  With the ever-increasing volume of data, there is an urgent need to provide
expressive and efficient tools to support Big Data analytics. The declarative
logical language Datalog has proven very effective at expressing concisely
graph, machine learning, and knowledge discovery applications via recursive
queries. In this demonstration, we develop Logic Library (LLib), a library of
recursive algorithms written in Datalog that can be executed in BigDatalog, a
Datalog engine on top of Apache Spark developed by us. LLib encapsulates
complex logic-based algorithms into high-level APIs, which simplify the
development and provide a unified interface akin to the one of Spark MLlib. As
LLib is fully compatible with DataFrame, it enables the integrated utilization
of its built-in applications and new Datalog queries with existing Spark
functions, such as those provided by MLlib and Spark SQL. With a variety of
examples, we will (i) show how to write programs with LLib to express a variety
of applications; (ii) illustrate its user experience in Apache Spark ecosystem;
and (iii) present a user-friendly interface to interact with the LLib framework
and monitor the query results.
