Scalability Transformations on Declarative Applications

Many current distributed applications are based on the exchange of XML messages. Scaling such applications to the high processing volume demanded by Internet-scale deployment typically requires costly redesign and coding. In this paper, we investigate how a declarative specification of such applications can simplify the task of deploying them on a large number of host machines. In our model, applications are represented as a graph of message queues connected by message flow rules. The state of application instances is encoded in the message history of the queues and accessed using XQuery expressions. We show how to split such an application into distributable fragments using graph partitioning and discuss different algorithms for placing the fragments on hosts. Typically, an initial application specification contains data dependencies that place an upper limit on the number of fragments, and hence the number of usable machines. We describe transformations that increase the number of possible fragments by converting data dependencies into message flow. An evaluation using the TPC-App benchmark and a runtime system prototype confirms the feasibility and performance benefits of this approach

Repetitive querying of large random heterogeneous datasets in RDBMS using materialized views

A methodology has been developed to increase time efficiency of querying large heterogeneous datasets repetitively by applying materialized views on repetitive complex queries. Additionally, a simple user interface is provided to demonstrate the utility of this research methodology. The programs demonstrate sufficiently that the core design can be used to deploy a complete system which could be used in different domains. The methodology as developed in this research is presented as an experimental proof-of-concept prototype based on an abstract design

Cost-Based Optimization of Integration Flows

Integration flows are increasingly used to specify and execute data-intensive integration tasks between heterogeneous systems and applications. There are many different application areas such as real-time ETL and data synchronization between operational systems. For the reasons of an increasing amount of data, highly distributed IT infrastructures, and high requirements for data consistency and up-to-dateness of query results, many instances of integration flows are executed over time. Due to this high load and blocking synchronous source systems, the performance of the central integration platform is crucial for an IT infrastructure. To tackle these high performance requirements, we introduce the concept of cost-based optimization of imperative integration flows that relies on incremental statistics maintenance and inter-instance plan re-optimization. As a foundation, we introduce the concept of periodical re-optimization including novel cost-based optimization techniques that are tailor-made for integration flows. Furthermore, we refine the periodical re-optimization to on-demand re-optimization in order to overcome the problems of many unnecessary re-optimization steps and adaptation delays, where we miss optimization opportunities. This approach ensures low optimization overhead and fast workload adaptation

Gridfields: Model-Driven Data Transformation in the Physical Sciences

Scientists\u27 ability to generate and store simulation results is outpacing their ability to analyze them via ad hoc programs. We observe that these programs exhibit an algebraic structure that can be used to facilitate reasoning and improve performance. In this dissertation, we present a formal data model that exposes this algebraic structure, then implement the model, evaluate it, and use it to express, optimize, and reason about data transformations in a variety of scientific domains. Simulation results are defined over a logical grid structure that allows a continuous domain to be represented discretely in the computer. Existing approaches for manipulating these gridded datasets are incomplete. The performance of SQL queries that manipulate large numeric datasets is not competitive with that of specialized tools, and the up-front effort required to deploy a relational database makes them unpopular for dynamic scientific applications. Tools for processing multidimensional arrays can only capture regular, rectilinear grids. Visualization libraries accommodate arbitrary grids, but no algebra has been developed to simplify their use and afford optimization. Further, these libraries are data dependent—physical changes to data characteristics break user programs. We adopt the grid as a first-class citizen, separating topology from geometry and separating structure from data. Our model is agnostic with respect to dimension, uniformly capturing, for example, particle trajectories (1-D), sea-surface temperatures (2-D), and blood flow in the heart (3-D). Equipped with data, a grid becomes a gridfield. We provide operators for constructing, transforming, and aggregating gridfields that admit algebraic laws useful for optimization. We implement the model by analyzing several candidate data structures and incorporating their best features. We then show how to deploy gridfields in practice by injecting the model as middleware between heterogeneous, ad hoc file formats and a popular visualization library. In this dissertation, we define, develop, implement, evaluate and deploy a model of gridded datasets that accommodates a variety of complex grid structures and a variety of complex data products. We evaluate the applicability and performance of the model using datasets from oceanography, seismology, and medicine and conclude that our model-driven approach offers significant advantages over the status quo

Proceedings of the 4th International Conference on Principles and Practices of Programming in Java

This book contains the proceedings of the 4th international conference on principles and practices of programming in Java. The conference focuses on the different aspects of the Java programming language and its applications