A novel data storage logic in the cloud

Databases which store and manage long-term scientific information related to life science are used to store huge amount of quantitative attributes. Introduction of a new entity attribute requires modification of the existing data tables and the programs that use these data tables. The solution is increasing the virtual data tables while the number of screens remains the same. The main objective of the present study was to introduce a logic called Joker Tao (JT) which provides universal data storage for cloud-based databases. It means all types of input data can be interpreted as an entity and attribute at the same time, in the same data table.

Databases which store and manage long-term scientific information related to life science are used to store huge amount of quantitative attributes. This is specially true for medical databases 1,2 . One major downside of these data is that information on multiple occurrences of an illness in the same individual cannot be connected 1,3,4 . Modern database management systems fall into two broad classes: Relational Database Management System (RDBMS) and Not Only Structured Query Language (NoSQL) 5, 6 . The primary goal of this paper is to introduce a novel data storage logic which provides an opportunity to store and manage each input data in one (physical) data table while the data storage concept is structured. JT can be defined as a NoSQL engine on an SQL platform that can serve data from different data storage concepts without several conversions.

Methods
The technical environment is Oracle Application Express (Apex) 5.0 cloud-based technology. Workstation: OS (which is indifferent) + internet browser (Chrome). The Joker Tao logic (www.jokertao.com) can be applied in any RDBMS system (e.g. www.taodb.hu). Specification of the physical data table structure was determined with -ID (num) as the identifier of the entity, which identifies the entity between the data tables (not only in the given data The codes which are stored in the Attribute column are also defined, sooner or later, in the ID column. At that time the attribute becomes an entity. In every case, the subjectivity determines the depth of entity-attribute definition in the physical data table. Firstly, we demonstrate a simplified relational database model ( Figure 1).

Amendments from Version 1
We added a short video about the differences between relational database model and JT logic based databases in the Method chapter. Firstly, we introduce a relational data table and a relational database. Following this, we demonstrate the JT data storage structure by the same data which were used sooner in the presented relational database.
We changed the previous tables in Method chapter to new concrete figures in the interest of facilitating reader's validation and transparency in Method chapter. We expanded the text below figures in Method to describe the relationships between the virtual data tables used in JT logic based databases. In Results, we added a brief mathematical description about JT database model using known variables from descriptions of relational databases. We updated the one physical data table in Results according to the new information in the video. Following this, the presented data tables have been modified step by step. At the end of these steps, each data from the presented database will be stored in one physical data table using JT logic. The first step is the technical data storage. In Figure 2, basic relationships will be stored which help to describe the names of attributes (columns), type of relationships (belonging to the structure) and virtual data tables (belonging to the virtual data table).
In the second step, the records witch form virtual records are displayed ( Figure 3). The physical records with the same ID values mean a virtual record (entity) in the JT logic based databases. These identifiers can be any natural number that has not already been used in the ID column.
In the third step, records witch form new attributes are also displayed ( Figure 4). The values of these identifiers can be any natural number that has not already been used in the Attribute column.
Each attributes are identified in the Attribute column. In this case the following contexts can be read out related to the entity identified with 1001 ID value: -The value of the "belonging to the virtual data table" attribute (code 2) is Personal data table (code 31); -First name (code 32) is Richard; -Second name (code 33) is Jones; -Date of birth (code 33) is 01/02/1963; -Social security number (code 34) is 33325333; -Nationality (code 25) is American. The codes (namely 2,31,32,33,34,35) have to be stored sooner or later in ID column. At that time these attributes become entities and are defined by other attributes (eg. the "name" of the entity identified with 82 ID value is Personal insurance ID; the attribute called "name" was defined earlier in ID column see Figure 2 and now it is applied in the attribute column as an entity attribute).
In the fourth step, the attributes are assigned to each virtual data table using a previously introduced attribute called "belonging to the virtual data table" ( Figure 5).   The following context can be read out: The entities identified with 1001 and 1002 ID values belong to the same virtual data

Results
The resulting table structure is called JT structure ( Figure 6). The result from automatic conversion is a physical data table which uses 6 columns. In cloud, Oracle Apex automatically add row IDs and we introduced "belonging to the virtual data table" attribute instead of Group IDs. In cloud we prefer to use only 4 columns to store each data in a database.
The JT logic-based databases can be defined using primitive relation scheme known as a three-tuple according to Paredaens (1989) 8 concept: where ω is a finite set of attributes, in our case, it is the set of entities from the ATTRIBUTES virtual data table.
δ is a finite set of entities, in our case, it is a set of virtual records.
dom : ω → δ is a function that associates each attribute to an entity; it can be interpreted as a predefined set of attributes called "1:N registry hive". This function is used to maintain the entities in the virtual data tables.

A relation scheme (or briefly a relation) is a three-tuple RS=(PRS,M,SC)
where PRS is a primitive relation scheme; M is the meaning of the relation. This is an informal component of the definition, since it refers to the real world and since we will describe it using a natural language. SC is a set of relation constraints. From the JT physical data table, the following definitions can be read out: • Virtual record is set of the physical records which have the same ID value.
• Virtual data table is set of the virtual records which have the same value of the "belonging to the virtual data table" attribute.

Thesis:
In the JT structure, each attribute needs only one index for indexing in the database.

Proof using mathematical induction:
It is obvious the statement is true for the case of one record stored in a data table (according to the RDBMS structure where the developers use more indexes to indexing more attributes). In this case the data table appears as shown in Figure 7.
Index= attribute (num) + value (varchar 2) In view of entity, an ID (numerical) index is also used in JT logic-based systems. This ID does not depend (no transitive dependency) on any attribute. Thus, the entities of the virtual data tables meet the criteria of the third normal form (Figure 8).
The modes of the expansion of a data table are: -input new entity ( Figure 9); -input new attribute ( Figure 10); -input new virtual data table (Figure 11).
The indexing is correct in case of n+1 record expansion also. With JT logic the user is able to use only one physical data table to define each virtual data table in a database. Therefore, since only one   index is required to index each attribute, the statement of the thesis is true in every case of the JT logic-based data table according to the principle of mathematical induction below. Thesis: For n=1 ergo; 1 + 2 + .. + n = n * (n + 1)/2 substituting one into the equation we get: result of the operation is 1=1, that is, the induction base is true.

Discussion and conclusions
Using the developed database management logic, each attribute needs only one index for indexing in the database. JT allows any data whether entity, attribute, data connection or formula, to be stored and managed even under 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 after or during the development process. With JT logic, one physical data storage is ensured in SQL database systems for the storage and management of long term scientific information.

Competing interests
No competing interests were disclosed.

Grant information
The first version of JT is a Hungarian product which was developed in 2008 (R. Work demonstrated in the paper is good and well explained. Complexity of work is not mentioned (algorithmic complexity) but this is not necessary as we already have high speed processors and time complexity may not matter much Some more references should have been added but not mandatory as . number of references are sufficient.
I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
No competing interests were disclosed. In this paper authors introduce a new logic called Joker Tao (JT) which provides universal data storage for cloud-based databases. However, the paper is very poorly written. Firstly, the proposed logic is not presented detailed enough for the reader to understand and validate the method. Authors should research how relational model is presented and based on rigorous relational calculus and algebra. Based on this research, this paper should be rewritten based on rigorous mathematical foundation and give clear examples. Secondly, one table based example is far from convincing and provided Java-program is unnecessary. Length of the paper should be greatly increased to contain detailed description of JT method and give examples. Lastly, presentation is so poor that is not even clear how queries to resulting JT structure can be executed. To be honest, currently paper looks more like computer generated rubbish than a real scientific paper.
I have read this submission. I believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.
No competing interests were disclosed. Competing Interests: