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.
Table of Contents
1. Introduction
1.1. Comparison of different database models
1.2. Rivarly between NoSQL and SQL
1.3. Knowledge-based systems development
1.4. Joker Tao
1.5. Comparison of JT and ontological modeling
1.6. Mathematical definitions of the relational database model and JT database model
1.6.1.Mathematical definition of the relational database model
1.6.2.Mathematical definition of the JT database model
1.7. State of the art
1.7.1. Relational systems
1.7.2. NoSQL systems
1.8. Objectives of my research and development
2. Methods
2.1. Research methods
2.2. The developed data storage and management methods
2.2.1.Four dimension of knowledge stored in data table
2.2.2.The developed JT algorithm exerting method
2.2.3.Virtual data table management
2.2.4.Indexing in JT
2.2.5.Converting data tables to one physical data table
2.3. Storing and processing the unknown, or from the data to the information
2.4. Technological environment
2.4.1.Cloud-based solutions
3. Results and discussion
3.1. Research results
3.2. Development results
3.3. JT database management
3.3.1.JT seed
3.3.2.ID usage
3.3.3.JT attribute
3.3.4.T_sequence and value
3.3.5.The dialectic of entity and attribute
3.3.6.JT Shell
3.3.7.Application Initialization (login) and access controls
3.3.8.Efficiency indicators
3.3.9.Development directions
3.3.10. Subset determination in JT logic
5. Conclusions
5.1. Conclusions of the research results
5.2. Conclusions of the development results
6. New and novel results
Research Objectives and Key Topics
The primary objective of this research is to introduce and develop a novel database management logic, the Joker Tao (JT), designed to address the challenges of managing heterogeneous data structures in agricultural and commercial applications. The research investigates how this logic enables the storage of various input data within a single physical data table, thereby enhancing system performance, simplifying data management, and eliminating the need for frequent database conversions.
- Universal data storage and management using a single physical data table.
- Interoperability between relational and NoSQL database management concepts.
- Enhancement of query performance in cloud-based environments.
- Reduction of human resource requirements for data management.
- Development of logic for handling unknown or dynamically evolving data structures.
Excerpt from the Book
1.4. Joker Tao
In this subchapter I introduce briefly the self-designed database management technology called Joker Tao (JT) which is based on a novel data model. Horizontal column expansion is not used, instead a customized code table was created which allows any data (which can be either an entity, an attribute, an integrity condition or a formula) to be stored and managed even with one physical data table. In this data model, the entity and the attribute are used interchangeably. Physical records with the same ID value form a virtual record. The set of the virtual records with same attribute value of the belonging to the data table mean a virtual data table. The inputted data is managed as entity and attribute at the same time. The categorization depth is determined by the actual task. One of the major problems in database management is indexing. If the developers index huge amount of attributes in a database then the increased number of the index tables may slow down the system or the database development. JT ensures that all attributes in a database can be indexed without using several indexes. In JT there is no normalization in the traditional sense, or no complex key usage. The first physical field (column) is used for the unique identification of virtual entities. Another field (Attribute) is used for the identification of physical records in a virtual entity. JT also handles relational data models differently; it allows records with the same ID values to identify a single entity. Similarly, non-relational models are also novel in JT; the inputted data are not stored in an unstructured concept, making it easier to manage a huge amount of data without creating several applications. When compared to NoSQL models the speed of the JT system lies in its use of vertical data expansion and the elimination of the sequential search approach, which slows queries. JT derives data from the relationship between entities and attributes stored in the system. In several cases the system can develop with the entry of new record. The records can arise both manually or can be triggered by events.
I present a solution which puts forth a whole new approach that eliminates the need for conversion and file compatibility problems by combining the different data storage concepts into a physical data storage level. JT then can defined as, a NoSQL engine on an SQL platform that can serve data from different data storage concepts without several conversions necessary. The greatest strength of the JT system is the fact that each data could be both an entity and an attribute at the same time.
Summary of Chapters
1. Introduction: Discusses the paradigm shift in agricultural management and the need for integrated information systems, establishing the motivation for a novel database model.
2. Methods: Outlines the research approach, including soil experiments and the technical specifications of the developed JT data storage and management methods.
3. Results and discussion: Presents empirical findings from soil carbon cycle experiments and evaluates the performance efficiency and practical implementation of the JT database management system.
5. Conclusions: Synthesizes the experimental results and development outcomes, confirming the efficiency and universal applicability of the JT model.
6. New and novel results: Lists the specific contributions of the research, highlighting the performance advantages and the unified data storage logic developed.
Keywords
Joker Tao, JT, Database Management, NoSQL, Relational Databases, RDBMS, Data Storage, Interoperability, Cloud Computing, Soil Science, Carbon Cycle, Software Engineering, Performance Optimization, Virtual Data Tables, Information Systems.
Frequently Asked Questions
What is the core purpose of this dissertation?
The dissertation introduces "Joker Tao" (JT), a novel, self-developed database management logic designed to store and manage diverse data types within a single physical table, improving efficiency and flexibility.
Which database management classes are discussed?
The work primarily focuses on the challenges and interoperability between the two broad classes of modern database management systems: Relational Database Management Systems (RDBMS/SQL) and NoSQL systems.
What is the primary research question?
The research explores how to create a data storage logic that allows for universal data management, enabling different applications to communicate automatically and handling evolving data structures without the need for complex schema modifications.
What scientific methods were employed?
The researcher conducted long-term fertilization experiments on soil carbon cycles while applying the developed JT framework in technical environments like Oracle APEX and custom JAVA applications to validate its efficiency.
What topics does the main part of the work cover?
The main part covers the theoretical foundations of different database models, the technical methodology behind the JT logic (including its four-dimensional knowledge storage), and performance comparisons between traditional models and the JT approach.
How is the work characterized by keywords?
The work is characterized by terms such as Joker Tao, database management, NoSQL, relational databases, data storage, interoperability, cloud computing, and performance optimization.
How does JT improve upon traditional database management in a corporate setting?
By using a single physical data table for all inputs, the JT system eliminates the need for complex conversions, simplifies the addition of new attributes, and has been shown to significantly reduce the human workload required for data management in companies.
How does the performance of JT compare to relational models?
The study demonstrates that when managing large datasets (e.g., 10,000 records or more) in a cloud environment, the JT model maintains query response times within one second, whereas relational models tend to slow down significantly.
- Quote paper
- Bence Mátyás (Author), 2016, A novel database management logic designed for some important production stages of farm to table, Munich, GRIN Verlag, https://www.grin.com/document/415404
