# Columns Occurrences Graph to Improve Column Prediction in Deep Learning [PERSON]  
1 Department of Computer Science and Engineering, Hanyang University, Ansan 15588, Korea; [EMAIL_ADDRESS] (S.A.); [EMAIL_ADDRESS] (M.U.K. )1 Department of Computer Science and Engineering, Hanyang University, Ansan 15588, Korea; shanza92@hanyang. for example, medicine, do not necessarily know structured query languages, limiting the access of organizational knowledge to a limited number of users. Therefore, existing data storage is not being utilized to its maximum potential. Compulsory knowledge of structured query language for comprehensive access to all aspects of data has become a hurdle. Translating natural language questions into structured query language would provide maximum user access to relational databases. Asking questions in English-language text provides constraint-free access to databases, liberating the user from careful selection and click-based interfaces. Solving text-to-SQL tasks can expose the whole relational database for users to utilize and analyze according to their needs and choices. Seeking a solution for this issue leads us toward the field of natural language processing. However, natural language processing techniques, alone, cannot solve this problem, as the database, itself, is a critical part of the stated problem in this context, leading us to data-science methods as well. Combining natural language processing and data science methods may yield the solution to building a natural language interface for databases. Thus, translating natural language queries into structured-language queries is a struggling area for merging natural language processing and data science. The goal is to translate natural language queries into SQL that can be executed on a system to access its data for all kinds of users. Translating natural language questions into programmed language has been a long-standing problem [1,2]. In this work, we have focused on a natural language interface to databases by the generation of executable SQL queries. Figure 1 elaborates the text-to-SQL task briefly. Deep learning has been an emerging tool in various research areas like communication, machine translation, and networking. Deep learning methods have arrived at competitive performances, compared with the traditional techniques, in a short time. They have been shown to be a potential tool for growth, even for mature fields with a higher bar of entry for new approaches, such as communication [3,4]. Deep learning has exhibited its problem-solving potential and growth in mobile traffic classification, as well. Though port information is not critical when working with deep learning traffic classifiers, mobile classifiers using deep learning methods can identify the application sources from which information is coming. Feature representations of direct-input data from training can be a potential path for improving traffic classifiers [5]. Deep learning has also shown promising results in the area of encrypted mobile traffic classification. However, deep learning, here, has some limitations because it is less mature in this area than are traditional methods, besides which, its black-box nature allows the least human intervention but contributes to resolving some of the complex matters in  
Figure 1: text-to-SQL task. improving performance [6; 7]. Deep learning has recently been adopted for text-to-SQL tasks; keeping in mind deep learning's success stories in neighbouring research areas, deep learning shows potential for rapid improvement in this task as well. The initial work in adapting deep learning to text-to-SQL tasks was primarily based on neural network sequence-to-sequence learning [8], adopting copying and attention mechanisms and the sequence-to-sequence RNN model to improve translation accuracy. These approaches improved the basic framework and their contributions were pioneering in NLIDB systems with deep learning concepts.