Atlas: on the expression of spatial-keyword group queries using extended relational constructs.

The popularity of GPS-enabled cellular devices introduced numerous applications, e.g., social networks, micro-blogs, and crowd-powered reviews. These applications produce large amounts of geo-tagged textual data that need to be processed and queried. Nowadays, many complex spatio-textual operators and their matching complex indexing structures are being proposed in the literature to process this spatio-textual data. For example, there exist several complex variations of the spatio-textual group queries that retrieve groups of objects that collectively satisfy certain spatial and textual criteria. However, having complex operators is against the spirit of SQL and relational algebra. In contrast to these complex spatio-textual operators, in relational algebra, simple relational operators are offered, e.g., relational selects, projects, order by, and group by, that are composable to form more complex queries. In this paper, we introduce Atlas, an SQL extension to express complex spatial-keyword group queries. Atlas follows the philosophy of SQL and relational algebra in that it uses simple declarative spatial and textual building-block operators and predicates to extend SQL. Not only that Atlas can represent spatio-textual group queries from the literature, but also it can compose other important queries, e.g., retrieve spatio-textual groups from subsets of object datasets where the selected subset satisfies user-defined relational predicates and the groups of close-by objects contain miss-spelled keywords. We demonstrate that Atlas is able to represent a wide range of spatial-keyword queries that existing indexes and algorithms would not be able to address. The building- block paradigm adopted by Atlas creates room for query optimization, where multiple query execution plans can be formed.