A novel database management logic designed for some important production stages of farm to table

Doctoral Thesis / Dissertation from the year 2016 in the subject Environmental Sciences, grade: 5, National Autonomous University of Nicaragua, language: English, abstract: The primary goal of the present dissertation is to introduce a self-developed data storage logic called Joker Tao (JT), which provides the opportunity to store and manage each input data in one (physical) data table while the data storage concept is structured. JT allows any data whether entity, attribute, data connection or formula, to be stored and managed under just one physical data table. In the JT logic based databases, the entity and the attribute are used interchangeably, so users can expand the database with new attributes either during or after the development process. With JT logic, data storage using one physical data table is ensured in SQL database system for the storage and management of long-term scientific information. I examined the effects and importance of JT during some production stages of farm to table. Namely, the interoperability between soil and GHG production data from different data storage structures, data relating to food-stock records, billing and payroll in the baking industry are examined in this dissertation. With regard to development, the results show that in case of 1000 records or more (positive results) significantly faster queries could be realized with JT model usage in cloud compared to relational databases (in Oracle APEX environment). The results show that from 10000 records the relational model generates slow (more than 1 second) queries in a cloud-based environment while JT can remain within a one second time frame. Farm-to-table refers to the stages of food production and is associated with sustainable agriculture. Studies on climate change and greenhouse gas emissions linked to soil cultivation require large amounts of data. These data are mainly inducted from laboratory measuring devices or from various systems including geographical information and other specific systems, which operate with data from different data storage structures. Operating with data from different data storage structures is a challenge for companies and research institutes. Modern database management systems fall into two broad classes: RDBMS and NoSQL. The interoperability between SQL and NoSQL often garners considerable attention in the world of databases, with many solutions to have been proposed to solve this issue. There are some initiations in the literature about converting systems. I focused on creating a data storage logic, which increases the performance of NoSQL data storage concept-based RDBMS database management system.

Auteur

Dr. Bence Mátyás, Ph.D currently works at the Universidad Politécnica Salesiana, Ecuador as Principal Professor. Bence does researches in natural sciences and in innovation (mainly Startup mentoring in higher education).