원문정보
초록
영어
While the database collection types set, list, and record have received in-depth attention, the fourth type, array, is still far from being integrated into database modeling. Due to this lack of attention there is only insufficient array support by today's database technology. This is surpris-ing given that large, multi-dimensional arrays have manifold practical applications in earth sciences (such as remote sensing and climate modeling), life sciences (such as microarray data and human brain imagery), and many more areas. Consequently, flexible retrieval today is supported on metadata, but not on the observation and simulation data themselves. To overcome this, large, multi-dimensional arrays as first-class database citizens have been studied by various groups worldwide. Several formalisms and languages tailored for use in array databases have been proposed and more or less completely implemented, sometimes even in operational use. In the attempt towards a consolidation of the field we compare four important array models, AQL, AML, Array Algebra }, and RAM. As it turns out, Array Algebra is capable of expressing all other models, and additionally offers functionality not present in the other models. We show this by mapping all approaches to Array Algebra. This establishes a common representation suitable for comparison and allows us discussing the commonalities and differences found. Finally, we show feasibility of conceptual array models for describing optimization and architecture.
목차
1. Introduction
1.1 The Array Abstraction
1.2 Technology Differentiation
1.3 Comparison Overview
2. Overview of Array Models
2.1 Array Algebra
2.2 AML
2.3 AQL
2.4 RAM
3. Comparison
3.1 Array Representation
3.2 Operations
3.3 Relational Embedding
4. Implementation Aspects
4.1 Architecture
4.2 Optimizability
4.3 Application Studies
4.4 Industrial Impact
5. Conclusion and Outlook
References