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Using LLM Artificial Intelligence Systems as Complex SQL Programming Assistants
(2024-01-01)
Pornphol, Putsadee
;
Chittayasothorn, Suphamit
Learning database programming such as SQL programming is a challenging task when the queries become more complex. SQL is a declarative language based on relational calculus which describes the definition of the query results instead of describing the procedure or steps used to obtain the query result. Tutorial sessions using tutorial assistances are generally required to support the learning of advanced part of the language. Recently generative AI systems demonstrated question answering capabilities including programming codes generation. This paper verifies the SQL code generating capabilities of four generative AI systems: Bing, Bard, ChatGPT, and Copilot and their suitability as SQL programming assistants.
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Verification of Relational Database Languages Codes Generated by ChatGPT
(2023-10-27)
Pornphol, Putsadee
;
Chittayasothorn, Suphamit
The potential of using large language model artificial intelligence systems to generate program codes for application development is significant. Database codes in SQL (Structured Query Language), which is the standard relational database language, can be generated by such systems. Generative AI systems know database languages syntax through their training data and the text patterns from various sources that include SQL queries and related text. Thus, the generated codes may not be perfect and need verification before usage. This paper verifies the relational completeness of the SQL codes generated by ChatGPT, one of the most widely used large language model systems. Relational algebra operators are used for the relational complete verification. An equivalent relational calculus statement is generated for each SQL and relational algebra statement. The results confirmed that ChatGPT has the ability to generate relational complete SQL, relational algebra, and relational calculus codes.
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Relational Database Principles Consideration for SQL Programming
(2023-01-01)
Pornphol, Putsadee
;
Chittayasothorn, Suphamit
In practice, SQL programmers often encounter queries which are hard to formulate using SQL statements. Many of them do not know if the queries are too hard beyond their capabilities, or they are the limitations of the language. Many of these programmers only know that SQL statements work on database tables and are not aware of relational database and the relational complete language principle. This paper presents the common problems that SQL programmers encounter and refers to the relevant relational database principles as solutions.
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A survey on spatial, temporal, and spatio-temporal database research and an original example of relevant applications using sql ecosystem and deep learning
(2020-01-01)
Jitkajornwanich, Kulsawasd
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Pant, Neelabh
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Fouladgar, Mohammadhani
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Elmasri, Ramez
Spatio-temporal data serves as a foundation for most location-based applications nowadays. To handle spatio-temporal data, an appropriate methodology needs to be properly followed, in which space and time dimensions of data must be taken into account ‘altogether’ –unlike spatial (or temporal) data management tools which consider space (or time) separately and assumes no dependency on one another. In this paper, we conducted a survey on spatial, temporal, and spatio-temporal database research. Additionally, to use an original example to illustrate how today’s technologies can be used to handle spatio-temporal data and applications, we categorize the current technologies into two groups: (1) traditional, mainstay tools (e.g. SQL ecosystem) and (2) emerging, data-intensive tools (e.g. deep learning). Specifically, in the first group, we use our spatio-temporal application based on SQL system, ‘hydrological rainstorm analysis’, as an original example showing how analysis and mining tasks can be performed on the conceptual storm stored in a spatio-temporal RDB. In the second group, we use our spatio-temporal application based on deep learning, ‘users’ future locations prediction based on historical trajectory GPS data using hyper optimized ANNs and LSTMs’, as an original example showing how deep learning models can be applied to spatio-temporal data.
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RSQLG: The Reverse SQL Question Generation Algorithm
(2019-05-14)
Julavanich, Thanakrit
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Nalintippayawong, Srinual
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Atchariyachanvanich, Kanokwan
Nowadays, the people who learn and teach SQL commands require to get a hand-on practice with real environment to make the learning effective. The creating SQL exercise is a time-consuming task for instructors. As a result, students might not get enough questions to meet their demands. The reverse SQL question generation algorithm (RSQLG) is developed to solve this problem. The RSQLG has ability to automatically generate SQL exercise for students. The RSQLG can reverse the manual question creation process which starts from creating question to start creating query answer first instead. The RSQLG considers the existing data and database structure by using various constraints. The instructors also can specify the language, format and explanation of the questions. The RSQLG supports DML commands-SELECT, INSERT, UPDATE and DELETE-and support data retrieval from multiple tables which perform by JOIN and subquery operations. The algorithm has ability to generate bulk questions with less effort. The instructor is not required to writing requirements and validating the queries. The RSQLG can be implemented in e-learning to enhance sustainable practices and improve learning outcome for students.
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Proving completeness of open GIS SQL spatial relationships and operations
(2017-10-19)
Jitkajornwanich, Kulsawasd
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Elmasri, Ramez
OpenGIS SQL is a standard for incorporating GIS and spatial concepts into SQL. Two types of methods are part of OpenGIS SQL: (1) boolean methods for topological relationships among spatial objects, and (2) methods for specifying spatial operations. We prove that the complete set of 2-dimensional spatial relationships defined by Egenhofer can all be specified using OpenGIS SQL operations and relationships. Our proof shows how each spatial relationship can be specified using OpenGIS SQL and thus provides users with the means of determining how each spatial relationship can be written in SQL. This provides users with enhanced usability and flexibility by providing the specific OpenGIS conditions for every possible 2- dimensional spatial relationship.
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A temporal object relational SQL language with attribute timestamping in a temporal transparency environment
(2008-12-01)
Chau, Vo Thi Ngoc
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Chittayasothorn, Suphamit
In this paper, a temporal object relational SQL language is proposed. It is intended to facilitate the non-procedural data definitions, queries, and modifications of temporal databases with attribute timestamping in a temporal transparency environment. With attribute timestamping, temporal data of any type is naturally handled. With temporal logic, users can issue temporal queries in an intuitively expressive manner. The language provides valid time support for upward compatibility, temporal upward compatibility, sequenced and non-sequenced variants to serve both non-temporal and temporal users. Using object relational technology, the implementation is made for embedded and interactive SQL modes. All existing interfaces of an ORDBMS are inherited with temporal transparency. © 2008 Elsevier B.V. All rights reserved.
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A temporal compatible object relational database system
(2007-01-01)
Vo, Thi Ngoc Chau
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Chittayasothorn, Suphamit
Time-varying data management has been much taken into consideration with either the tuple(object)timestamping or attribute-timestamping scheme. The first scheme is simpler with first normal form relations; but the second is more expressive and natural with unnormalized tables. However, no existing database management system has supported the handling of attribute timestamping on such unnormalized tables. In addition, even though planned for a long time, standards SQL have not yet been published with any temporal semantics. Moreover, there exists no available environment where both temporal and nontemporal database users work together with different awareness of temporal aspects of a database. Therefore, to facilitate the temporal data management according to users' needs and their own ability in data manipulations, a database system is figured out with several types of database users in this paper. It is a temporal object relational database system following the attribute timestamping scheme, allowing upward compatibility with SQL:2003 and temporal upward compatibility. The implementation is carried out on Oracle 10g without the introduction of any new temporal clauses into SQL:2003. As a result, both temporal and nontemporal database users do not have to make any effort to use this system © 2007 IEEE.

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