@article {17865,
	title = {Processing large-scale multi-dimensional data in parallel and distributed environments},
	journal = {Parallel Computing},
	volume = {28},
	year = {2002},
	month = {2002/05//},
	pages = {827 - 859},
	abstract = {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 raw data in a dataset and processing it into a data product. However, in many areas of science and engineering, a scientist{\textquoteright}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.},
	keywords = {Data-intensive applications, Distributed computing, Multi-dimensional datasets, PARALLEL PROCESSING, Runtime systems},
	isbn = {0167-8191},
	doi = {10.1016/S0167-8191(02)00097-2},
	url = {http://www.sciencedirect.com/science/article/pii/S0167819102000972},
	author = {Beynon,Michael and Chang,Chialin and Catalyurek,Umit and Kurc,Tahsin and Sussman, Alan and Andrade,Henrique and Ferreira,Renato and Saltz,Joel}
}
