecializations | L106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies | |
dc.subject.en | Data mining | |
dc.subject.en | Scientific workflow | |
dc.subject.en | Modelling data mining process | |
dc.subject.en | Dimensionality reduction | |
dc.subject.en | Cloud computing | |
dc.subject.en | High-performance computing | |
dcterms.sourcetitle | Simulation modelling practice and theory | |
dc.description.volume | Vol. 76 | |
dc.publisher.name | Elsevier Science | |
dc.publisher.city | Amsterdam | |
dc.identifier.doi | 000404706300004 | |
dc.identifier.doi | 2-s2.0-85014722945 | |
dc.identifier.doi | 10.1016/j.simpat.2017.03.001 | |
dc.identifier.elaba | 20897414 | |