International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.10, No.6, Jun. 2018

SDRED: Smart Data Retrieval Engine for Databases

Full Text (PDF, 731KB), PP.1-10

Views:89   Downloads:3


Shahnawaz Ahmad, Syed Rameem Zahra

Index Terms

Digital data;non-expert user;Structured Query Language (SQL);Natural Language Queries


Today computers are continuously betrothed in almost all domains and organizations. Thus, databases act as the heart for storing and retrieving information that contain huge digital data. However, in order to interact with such databases, it is necessary to have knowledge about the Structured Query Language (SQL), which is difficult for non-expert users to understand and manipulate. So, there is an emergent need to develop a smart and a user friendly computational technique to interact with databases. The current work proposed a smart technique that can handle such context. The proposed “Smart Data Retrieval Engine for Databases (SDRED)” provided an environment that allows a non-expert user to write and to execute the database queries easily. Furthermore, it retrieved the data stored in databases without a prior knowledge of the SQL. SDRED, which enables the non-expert user to write database queries in natural language (such as English) and to convert them to their SQL query equivalents. The current work presented a detailed design and evaluation for the proposed system by executing different database queries in English. The results established that SDRED successfully converted the non-expert user’s natural language queries into their equivalent SQL queries, thereby providing an easy and user-friendly environment to interact with databases.

Cite This Paper

Shahnawaz Ahmad, Syed Rameem Zahra, "SDRED: Smart Data Retrieval Engine for Databases", International Journal of Information Technology and Computer Science(IJITCS), Vol.10, No.6, pp.1-10, 2018. DOI: 10.5815/ijitcs.2018.06.01


[1]ElmasriRamez, and Shamkant B. Navathe, Fundamentals of database systems, Pearson, (2014).

[2]Burleson, Donald K., et al., Handbook of Advanced SQL Database Programmer, Rampant Tech Press, (2003).

[3]Ma, Zongmin, et al., Intelligent databases: technologies and applications, IGI Global, (2007).

[4]Weizenbaum, Joseph, ELIZA—a computer program for the study of natural language communication between man and machine, Communications of the ACM, vol. 9, no. 1, pp. 36-45, (1966). DOI:10.1145/365153.365168

[5]Ilyasu Anda, Isah Omeiza Radiu, Enesi Femi Amine, “A safety data model for data analysis and decision making”, International Journal of Information Engineering and electronic business (IJIEEB), Vol. 9, No. 4, pp. 21 – 30, 2017. DOI: 10.5815/IJIEEB.2017.04.04.

[6]Harrison John Bhatti, Babak Bashari Rad, “Databases in Cloud Computing: A literature review”, International journal of Information Technology and Computer Science (IJITCS), Vol. 9, No. 4, pp.9-17, 2017. DOI: 10.2815/ijitcs.2017.04.02 

[7]ElisaBertino, Barbara Catania, and Gian P. Zarri, Intelligent database systems, ACM Press, Addison-Wesley, (2001). ISBN: 0-201-87736-8. 

[8]Lewis, David D., and Karen Spärck Jones, Natural language processing for information retrieval, Communications of the ACM, vol. 39, no. 1, pp 92-101, (1996). DOI:10.1145/234173.234210 

[9]Daniel Jurafsky, and James H. Martin, Speech and Language Processing, Pearson, (2000).

[10]Paredes-Valverde, Mario Andrés, et al, ONLI: An ontology-based system for querying DBpedia using natural language paradigm, Expert Systems with Applications, vol. 42, no. 12, pp. 5163-5176, (2015).

[11]Ngamnij, Somjit et al., Semantic ontology mapping for interoperability of learning resource systems using a rule-based reasoning approach, Expert Systems with Applications, vol. 40, no. 18, pp7428-7443, (2013). DOI:10.1016/j.eswa.2013.07.027

[12]Heeringa, Wilbert, Measuring dialect pronunciation differences using Levenshtein distance [Dissertation], Rijksuniversiteit Groningen, (2004).

[13]NF Ayan, ArindamMandal, and Jing Zheng, Clarifying natural language input using targeted questions, U.S. Patent 13/866,509, (2013).

[14]Li, Fei, and Hosagrahar V. Jagadish, NaLIR: An interactive natural language interface for querying relational databases, Proceedings of the 2014 ACM SIGMOD international conference on Management of data. ACM, (2014). DOI:10.1145/2588555.2594519

[15]Damljanović, Danica, et al., Improving habitability of natural language interfaces for querying ontologies with feedback and clarification dialogues, Web Semantics: Science, Services and Agents on the World Wide Web, vol. 19, pp1-21, (2013). DOI:10.1016/j.websem.2013.02.002

[16]Deebha Mumtaz, Bindiya Ahuja, “A Lexical Approach for opinion Mining in Twitter”, International Journal of Education and Management Engineering (IJEME), Vol. 6, No. 4, pp. 20 – 29, 2016. DOI: 10.5815/ijeme.2016.04.03. 

[17]Kaufmann, Esther, and Abraham Bernstein, Evaluating the usability of natural language query languages and interfaces to Semantic Web knowledge bases, Web Semantics: Science, Services and Agents on the World Wide Web, vol. 8, no. 4, pp377-393, (2010). DOI:10.1016/j.websem.2010.06.001 

[18]I. Habernal, M. Konopík, SWSNL: Semantic web search using natural language, Expert Systems with Applications, vol. 40, no. 9, pp3649–3664, (2013). DOI:10.1016/j.eswa.2012.12.070 

[19]Wen-Tau Y., Ming-Wei C., Xiaodong H., Jianfeng G., “Semantic parsing via staged query grape generation”, Microsoft Research, Redmond, WA 98052, USA. 

[20]Llopis, Miguel, and Antonio Ferrández, How to make a natural language interface to query databases accessible to everyone: An example, Computer Standards & Interfaces, vol. 35, no. 5, pp 470-481, (2013). DOI:10.1016/j.csi.2012.09.005 

[21]Melyara. Mezzi, Nadjia. Benblidia, “Study of context Modelling criteria in information Retrieval”, International journal of Information Technology and Computer Science (IJITCS), Vol. 9, No. 3, pp. 28-39, 2017. DOI: 10.5815/ijitcs.2017.03.04