Structured Query Language (SQL) is commonly used in Relational Database Management Systems (RDBMS) and is currently one of the most popular data definition and manipulation languages. Its core functionality is implemented, with only some minor variations, throughout all RDBMS products. It is an effective tool in the process of managing and querying data in relational databases. This paper describes a method to effectively automate the conversion of a data query from a Natural Language Query (NLQ) to Structured Query Language (SQL) with Online Analytical Processing (OLAP) cube data warehouse objects. To obtain or manipulate the data from relational databases, the user must be familiar with SQL and must also write an appropriate and valid SQL statement. However, users who are not familiar with SQL are unable to obtain relevant data through relational databases. To address this, we propose a Natural Language Processing (NLP) model to convert an NLQ into an SQL query. This allows novice users to obtain the required data without having to know any complicated SQL details. The model is also capable of handling complex queries using the OLAP cube technique, which allows data to be pre-calculated and stored in a multi-dimensional and ready-to-use format. A multi-dimensional cube (hypercube) is used to connect with the NLP interface, thereby eliminating long-running data queries and enabling self-service business intelligence. The study demonstrated how the use of hypercube technology helps to increase the system response speed and the ability to process very complex query sentences. The system achieved impressive performance in terms of NLP and the accuracy of generating different query sentences. Using OLAP hypercube technology, the study achieved distinguished results compared to previous studies in terms of the speed of the response of the model to NLQ analysis, the generation of complex SQL statements, and the dynamic display of the results. As a plan for future work, it is recommended to use infinite-dimension (n-D) cubes instead of 4-D cubes to enable ingesting as much data as possible in a single object and to facilitate the execution of query statements that may be too complex in query interfaces running in a data warehouse. The study demonstrated how the use of hypercube technology helps to increase system response speed and process very complex query sentences.

Small and medium-sized (SMEs) companies have proven economic competencies and great potential in light of the recent global economic transformations in developed and developing countries, which qualified them to play a major role in development and reform policy, by providing job opportunities, intensifying the industrial fabric, and supporting large industries. It should be noted that these institutions need greater efforts and important components that respond to their aspirations and idiosyncrasies, to be able to survive and compete in the markets, as we have witnessed the bankruptcy and collapse of many companies due to financial conditions and obstacles. In this paper, we proposed building a lending decision support system (DSS) for SMEs and researching the reasons that prevent them from lending, and what are the pillars on which the credit decision in banks depends, and how difficult it is to analyze the variables and their information, the data set represents SMEs lending requests from Jordan Ahli Bank. Four machine learning algorithms were used to build an automated system to support decision-making in granting credit. The classification accuracy of the models was evaluated where the accuracy exceeded 90%, and SVM outperformed other rating models. Our findings show that the challenges that SMEs face to obtain credit are summarized by the lack of the company's capital, the recent date of incorporation, and the lack of guarantees.