There is increasing demand to integrate big data analytic systems using SQL. Given the vast ecosystem of SQL applications, enabling SQL capabilities allows big data platforms to expose their analytic potential to a wide variety of end users, accelerating discovery processes and providing significant business value. Most existing big data frameworks are based on one particular programming model such as MapReduce or Graph. However, data scientists are often forced to manually create adhoc data pipelines to connect various big data tools and platforms to serve their analytic needs. When the analytic tasks change, these data pipelines may be costly to modify and maintain. In this paper we present SQL-SA, a polymorphic and parallelizable SQL scalar and aggregate infrastructure in Aster 6.20. This infrastructure extends Aster 6's MapReduce and Graph capabilities to support polymorphic user-defined scalar and aggregate functions using flexible SQL syntax. The implementation enhances main Aster components including query syntax, API, planning and execution extensively. Integrating these new user-defined scalar and aggregate functions with Aster MapReduce and Graph functions, Aster 6.20 enables data scientists to integrate diverse programming models in a single SQL statement. The statement is automatically converted to an optimal data pipeline and executed in parallel. Using a real world business problem and data, Aster 6.20 demonstrates a significant performance advantage (25%+) over Hadoop Pig and Hive.

We describe new, efficient algorithms for layout modification and phase assignment for dark field alternating-type phase shifting masks in the single exposure regime. We make the following contributions. First, we suggest new two-coloring and compaction approach that simultaneously optimizes layout and phase assignment which is based on planar embedding of an associated conflict graph. We also describe additional approaches to cooptimization of layout and phase assignment for alternating PSM. Second, we give optimal and fast algorithms to minimize the number of phase conflicts that must be removed to ensure two colorability of the conflict graph. We reduce this problem to the T-join problem which asks for a minimum weight edge set A such that a node u is incident to an odd number of edges of A if u belongs to a given node subset T of a weighted graph. Third, we suggest several practical algorithms for the T-join problem. In sparse graphs, our algorithms are faster than previously known methods. Computational experience with industrial VLSI layout benchmarks shows the advantages of the new algorithms.

We describe new, efficient algorithms for layout modification and phase assignment for dark field alternating-type phase shifting masks in the single exposure regime. We make the following contributions. First, we suggest new two-coloring and compaction approach that simultaneously optimizes layout and phase assignment which is based on planar embedding of an associated conflict graph. We also describe additional approaches to cooptimization of layout and phase assignment for alternating PSM. Second, we give optimal and fast algorithms to minimize the number of phase conflicts that must be removed to ensure two colorability of the conflict graph. We reduce this problem to the -join problem which asks for a minimum weight edge set such that a node is incident to an odd number of edges of if belongs to a given node subset of a weighted graph. Third, we suggest several practical algorithms for the -join problem. In sparse graphs, our algorithms are faster than previously known methods. Computational experience with industrial VLSI layout benchmarks shows the advantages of the new algorithms.