A domain specific language for the internet of things in the retail sector

Abstract

In recent years, there has been a marked rise in the need for data analysis. This is partly due to the increased pervasiveness and adoption of the Internet of Things (IoT) and, consequently, the data it generates – contributing Big Data. Considerable leaps have been made regarding applications and organisations that can conduct data analysis giving rise to the necessary scientific treatment of data. Profitability for businesses through the valorisation and usability of data is the primary reason for this increase in analytical work. However, due to the speed at which, and the amount of, data being generated, data analysis is a commodity that not every business can afford or has the resources to effectively carry out. In particular, small to medium-sized enterprises face challenges when investing in data analysis due to high outsourcing costs. This project discusses how a Domain Specific Language (DSL) can help small businesses benefit from investing in solutions, using smart devices connected as an IoT and subsequent data analysis. This was done by implementing a cost-effective solution that will enable small to medium-sized businesses better manage their data. The solution is made up of two parts. The first part enables the store owners/manager or salespersons the ability to input their sales records into the solution using a form. The data is stored in a CSV file, and it is automatically prepared for analysis. The other part offers the store owner/manager the ability to analyse the inputted data. The solution allows the forementioned users to input their query in simple English, for example: “What was the best month for sales between 1st February 2020 and 1st July 2020?”. Then, the DSL will interpret this query using natural language processing, and it will automatically analyse the dataset; based on this query. Afterwards, the solution will produce graphical representations and text-based outputs of the analysis conducted on the data. The artefact was evaluated through its ability to validly handle different datasets. To evaluate the solution’s ability to deal with various datasets, those used to test the system were randomly generated from different years and related to various retail outlets, namely, electronic stores and fashion stores.