## 2 Related Work  
A wide variety of studies have proposed the use of sequential pattern mining to solve the problems of shelf management and layout. The authors of [53] proposed a timeliness variable threshold for sequential patterns, with a focus on finding pyramid scheme patterns in both individual and different sequences. The authors of this study employed the abovementioned method with real financial data to analyze sequential time intervals. The authors of [54] proposed the use of the PrefixSpan algorithm to generate sequential patterns to create a personalized product recommendation system. Its aim was for users to be able to buy the best products in the shortest possible time, and the research presented a cross-domain recommendation by employing ontology while solving the problem. The experimental results showed that it could help alleviate the problems of new users to some extent. The authors of [55] studied a cloud service-customized product information system. They collected three types of data: e-commerce services, promotion-type modules, and cloud service-customized promotional products. The purposes of the study were to generate and propose a system for detecting customers' behaviors. Marketing data were analyzed by employing an association rule mining algorithm and sequential pattern mining. The experimental results of this research revealed that the SPM method could effectively analyze customer behavior data and search for information regarding product promotion with the goal of evaluating sales and increasing production. The authors of [56] proposed the use of commonsense knowledge (CSK) in a machine-learning application, which was compared with human behavior, and the application was found to improve common-sense decision-making and enhance data mining. The authors of [57] suggested that the backroom was the most important part of retail, so they focused on optimizing the distribution center and sales spaces of a store. They proposed a solution for the problem of grocery backroom sizing to reduce floor space, which may result in higher revenues and/or lower backroom-associated costs. The authors of [58] proposed a framework for sequential pattern mining to find a pattern that was more complex and more difficult than what was currently available. Such a pattern was found in the data regarding individual self-management. The findings of this study were also applied to the American Time Use Survey (ATUS) dataset to explore individual time-allocation behaviors and identify sequences of activities, e.g., which patterns of activities need to be performed first and later, a sequence known as the frequency. The authors of [59] proposed an analysis method based on the Poisson model that considers impactable parameters, such as the time of the day, day of the week, and week of the month, to investigate a source of variation in a case study from Chile. The authors of [37] proposed the use of the Map-Reduce algorithm in conjunction with sequential pattern mining to find correlations between groups of products. The data were randomized for sequential pattern mining with the aim of finding the most profitable products in order to improve the overall profitability of a supermarket. The authors of [60] found problems in analyses of travelling data and ineffective tourism management because the existing techniques were unable to capture the sequential patterns of hidden tourist data. After researching a case study of Australian outbound tourism, the authors suggested a solution that comprised an analysis of travelers' behaviors through location-tagging photos and the use of sequential pattern mining. The efficacy of the proposed method demonstrated the benefits of promoting tourism and presenting suitable destinations for tourists. The authors of [61] applied item-based collaborative filtering with an Apriori-based model to find patterns in an e-commerce service. The disadvantages of this filtering system included redundant rules and sequential patterns, so further improvements are required.