Abstract

Abstract
Modern Java Compilers, such as Sun's HotSpot compilers, implement a number of optimizations, ranging from high-level program transformations to low-level architecure dependent operations such as instruction scheduling. In a Just-in-Time (JIT) environment, the impact of each optimization must be weighed against its cost in terms of total runtime. Towards better understanding the usefulness of individual optimizations, we study the main optimizations available on Sun HotSpot compilers for a wide range of scientific and non-scientific benchmarks, weighing their cost and benefits in total runtime. We chose the HotSpot technology because it is state of the art and its source code is available. © J.UCS.

Abstract
Interpreted languages are widely used due to ease to use, portability, and safety. On the other hand, interpretation imposes a significance overhead. Just-in-Time (JIT) compilation is a popular approach to improving the runtime performance of languages such as Java. We compare the performance of a JIT compiler with a traditional compiler and with an emulator. We show that the compilation overhead from using JIT is negligible, and that the JIT compiler achieves better overall performance, suggesting the case for aggresive compilation in JIT compilers. © J. UCS.

Abstract

Abstract
Statistical relational learning (SRL) algorithms learn statistical models from relational data, such as that stored in a relational database. We previously introduced view learning for SRL, in which the view of a relational database can be automatically modified, yielding more accurate statistical models. The present paper presents SAYU-VISTA, an algorithm which advances beyond the initial view learning approach in three ways. First, it learns views that introduce new relational tables, rather than merely new fields for an existing table of the database. Second, new tables or new fields are not limited to being approximations to some target concept; instead, the new approach performs a type of predicate invention. The new approach avoids the classical problem with predicate invention, of learning many useless predicates, by keeping only new fields or tables (i.e., new predicates) that immediately improve the performance of the statistical model. Third, retained fields or tables can then be used in the definitions of further new fields or tables. We evaluate the new view learning approach on three relational classification tasks.

Abstract
The amount of data collected and stored in databases is growing considerably in almost all areas of human activity. In complex applications the data involves several relations and proposionalization is not a suitable approach. Multi-Relational Data Mining algorithms can analyze data from multiple relations, with no need to transform the data into a single table, but are computationally more expensive. In this paper a novel relational classification algorithm based on the k-nearest neighbour algorithm is presented and evaluated.