Processing large-scale multi-dimensional data in parallel and distributed environments
2002
Analysis of data is an important step in understanding and solving a scientific problem. Analysis involves extracting the data of interest from all the available data in a dataset and processing it into a data product. However, in many areas of science and engineering, a scientist's ability to analyze information is increasingly becoming hindered by dataset sizes. The vast amount of data in scientific datasets makes it a difficult task to efficiently access the data of interest, and manage potentially heterogeneous system resources to process the data. Subsetting and aggregation are common operations executed in a wide range of data-intensive applications. We argue that common runtime and programming support can be developed for applications that query and manipulate large datasets. This paper presents a compendium of frameworks and methods we have developed to support efficient execution of subsetting and aggregation operations in applications that query and manipulate large, multi-dimensional datasets in parallel and distributed computing environments.
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
0
References
0
Citations
NaN
KQI