Incremental evaluation of continuous preference queries

Over recent decades, several studies regarding the incorporation of preferences into query languages have been developed in the database research field. The enthusiasm and interest in this research topic is due to its use in important practical applications such as e-commerce and social networks. An example of a solid work in this research field for relational databases is the CPrefSQL language that allows for the execution of queries with conditional preferences. More recently, due to the broad spectrum of new data stream applications, the interest in continuous query processing by the database community has shown a notable increase. The evaluation of preferences in continuous queries poses additional challenges, since data change rapidly in data stream scenarios. In order to aid in dealing with these issues, we proposed an incremental and efficient method for evaluating continuous CPrefSQL queries. We revisited the state of the art algorithms for the evaluation of continuous CPrefSQL queries and compared these with our new approach. We conducted a detailed complexity analysis and an extensive set of experiments with synthetic and real datasets, which shows that our proposed algorithm has considerably superior performance.