9 research outputs found
Speeding up SOR Solvers for Constraint-based GUIs with a Warm-Start Strategy
Many computer programs have graphical user interfaces (GUIs), which need good
layout to make efficient use of the available screen real estate. Most GUIs do
not have a fixed layout, but are resizable and able to adapt themselves.
Constraints are a powerful tool for specifying adaptable GUI layouts: they are
used to specify a layout in a general form, and a constraint solver is used to
find a satisfying concrete layout, e.g.\ for a specific GUI size. The
constraint solver has to calculate a new layout every time a GUI is resized or
changed, so it needs to be efficient to ensure a good user experience. One
approach for constraint solvers is based on the Gauss-Seidel algorithm and
successive over-relaxation (SOR).
Our observation is that a solution after resizing or changing is similar in
structure to a previous solution. Thus, our hypothesis is that we can increase
the computational performance of an SOR-based constraint solver if we reuse the
solution of a previous layout to warm-start the solving of a new layout. In
this paper we report on experiments to test this hypothesis experimentally for
three common use cases: big-step resizing, small-step resizing and constraint
change. In our experiments, we measured the solving time for randomly generated
GUI layout specifications of various sizes. For all three cases we found that
the performance is improved if an existing solution is used as a starting
solution for a new layout
Generalizing, Decoding, and Optimizing Support Vector Machine Classification
The classification of complex data usually requires the composition of processing steps. Here, a major challenge is the selection of optimal algorithms for preprocessing and classification. Nowadays, parts of the optimization process are automized but expert knowledge and manual work are still required. We present three steps to face this process and ease the optimization. Namely, we take a theoretical view on classical classifiers, provide an approach to interpret the classifier together with the preprocessing, and integrate both into one framework which enables a semiautomatic optimization of the processing chain and which interfaces numerous algorithms
An evaluation of the challenges of Multilingualism in Data Warehouse development
In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen