Sinisa M. Arsic, Marko M. Mihić, Dejan Petrović, Zorica M. Mitrović
{"title":"Data driven analysis of lifecycle stages in Serbian SMEs","authors":"Sinisa M. Arsic, Marko M. Mihić, Dejan Petrović, Zorica M. Mitrović","doi":"10.1109/ICECCME55909.2022.9987806","DOIUrl":null,"url":null,"abstract":"In this paper, the authors presented a concept design and prologue for a trend analysis software tool (entirely web based, in cloud environment), with an underlying machine learning model, incorporated with real time refresh of input data. Based on existing research, general architecture of the tool has been defined, as the foundation with its integral elements. The envisioned tool would enable SME business owners to perform trend analysis of their business indicators, to monitor essential outcome predictions, and to position themselves within wider macroeconomic situation, by aligning all similar SMEs into one universe. Plans for this research initiative include a full-scale crowdsourcing dataset population, training and deployment of a machine learning mode, with development of web application in parallel.","PeriodicalId":202568,"journal":{"name":"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICECCME55909.2022.9987806","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, the authors presented a concept design and prologue for a trend analysis software tool (entirely web based, in cloud environment), with an underlying machine learning model, incorporated with real time refresh of input data. Based on existing research, general architecture of the tool has been defined, as the foundation with its integral elements. The envisioned tool would enable SME business owners to perform trend analysis of their business indicators, to monitor essential outcome predictions, and to position themselves within wider macroeconomic situation, by aligning all similar SMEs into one universe. Plans for this research initiative include a full-scale crowdsourcing dataset population, training and deployment of a machine learning mode, with development of web application in parallel.