The Gap Between Theory And Practice In Database Schema Essay.

Research existing theories for databases and data management. Identify common theories, and explain if these theories have evolved over time. Then, research how these technologies are used in practice.

Analysis of the Gap Between Theory and Practice

In database and data management, a database schema is a design or layout that defines the structure of the database. The schema gives the framework for the contents and operations of the database. A database schema theory is a database theory that organizes knowledge or information in the database into units called schemata (Gorskis, 2016). Basically, a schema is a conceptual system for analyzing and understanding generalized knowledge description. Database schema provides knowledge descriptions that explains how knowledge is used or represented. The table is the basic unit of a database and consists of rows containing related information. The example better describes what a database schema is. The example lists the field names in the headers and the content in the rows. The example represents a personal phonebook. The most essential information in a phonebook is a phone number and name and thus the information is represented in a table containing two fields: phone number and name.

Table 1: phonebook table

The database schema represents the logical setup of all the sections of a relational database. it can exist as a set of formulas or as visual representation. The formulas are represented in a data definition language and as section of data dictionary, the schema shows how the entities form the database associate with each other including stored procedures, views, tables, among others. A database schema is created by database designers to help programmers developing applications and programs that will interact with the database (Wang, Zhou & Wang, 2014). Data modelling is the process of creating database schemas. There exist two types of database schema: a physical database schema and a logical database schema. A database schema shows which relations and tables make up the database.

The gap between what we learn theoretically concerning database design and what happens in the industry is increasing every year. Database schema theory provides the underlying concepts of the relational database model. The theory provides simple examples such as university administration database with a few tables so as to allow the learners get the idea of the database schema theory and focus on the database design principles (Hamer, 2017). Nevertheless, when the leaners get out there to the industry, they find it scarier because the industry has big databases with hundreds of tables with dreadful practices of naming the tables with inadequate documentation making it difficult for the learners to understand why and how the particular design was selected. Industry database design also have adopted several solutions to solve problems that were not mentioned by the theory which some may be good while others are not. Some of the industry concepts not mentioned by the theory include how to handle events, transactions, and concepts that evolve overtime. The newcomers into the industry get immersed in mystifying folklore that exist among application developers and database designers concerning what are the necessary and good practices with respect to database schema theory (Peng, 2008). This document seeks to analyze the gap between theory and practice in order to better understand the benefits and challenges associated with the theory.

Root Cause of the Gap Between Theory and Practice

There is a famous saying that says ‘theory without practice is sterile and practice without theory is blind’. There should be a perceived association between practice and theory in database design and data management. There is need to have database theories to explain the various concepts of data and database design, their relationships, structure, and frameworks (Kumar, PriyaVaijayanthi & Prakasan, 2009). It should include rules, heuristics, and theorems and not just a collection of facts that can be applied in several situations over a period of time.

The challenge the new database professionals face when they get to the real world from school is how to apply what they have learnt is solving industry problems. The modern databases began as a relational database theory and has evolved to be ubiquitous in nature. Database schema theory provides the users with the ability to define objects using default schema forming the first schema used to solve unqualified object names while being searched. Database schemas are important in the industry practices to facilitate transfer of ownership of schema-owned objects. This requires the use of alter authorization command. Applying the theory learnt in class is still a major challenge to many database professionals thus increasing the gap between theory and practice. The theory also state that it is possible to move objects within the schemas by using the alter scheme. Applying these concepts in the real-world is quite challenging. However, the schema concept has brought numerous advantages to the industry because objects can now be independently manipulated. Logical entities can be managed in one physical database because schemas provide the platform to simplify administration of backup and restore, security, and database management facilitating grouping of entities and objects logically together (McVee, Dunsmore & Gavelek, 2015). A database administrator can regulate access to critical objects because database schema provides object protection. This concept is simple when explained theoretically but bring about numerous complexities in the real-world. This widens the gap between the theory and practice in database schema theory.

The major cause of the gap between theory and practice in database schema theory is the fact that when in school, the students are taught using the simplest form of examples and scenarios. As such, when the student gets to the industry, they find bigger challenges that their little concept regarding database design cannot apply (Jainae & Suwannasart, 2014). The teachers may have used simple examples like the example given above of representing a phonebook but when they get to the industry it is difficult to relate because the industries have more complex challenges that requires numerous tables, entities, transaction algorithm among others. Such scenarios were not covered by the theory because it only provides the concept of what could be done. This widens the gap between theory and practice and this gap will have a greater impact in solving industry problems because of the insufficient skills and knowledge of how to apply the theory into practice (Curino, Moon & Zaniolo, 2008).

The gap between theory and practice in database schema theory have more negative impact both to the industry and the database professionals. This is because teaching the students (database professionals) using simple examples and scenarios will limit their ability to solve complex real-world examples (Biskup, 2011). This will translate to the industry having more problems to solve with few experts to solve them. More run time errors will be experienced because of the insufficient skills and expertise in writing database schema algorithms to solve complex problems and transactions. For instance, while in school students were taught how to use database schemas in designing a university administration database, which is the simplest example, but when they get out to the industry, they find more complex problems such as designing a database for an ecommerce website that has numerous transactions and different user levels. Relating what they learnt in class to the real world will be such a difficult task for them (Wang & Chen, 2011).

Conclusion

Database schema theory is very important in solving industry problems because it provides knowledge descriptions that explains how knowledge is used or represented. Additionally, the schema concept has brought numerous advantages to the industry because objects can now be independently manipulated. Logical entities can be managed in one physical database because schemas provide the platform to simplify administration of backup and restore, security, and database management facilitating grouping of entities and objects logically together. A database administrator can regulate access to critical objects because database schema provides object protection

However, the industry database design also has adopted several solutions to solve problems that were not mentioned by the theory which some may be good while others are not. Some of the industry concepts not mentioned by the theory include how to handle events, transactions, and concepts that evolve overtime. This has resulted to increased gap between theory and practice.

References

Biskup, J. (2011). Database schema design theory: Achievements and challenges. Lecture Notes In Computer Science, 14-44. doi: 10.1007/3-540-60584-3_22

Curino, C., Moon, H., & Zaniolo, C. (2008). Graceful database schema evolution. Proceedings Of The VLDB Endowment, 1(1), 761-772. doi: 10.14778/1453856.1453939

Gorskis, H. (2016). Improved Database Schema Development for OWL2. Information Technology And Management Science, 19(1). doi: 10.1515/itms-2016-0016

Hamer, M. (2017). Relational database practices. London: Malcolm Hamer.

Jainae, J., & Suwannasart, J. (2014). A Framework for Test Case Impact Analysis of Database Schema Changes Using Use Cases. International Journal Of Engineering And Technology, 6(3), 186-189. doi: 10.7763/ijet.2014.v6.693

Kumar, R., PriyaVaijayanthi, R., & Prakasan, K. (2009). Towards converting a production database schema to XML schema. International Journal Of Computer Aided Engineering And Technology, 1(3), 338. doi: 10.1504/ijcaet.2009.026978

McVee, M., Dunsmore, K., & Gavelek, J. (2015). Schema Theory Revisited. Review Of Educational Research, 75(4), 531-566. doi: 10.3102/00346543075004531

Peng, Z. (2008). S-CBR: Presenting Results of Keyword Search over Databases Based on Database Schema. Journal Of Software, 19(2), 323-337. doi: 10.3724/sp.j.1001.2008.00323

Wang, H., & Chen, M. (2011). Efficient and self-adaptive storage schema in flash-based database system. Journal Of Computer Applications, 31(5), 1400-1403. doi: 10.3724/sp.j.1087.2011.01400

Wang, X., Zhou, X., & Wang, S. (2014). Hybrid Schema Summarization Method of Large Scale Database. Chinese Journal Of Computers, 36(8), 1616-1625. doi: 10.3724/sp.j.1016.2013.01616