Abstract
Business Intelligence with Data Warehouse technologies are known in the literature as solutions that allow access to business data dynamically and analytical operations on them. Scientific literature lacks works that investigate the current use of these technologies in the agrarian sector, at the international level in the last 10 years. This work presents a bibliometric analysis, which was done through the ProKnow-C methodology, of the application of Business Intelligence and Data Warehouse technologies in the agrarian sector. The objective is to investigate the dissemination of such technologies in this sector in national and international scale. The main findings were the following: number of papers in last years are increasing. Majority of papers were found in the journal named Computers and Eletronics in Agriculture, with a great number of colaborations between authors of France. Few colaborations between authors from different countries were found. Sandro Bimonte was the most cited author. France and India highlight in researches approaching Data Warehouse and Business Intelligence usage in agrarian sciences. The majority of references from Bibliographic Portfolio were from 2001-2010. 66% of papers use some open source technology. Star schema is the most used modelling technique and the use of Unified Modeling Language by authors of France in agricultural Data Warehouse modelling is encouraged. The main limitations were the impossibility of free access in some databases, absence of research on proprietary solutions of technology market in the rural sector and few number of keyword searches.
Highlights
The need for technologies to optimize decision making processes in organizations is increasing, as large volumes of data are generated daily and the requirement for useful strategic information is growing (Shahid et al, 2016)
It is observed a research gap that this study aims to fill, by answering the following guiding question of this article: What is the scientific production related to the use of Business Intelligence (BI) and Data Warehouse (DW) technologies in the agrarian sector, in national and international conferences and journals? From this issue, questions arise such as: the main authors and countries that contribute to these researches, the main events and journals with relevant publications, the level of interest in this topic over the years, citations and recognition of the works found and quantification of specific aspects of the most relevant works
This work is delimited in the following criteria: (i) articles published in journals and conferences, (ii) stages of Bibliographic Portfolio (BP) selection and bibliometric analysis of ProKnow-C, (iii) published papers with temporal delimitation (2008-2018), (iv) a few number of keywords used and only one search carried out in each database, (v) databases and search mechanisms of CAPES (Coordination of Improvement of Higher Education Personnel) Periodicals with free access to articles of the agrarian sciences; (vi) alignment of articles with the theme according to researchers’ perception
Summary
The need for technologies to optimize decision making processes in organizations is increasing, as large volumes of data are generated daily and the requirement for useful strategic information is growing (Shahid et al, 2016). In a similar context of other kind of organizations, rural producers, farmers and professionals in the agrarian sector need to subsidize their decisions, to optimize productivity or reduce the risks and uncertainties inherent in their activities. A concept that relates decision-making processes, large volumes of data, extraction of useful information and knowledge discovery is Business Intelligence (BI). The DW, together with ETL (Extraction, Transformation and Load) process, help in organizing, integrating and cleaning the data, as well as facilitating the use of data visualization tools and knowledge extraction, such as data mining tools, online analytical processing (OLAP), reporting, ad-hoc queries, among others. DW is widely used in different sectors, such as: government, business, financial, health, industry, education, agribusiness, among others (Rai, Dubey, Chaturvedi, & Malhotra, 2008; Shahid et al, 2016)
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