Towards More Data-Aware Application Integration (extended version)

Although most business application data is stored in relational databases, programming languages and wire formats in integration middleware systems are not table-centric. Due to costly format conversions, data-shipments and faster computation, the trend is to "push-down" the integration operations closer to the storage representation. We address the alternative case of defining declarative, table-centric integration semantics within standard integration systems. For that, we replace the current operator implementations for the well-known Enterprise Integration Patterns by equivalent "in-memory" table processing, and show a practical realization in a conventional integration system for a non-reliable, "data-intensive" messaging example. The results of the runtime analysis show that table-centric processing is promising already in standard, "single-record" message routing and transformations, and can potentially excel the message throughput for "multi-record" table messages.Comment: 18 Pages, extended version of the contribution to British International Conference on Databases (BICOD), 2015, Edinburgh, Scotlan

R-SQL: An SQL Database System with Extended Recursion

The relational database language SQL:1999 standard supports recursion, but thisapproach is limited to the linear case. Moreover, mutual recursion is not supported,and negation cannot be combined with recursion. We designed the language R-SQLto overcome these limitations in [ANSS13], improving termination properties in re-cursive definitions. In addition we developed a proof of concept implementation ofan R-SQL system. In this paper we describe in detail an improved system enhanc-ing performance. It can be integrated into existing RDBMS’s, extending them withthe aforementioned benefits of R-SQL. The system processes an R-SQL databasedefinition obtaining its extension in tables of an RDBMS (such as PostgreSQL andDB2). It is implemented in SWI-Prolog and it produces a Python script that, uponexecution, computes the result of the R-SQL relations. We provide some perfor-mance results showing the efficiency gains w.r.t. the previous version. We alsoinclude a comparative analysis including some representative relational a deductive systems

R-SQL: An SQL Database System with Extended Recursion

The relational database language SQL:1999 standard supports recursion, but this approach is limited to the linear case. Moreover, mutual recursion is not supported, and negation cannot be combined with recursion. We designed the language R-SQL to overcome these limitations, improving termination properties in recursive definitions. In addition we developed a proof of concept implementation of an R-SQL system. In this paper we describe in detail an improved system enhancing performance. It can be integrated into existing RDBMS's, extending them with the  aforementioned benefits of R-SQL. The system processes an R-SQL database definition obtaining its extension in tables of an RDBMS (such as PostgreSQL and DB2). It is implemented in SWI-Prolog and it produces a Python script that, upon execution, computes the result of the R-SQL relations. We provide some performance results showing the efficiency gains w.r.t. the previous version. We also include a comparative analysis including some representative relational a deductive systems

An Adaptive Genetic Algorithm with Dynamic Population Size for Optimizing Join Queries

The problem of finding the optimal join ordering executing a query to a relational database management system is a combinatorial optimization problem, which makes deterministic exhaustive solution search unacceptable for queries with a great number of joined relations. In this work an adaptive genetic algorithm with dynamic population size is proposed for optimizing large join queries. The performance of the algorithm is compared with that of several classical non-deterministic optimization algorithms. Experiments have been performed optimizing several random queries against a randomly generated data dictionary. The proposed adaptive genetic algorithm with probabilistic selection operator outperforms in a number of test runs the canonical genetic algorithm with Elitist selection as well as two common random search strategies and proves to be a viable alternative to existing non-deterministic optimization approaches