An open source approach of building web apps to support decision making with exploratory techniques. Case study: the multiple correspondence analysis on real data from agritourism. (16th April 2019)

• Record Type: Journal Article
• Authors: Moschidis, Stratos; Thanopoulos, Athanasios C.; Vlachopoulou, Maro
• Is Part Of: International journal of sustainable agricultural management and informatics. Volume 4:Number 3/4(2018)
• Page Start: 258
• Page End: 272
• Publication Date: 2019-04-16
• Subjects: decision support systems -- multicriteria analysis -- R programming -- multiple correspondence analysis -- MCA -- exploratory statistics -- big data -- open source software; Sustainable agriculture -- Management -- Periodicals; Sustainable agriculture -- Data processing -- Periodicals; Agricultural productivity -- Management -- Periodicals; Agricultural productivity -- Data processing -- Periodicals; Agricultural resources -- Data processing -- Periodicals; Agricultural systems -- Management -- Periodicals; Agricultural systems -- Data processing -- Periodicals; 338.1
• Languages: English
• ISSNs: 2054-5819

Abstract

The purpose of this paper is to present a technical path in order to formulate the creation of small customised web applications that handle big data. Such web tools could be easily integrated in knowledge-based or data driven decision support systems and provide added value. The paper studies the various ways in which relevant application can be developed and the findings resulted in a specific technical proposal. This is a combination of techniques and technologies that can be pursued to achieve more efficient and costless data analysis. To this direction, a novel demo web app was developed with open source code in which a user can login, upload data and analyse them in real-time with the multivariate exploratory method of multiple correspondence analysis (MCA). The application was created under R programming language and underlines the use of shiny library. The paper contributes to the scientific facilitation of researchers and professionals with a minimum technical background in order to perform online complex data analytics tasks. Eventually, this architecture enables extensibility and customisation that suggests how decision support modules should be delivered in intelligent data systems.