Processing universal quantification queries using MapReduce

Universal quantification queries are an interesting type of queries that are used in many applications. Although, universal queries have gained their importance in querying traditional databases that are usually implemented on a single machine; nowadays, the rapid growth in information and the extremely fast increase in the number of Web users have driven the need to migrate to new processing environments that are capable to access, process, store, and maintain huge amounts of valuable data. Thus, the use of cloud emerged as a solution for several big data problems. In this paper, we present a number of computing techniques for processing universal quantification queries on large datasets using the popular MapReduce framework. In addition, we present experimental results that show the speed-up and scale-out properties of our proposed algorithms.