I tried to reproduce the database that Stephen Ramsay described in his article. In the case of retrieving and storing data I’ve learned that in the system the organization of it is
“Complicated by the need for systems which facilitate interaction with multiple end users, provide platform-independent representations of data, and allow for dynamic insertion and deletion of information.”
A part of database design is to categorize most efficiently all the information in the database subtracting the maximum amount of repetition and “useless” data. Also in a database that has a lot of data stored a good design includes the ability to recall and store data very effectively without the need to repeat any values. This includes good categorization.
The Relational Model
The relational model attempts to factor out all the redundancies of the data by finding all the relationships between the datasets. The relational model also makes it easy to categorize large groups of data. This model also makes it easy to manipulate and search in large groups of data. This model keeps information organized very well by placing sets of data points under a main category then you can have sub-categories underneath the main headers. Many to one and one to many is a technique that makes it easy to find specific points of data in a large data set by only using a specific set of definitions.
One of the main cons of this model is that through the method of categorizing the large sets of data, personal opinion and bias is inputed into this “objective” database. By inputting this data and categorizing it, you will eliminate the factor of the neutrality of the pieces of information.
“Any time we break information down and classify it into categories, we are imposing our human world view and experiences on the information, whether consciously or not. “
This process is unavoidable, but there is not way around it, the best way of dealing with it is to input metadata in the data itself to explain the thought behind each step of the process.
One of the great things about flat databases is that it is extremely to get started with it. Setting up an Excel sheet is one of the easiest things ever. It is very easy to set up flat databases for small groups of data. It’s very familiar to get knowledgeable about how to use flat databases, as it is a simple process of inputting the data into the database.
The cons of flat databases is that it isn’t powerful enough to handle big datasets. And this is a common theme of flat databases, is that it doesn’t have enough power to compute large datasets, it also doesn’t have enough “power” to categorize everything.