Data Schema of Relational Table Importing to Graph Database - Dimensionality

Definition

Relational Table

The Table is the fundamental components inside a Relational Database which stores data in tabular format using Column and Row to coordinate a specific Value (i.e. the Cell). Form and Table (i.e. Tabular format) are everywhere in your daily life.

Data Schema in Relational Database

A data schema in a Relational Database is a blueprint or structure that defines how data is organized, stored, and related within the database. It describes the database’s logical design and covering when and what to create Table, Columns , Index, Contraints, Relationship and Data Type inside a Table in a Relational Database

Graph Database

A graph database is a type of database that uses graph structures to represent and store data. Instead of organizing data into rows and columns like relational databases, graph databases focus on relationships and connections between data points.

Objective of Importing Data From Relational Table to Graph Database

While Graph Database can be acting as the Enterprise Knowledge Base which can speed up the learning curve of both staff and client , how to import the data into the Graph Database is a challenge.

As data with Tabular format is dominant in the world, it is inevitable for us to import data from a Relational Database to a Graph Database. There are few ways that we can import the data into the graph database:

Directly coding by Cypher Cypher is a Graph Query Language , as if the SQL in relational database
Import both the data schema (i.e. the metadata. E.g. datatype, column constraint) and the business data (i.e. a normal record inside a table in a relational database) directly from the relational database)

While graph databases are not common in the general public, it seems that both of the methods required some kind of expertise in order to get the job done. Besides, the methods above are good for batch importing. If you want to import the data in piecemeal, it is not handy.

It is necessary to have a normal input Form (just like the Form you can see every day in your life) which has zero learning curve for the user to import the data manually into the graph database.

Data Types

The wording “Data Type” has a different meaning. it can be referring to the description of the data format , e.g. a column in a Form which can only input a value of Integer , decimal ,text, or a autonumber.

Instead, Data Type has another meaning which is used to classify data as Business Data, Metadata and Model Data. For details about the difference between the 3 , it is strongly recommended you to read the article bGraph Architecture – Model Data beforehand.

The focus of this article is for catering the latter.

Interpreting Relational and Graph Database in a Dimensional Perspective

Before we dive into the problem pattern and its paired solution on importing the data from a relational table into a graph database, the concept of interpreting data in terms of dimension is crucial for us to understand what and why we are solving the problem in the way we did.

What is Dimension

By definition, In physics, a Dimension is a space that can be measured and extended. For example:

Dimension (x-D)	Example of Unit	Example in Reality
0-D	N/A	a point (or a Spot)
1-D	cm	a Line
2-D	cm²	a Plane (or an Area)
3-D	cm³	a Volume
4-D	Hour	Time
5-D	??	??

Relationship Between Dimension and Vector

A practical purpose of the concept “Dimension” is to coordinate something.

For example, can you tell me where the alphabet “A” is in the Line below?

______A___

While the “A” is definitely not in the middle, it is not in the starting or ending point of the line either. You can say it is right-stewed , but cannot exactly tell.

How about now?

123456A89

Now you can confidently say that the “A” is located in the position “7” of this 9-unit-long Line.

The Line is regarded as “1 dimension” because you can measure the length of the line (1-9) in width (and only width) , but not in height or in depth or in time.

Dimension	Co-ordinate of Data Point Example
0-D	N/A
1-D	{1}
2-D	{1,2}
3-D	{1,2,8}
4-D	{1,2,8,10}

So it is quite easy to understand that the amount (or you can say the “digit”) of data points is exactly the same as the degree of dimension. Whenever you want to coordinate an additional dimension, simply add an additional data point (i.e. the digit) into the array.

In mathematics , we call this long number and extensible array (e.g. {1,2,8,10}) as Vector, which is the concept under the mathematics branch of Linear Algebra.

While as a human being we cannot imagine a material object in a 5-D physical world, there is no limitation on how many dimensions you can add in mathematical world. In fact , you can add 100 , 1,000 or 10,000 data points into the Vector if needed, as long as you can think of the use case (e.g. to coordinate something) and provided that you have sufficient computing power to do the vector calculation and operation.

That’s the reason why even the Graph Theory coined almost 300 years before, and computer existed almost 90 years, we still didn’t hear of Graph Database until this 1 or 2 decades (may be because you were not even born!) due to the limitation of the computational power and infrastructure.

Relationship Between Dimension and Database

0-Dimension in Database

As mentioned before, the main purpose of a Vector is to coordinate a data point. In fact , coordinating something is the initial step of “Searching”.

What is the benchmark of whether we can successfully coordinate something mostly depends on whether the coordination can refer to one and only one outcome (i.e. singleton)

Allow me to start with an example based on 2023 Barbie film.

Imagine in a country ,Barbie Land, Barbie¹ is the one and only one citizen. To record Barbie as the citizen of this country (most likely Barbie will be the one who carries out this recording task!), Barbie can simply record like below:

Barbie

That is!

While there is one and only one citizen in this country, every time anyone talk about “Barbie” in this country, the word “Barbie” can uniquely identity the material substance of the person who is referring to , which means it can perfectly and good enough to “coordinate” the one and only one material substance citizen “Barbie” in Barbie Land.

You can regard this “Cell” as 0-Dimension because there is one and only one value, and there is no any direction for you to extend horizontally or vertically. (Remember the definition of Dimension?)

0-Dimension : In terms of Data Structure, only one and only one Cell which stored the Value.

1-Dimension

When someone outside Barbie describes Barbie, he/she may say that Barbie is 29 cm height , the citizen of Barbie Land. If height = 29cm , citizenship = Barbie Land, then X = Barbie , what is the “X”?

Obviously the X = Name.

In order to faciliate others to describe the material substance object (i.e. Barbie herself), we can add an additional Cell on top of the Cell Barbie as below.

Name
Barbie

By applying the definition of Dimension, you can realize that the Cell (i.e. 0-Dimension) had been added an additional Cell Name and turn it to a vertical line (i.e. a Column). Therefore, the data structure had been transformed from 0-Dimension to 1-Dimension.

From Now on , you can call this data structure as Column.

1-Dimension : In terms of Data Structure, when a “Column Name” is appended on top of the 0-Dimension Cell, it can be regarded as 1-Dimension.

2-Dimension

One day, when Barbie experienced the imprefection of herself , she realized that she is in fact only a stereotype. She therefore gave herself a Last Name as below:

First Name	Last Name
Barbie	Stereotypical

When a column Last Name is added, the word “Name” is not enough to differentiate between the two. And to uniquely identify the two , the “First” and “Last” are added in front of the “Name”.

You may find the pattern that whenever you want to uniquely identify some objects, simply attach some attributes (e.g. Last Name) to that object, in order to let the Name refers to one and only one instance. (This is the definition of the word “Definition”!)

Back to our database use case, as now an additional Column is attached to Barbie, you can see the data structure is in fact extended from a Column (1-Dimension) to a Plane (2-Dimension). If you remember the vector example, you can write it the vector as {Barbie, Stereotypical} .

From now on, you can call this Plane as Table.

2-Dimension : In terms of data structure, whenever there are 2 columns with a Column Name and 1 Record, it can be regarded as 2-Dimension.

Adding a New Record in 2-Dimension

As time goes by, the population of the Barbie Land increased 100% from 1 person to 2 people , of which the newbie is also named Barbie.

First Name	Last Name
Barbie	Stereotypical
Barbie	Weird

Applying the same logic of the definition of Dimension, although an additional record , i.e. Barbie Weird, is appended to the list, the data structure is still in 2-Dimension as the new record by itself does not extend to any new direction. (it extended vertically which this direction already existed before.) That means no matter how many records you added, the Table is still in 2-Dimension.

Adding a New Column in 2-Dimension

Although logically anyone can classify 2 Barbies with different First Name and Last Name, in reality as the 2 Barbies will not seal their name in their forehead, there is no way to teach a person who never see them before to differentiate between the two.

Having discussed with this problem, in order to uniquely identify themselves visually, the 2 Barbies agreed to give a Hair Style describe to themslves as below:

In this case, a obversable character is needed to enrich the data structure, letting the information inside the data structure more close to the reality.

First Name	Last Name	Hair Style
Barbie	Stereotypical	Floating
Barbie	Weird	Quirky

Applying the same logic of the definition of Dimension, although an additional column , i.e. Hair Style, is appended to the Table, the data structure is still in 2-Dimension as the new Column by itself does not extend to any new direction. (it extended horizontally which this direction already existed before.) That means no matter how many Columns you added, the Tabel is still in 2-Dimension.

3-Dimension

It seems the 2-Dimension Table can describe the reality of the Barbie Land well until both Barbies want to create a sunglasses list which can help her to manage all your eyewear in their wardrobe.

After hours of effort , they created the sunglasses list as below:

As a new Data Table is created, here raised another question : How to uniquely identitiy the 2 Tables during communication?

Well, as you can think of, simply giving the name to each of the 2 Tables can solve the problem.

Sunglasses#	Sunglasses Style	Sunglasses Frame Color
S01	Cat-Eye Frame	Orange
S02	Aviator	Deep Blue
S03	Sporty	Silver

Therefore, Stereotypical Barbie name the 2 Tables as Citizenand Sunglassesrespectively.

Same as your life, the 2 barbies just open a can of worms. While both Citizenand Sunglasses by itself are 2-dimension objects (i.e. a Table), listing out 2 Tables , referring to the definition of Dimension, will create another new Dimension (2 Tables are definitely measurable and you can extend as many objects (i.e. Tables) as you want in the same direction).

And therefore, a 3-Dimension data structure is just born! Now you can call this 3-Dimension data structure as Database.

Testing The Theory From 1 to 3 Dimension

It’s time for us to take a deep breath before we dive into the 4-Dimension data world. Limited by the human brain’s structure, any dimension higher than 3 will be hard to visualise and project in our brain. Therefore we have to make sure we are acquainted with the 0-3 Dimension concepts well.

The next question is, how do we prove that our 0-3 Dimension data structure theory is correct and practical?

Let us back to the basics of why we have to build the data structure. This is the fundamental objective of the information system. The logic is as below:

Making decision will bring value in the real world
Decision Making Quality affects the value of Decision Making.
Accurate, timely and relevant Information will facilitate the Decision Making Quality.
Information is refined from how we analyze and process (e.g. egress, load and transfer) the Data in the Database.
The Data stored in the Database is classified as Descriptive Data and Inferential Data.
Descriptive Data is from Real World Observation.
Inferential Data is the product of both Descriptive Data and Intelligent (i.e. how we analyse the data, for example using logistic regression model to classify the Descriptive Data).

To streamline the deduction steps above, you can conclude that the purpose of the Information System is that the user of that Information System (i.e. the Database) can simply make the decision by observing the world, leaving all the processing and analysing steps to the Information System.

And hence, whether or not the database is competent or not depends on whether the data stored can reflect the reality in fidelity (i.e. is it descrptive enough) and uniquely identify the underlying objects found with the real world (i.e. co-ordinating) , while leaving the inferential duty to other system. (e.g. an AI facial recognition system)

It is straightforward that we can test the fidelity of the database by asking simple questions in below:

Q1: How many citizens are there in Barbie Land?

A1: Two.

By counting the number of records (i.e. Row) in the Table Citizen, we can easily figure out there are 2 records in the Table Citizen.

The 1-Dimension data structure (i.e. any of the Column inside the Table) perfectly performs the task on description the objects in reality, and hence it can be regarded as a competent database.

Q2: Please describe the citizen in Barbie Land whose last name initialized with “s” ?

A2: Steretypical Barbie is the citizen in Barbie Land who has a floating hair style.

By filtering the Last Name column in the Table Citizen , you can easily describe the object (i.e. the record) by finding out its First Name, Last Name and Hair Style.

This 2-Dimension data structure (i.e. Database) perfectly performs the task on description and co-ordinating the object in reality, and hence it can be regarded as a competent database.

Q3: What object types can be found in Baribe Land? and how many instance in each object?

A3: 2 Citizens and 3 Sunglasses can be found in Barbie Land.

By enumerating all the Tables inside the Database, we can easily answers the question Q3.

This 3-Dimension data structure (i.e. Database) perfectly performs the task on description and co-ordinating the object in reality, and hence it can be regarded as a competent database.

You can see that up to now , the existing 1 to 3-Dimension data structure perfectly answers the 3 questions above, until we start asking another type of questions : 4-Dimension question.

4-Dimension

Let’s start the 4-Dimension session with a question and you will realize the limitation of the 3-Dimension data structure.

Q4: What is the wearing habit of the Citizens in Weekday?

While we already have the Table Citizen and Sunglasses to record each individual citizen and sunglasses, these 2 Tables do not describe the Relationship between the 2.

If we tried to record the mix and match wearing observation in existing Tables, no matter which Table (Citizen or Sunglasses) , it will look like this:

Weekday	Sunglasses#	Sunglasses Style	Sunglasses Frame Color	Citizen
Monday	S01	Cat-Eye Frame	Orange	Stereotypical
Monday	S03	Sporty	Silver	Weird
Tuesday	S02	Aviator	Deep Blue	Weird
Tuesday	S03	Sporty	Silver	Stereotypical

The Table above sacrificed the fidelity the reality provided in answer of Q1 (i.e. How many citizens are there in Barbie Land?) as some of the citizen are duplicated in the records such that we cannot simply count the records to answer the question. (i.e. There are 4 records in total but in reality it got only 2 sunglasses and 2 citizens)

In order to solve the problem, instead of appending the additional columns into any of the existing Tables , it is a better way to create 2 additional tables – WeekDay Table

Weekday
Monday
Tuesday
Wednesday
Thursday
Friday

Together with the Mix and Match Table that we just created:

Weekday	Sunglasses#	Sunglasses Style	Sunglasses Frame Color	Citizen
Monday	S01	Cat-Eye Frame	Orange	Stereotypical
Monday	S03	Sporty	Silver	Weird
Tuesday	S02	Aviator	Deep Blue	Weird
Tuesday	S03	Sporty	Silver	Stereotypical

Now there are 4 Tables in Total inside the Database:

Citizen
Sunglasses
Weekday
Mix and Match

However, when you observe carefully, you can realize that in fact the terms Mix and Match has no records and does not exist in reality. Instead, the concept Mix and Match is a concept (i.e. a Relatoinship) rather than a material substance which can be observed.

If you still have no idea what i am talking about, let’s recall the table Citizen that we have created during the 2-Dimension session:

First Name	Last Name	Hair Style
Barbie	Stereotypical	Floating
Barbie	Weird	Quirky

Can you realize that you cannot exactly find any material substance of “Citizen” inside the Citizen Table? It is because the terms Citizen is a Class , while the 2 Barbies are records are the Instance. There is no material subtance for the term Citizen.

Same phenomenon happended in other Tables that you can only find the Instance instead of the Class inside the records in any tables.

If you think this logic make sense and apply to the table Mix and Match , you may now realize that the concept Mix and Match is a Class instead of an Instance which cannot be found inside the record of the Table Mix and Match.

As we just created a new Class Mix and Match to consolidate all the 3-dimesion Tables Citizen , Sunglasses and Weekend , that means another new Dimenion , 4-Dimension, had been created.

If we visualize all the relationships of all concepts mentioned throughout the paragraph via the Entity Relationship Diagram, it will become the diagram in below:

Barbie Land Mix And Match Entity Relationship Diagram

4-Dimension+

So, it is possible that i can build infinity number of Dimensions of the data structure inside the database? Yes, in theory you can. Whenever you cosolidate all the instance in the same Dimenion and form a list, you created a new Dimension.

As long as you understand how we use the dimensionality in the data structure to describe the real world, we can stop the example in 4-Dimension.

Conclusion

In this section we just introduced how we observe and describe the reality and fit them into a Relational Database via a dimensional way, as well as how we create a new dimension by gathering all instance in lower dimension to form a new class in the upper dimension.

In next article, we are going to address the problems that the Relational Database suffers, and how we fit the Relational Database into the Graph Database to compensate the problems suffered by Relational Database.

Footnotes

Please refer to the Barbie film 2023 in order understand why we use the name Barbie as an example. ↩︎

Data Schema of Relational Table Importing to Graph Database – Dimensionality