to a large number of ontologies covering overlapping domains. In order for these ontologies to be reused, they first need to be merged or aligned to one another. The processes of ontology alignment and merging are usually handled manually and often constitute a large and tedious portion

This paper describes an approach for the automatic generation and optimization of numerical software for processors with deep memory hierarchies and pipelined functional units. The production of such software for machines ranging from desktop workstations to embedded processors can be a tedious

Abstract The KNOWITALL system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, domain-independent, and scalable manner. The paper presents an overview of KNOW-ITALL's novel architecture

The processing power of mobile devices has greatly increased in recent years. This increased power has allowed for a greater range of applications to be deployed on this platform, providing constant access to information for end users. The limited size of these devices however, causes a number

. Thus, the validation of models is still a tedious task. In our previous work, we proposed cross-element validation. This process consists of performing the validation within a model by comparing the simulation results of the model under several instances of some of its composite elements. Elements are

processes including waves and currents, sediment transport and morphology change, channel infilling and inlet morphology, and dredged material fate. SMS provides the tools and macros for editing and display for mesh development, coordinate conversion, model con-nectivity, animations, and comparisons. SMS

are generated by a processes of “pseudo–exponentiation ” whose combinatorics faithfully accord with expectations based on Kreimer’s modified shuffle product and on the Drinfeld–Deligne conjecture. The existence of such a mechanism, relating thousands of complicated rational numbers, enables us to identify

ABSTRACT Manually querying search engines in order to accumulate a large body of factual information is a tedious, error-prone process of piecemeal search. Search engines retrieve and rank potentially relevant documents for human perusal, but do not extract facts, assess confidence, or fuse

Most methods for isolation of RNA from yeast require tedious vortexing with glass beads, and give low yields when scaled down to 10 ml cultures (1). In addition, it is frequently desirable to prepare RNA from several different yeast strains grown under a variety of growth conditions

Manually querying search engines in order to accumulate a large body of factual information is a tedious, error-prone process of piecemeal search. Search engines retrieve and rank potentially relevant documents for human perusal, but do not extract facts, assess confidence, or fuse information from