SQL comprehension and synthesis

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dc.contributor.authorObaido, George Rabeshi
dc.date.accessioned2021-07-02T08:34:11Z
dc.date.available2021-07-02T08:34:11Z
dc.date.issued2020
dc.descriptionA thesis submitted to the Faculty of Science, in fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science, 2020en_ZA
dc.description.abstractStructured Query Language (SQL) remains the standard language used in Relational Database Management Systems (RDBMSs), and has found applications in healthcare (patient registries), businesses (inventories, trend analysis), military, education, etc. Although SQL statements are English-like, the process of writing SQL queries is often problematic for non-technical end-users in the industry. Similarly, formulating and comprehending written queries can be confusing, especially for undergraduate students. One of the pivotal reasons given for these difficulties lies with the simple syntax of SQL, which is often misleading and hard to understand. An ideal solution is to present these two audiences: undergraduate students and non-technical end-users with learning and practice tools. These tools are mostly electronic, and can be used to aid their understanding, as well as enable them write correct SQL queries. This work proposes a new approach aimed at understanding and writing correct SQL queries using principles from Formal Language and Automata Theory. We present algorithms based on: regular expressions for the recognition of simple query constructs, context-free grammars for the recognition of nested queries and a jumping finite automaton for the synthesis of SQL queries from natural language descriptions. As proof of concept, these algorithms were further implemented into interactive software tools aimed at improving SQL comprehension. Evaluation of these tools showed that the majority of participants agreed that the tools were intuitive and aided their understanding of SQL queries. These tools should, therefore, find applications in aiding SQL comprehension at higher learning institutions and assist in the writing of correct queries in data-centered industriesen_ZA
dc.description.librarianCK2021en_ZA
dc.facultyFaculty of Scienceen_ZA
dc.identifier.urihttps://hdl.handle.net/10539/31408
dc.language.isoenen_ZA
dc.phd.titlePhDen_ZA
dc.schoolSchool of Computer Science and Applied Mathematicsen_ZA
dc.titleSQL comprehension and synthesisen_ZA
dc.typeThesisen_ZA
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