Thai QBE for Ad Hoc Query

  • Areerat Trongratsameethong Chiang Mai University
  • Phada Woodtikarn Chiang Mai University
Keywords: Thai QBE, Fixed Asset Database, Ad Hoc Query, Levenshtein Distance, Edit Distance Algorithm.


Data for answering business questions usually query from databases. Software applications are provided for users to query these data. However, queries provided by software programs are must be predefined through searching criteria specified in program user interfaces. The searching criteria are transformed to SQL statement and stored in a program. If users want data in different views or want more data, the searching criteria stored in a program must be modified or new programs must be created. This will spend more times and costs. Moreover, the ad hoc questions for real-time decision making require ad hoc query tools to generate ad hoc data and ad hoc reports. But users who want to use these tools must be trained or learned how to use them. This inspires us to develop an ad hoc query tool named Thai QBE to query ad hoc data. User interfaces of Thai QBE are designed to support Thai language and they are designed for easy to use. Users who use the Thai QBE do not require technical skills. They can use it without training. Concept used to design Thai QBE is derived from Query-by-Example (QBE) of Microsoft Access. Thai QBE prototype was developed as a web application. Users can use it for querying ad hoc data anytime and anywhere. There are two types of queries supported by Thai QBE: general query and statistical query. The Thai QBE prototype was implemented and tested with fixed asset system database. The experiments reveal that Thai QBE is another powerful tool for querying ad hoc data. Thai QBE can be applied to query ad hoc data from other databases easily, it is straightforward to modify. The Thai QBE will be extended to support recursive relationship in future.


1. Adamski, J. J. & Finnegan, T. K. (2010). New Perspectives on Microsoft Office Access 2007: Introductory. Florence: Cengage Learning, 105-144.
2. Hinz et al. (2020). MySQL 8.0 Reference Manual: Schema Object Names. (Online)., November 2, 2019.
3. Necas et al. (2014, December 10). python-Levenshtein. (Online)., November 10, 2019.
4. NLTK Project. (2019, August 20). WordNet Interface. (Online)., November 15, 2019.
5. Trongratsameethong, A. Vichianroj. P. (2018) ASQLAG - Automated SQL Assignment Grading System for Multiple DBMSs. Journal of Technology and Innovation in Tertiary Education Siam technology College,vol.1,no.42-62.
6. Wilbert, A. (2010, September 9). Navigate & use the Query by Example (QBE) interface in Microsoft Access 2007. (Online)., No-vember 2, 2019.
7. Williams, B., (2017, August 22). Data Model for Asset Management. (Online)., Novem-ber 25, 2019.
8. Yin, T. (2017, December 2). python-translate (Online)., November 13, 2019.
9. Zhang, S., Hu,Y., & Bian, G. (2017, 25-26 March ). Research on String Similarity Algo-rithm based on Levenshtein Distance. The 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China. DOI: 10.1109/IAEAC.2017.8054419.
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