Usability, Performance and Scalability
         for Expressive Data Languages
        via Cardinality-Based Aggregates

                                  Carlo Zaniolo

                      UCLA Computer Science Department
                               Los Angeles, CA
                                    USA
                         Email: zaniolo@cs.ucla.edu



    Abstract. In theory, the combined use of recursion and negation al-
    lows the declarative expression of very powerful algorithms in languages
    such as Datalog or SQL. However, the diffculty of writing such programs
    and proving that they satisfy formal non-monotonic semantics makes
    such an approach totally impractical for software developers. Thus, we
    propose a new general solution to the problem of developing advanced
    applications in logic-based languages. Our approach is based on the com-
    bined use of recursion and aggregates endowed with a unified definition
    of their semantics based on cardinality. In fact, we show that this entails
    the expression on a wide range of algorithms used in ML, data-mining
    and graph applications, which can be very appealing for practitioners
    because of (i) the compactness of their code, (ii) a simplified proof or
    verification for their stable-model semantics, (iii) their effcient implemen-
    tations via a max-optimized semi-naive fixpoint algorithm, and (iv) their
    superior scalability via Stale-Synchronous Parallelism. To demonstrate
    and further enhance the usability of our proposed framework, including
    the several efficient and scalable applications we developed, we provide
    a Logical Algorithm Library (Llib) and a Logical Data-Frame System
    (LFrame). By integrating access to Llib libray with other Apache Spark
    libraries, and supporting the interoperability of our BigDatalog, RaSQL,
    and Datalog-ML systems with Scala, Java and Python, LFrame turns
    Datalog into a powerful and attractive tool for advanced application de-
    velopment in the Spark ecosystem.




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