Clustering and Association Rules for Web Service Discovery and Recommendation: A Systematic Literature Review

Abstract

The purpose of this study is to identify, summarize, and systematically compare various clustering and association rule techniques for web service discovery and recommendation, identify the most common data sets used in the extant literature, and highlight current trends and future research directions. Following the methodology of Kitchenham and Charters (Guidelines for performing Systematic Literature reviews in Software Engineering, 2007) for a systematic literature review (SLR), a set of research questions are designed. Six digital databases are searched. A total of 4581 papers were initially retrieved, and a rigorous two-stage scanning process resulted in 66 relevant papers. Based on the selection criteria and data extraction, 57 final studies were selected. These papers are summarized and compared, and the relevant information is extracted to answer the research questions. The synthesis resulted in knowledge of currently proposed methods for web service discovery and recommendation based on clustering and association rule techniques. Furthermore, it identifies algorithms, similarity measures, evaluation metrics, and data sets. Also identifies challenges, research gaps, trends, and future directions. We propose a classification of web service discovery and recommendation methods and map the 57 final selected papers into these classes. This review will help researchers to understand the current state-of-the-art in clustering and association rules techniques for web service discovery and recommendation, and also recognize trends and future directions for improvement. Future studies should broaden the basis of discovery and recommendation by including various types of web service descriptions including plain text that are currently used in web APIs. An opportunity for improvement by utilizing modern techniques based on big data analytics and social network analysis.