  In the past, the semantic issues raised by the non-monotonic nature of
aggregates often prevented their use in the recursive statements of logic
programs and deductive databases. However, the recently introduced notion of
Pre-mappability (PreM) has shown that, in key applications of interest,
aggregates can be used in recursion to optimize the perfect-model semantics of
aggregate-stratified programs. Therefore we can preserve the declarative formal
semantics of such programs while achieving a highly efficient operational
semantics that is conducive to scalable implementations on parallel and
distributed platforms. In this paper, we show that with PreM, a wide spectrum
of classical algorithms of practical interest, ranging from graph analytics and
dynamic programming based optimization problems to data mining and machine
learning applications can be concisely expressed in declarative languages by
using aggregates in recursion. Our examples are also used to show that PreM can
be checked using simple techniques and templatized verification strategies. A
wide range of advanced BigData applications can now be expressed declaratively
in logic-based languages, including Datalog, Prolog, and even SQL, while
enabling their execution with superior performance and scalability.
