@conference {17891,
	title = {Time and space optimization for processing groups of multi-dimensional scientific queries},
	booktitle = {Proceedings of the 18th annual international conference on Supercomputing},
	series = {ICS {\textquoteright}04},
	year = {2004},
	month = {2004///},
	pages = {95 - 105},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Data analysis applications in areas as diverse as remote sensing and telepathology require operating on and processing very large datasets. For such applications to execute efficiently, careful attention must be paid to the storage, retrieval, and manipulation of the datasets. This paper addresses the optimizations performed by a high performance database system that processes groups of data analysis requests for these applications, which we call queries. The system performs end-to-end processing of the requests, formulated as PostgreSQL declarative queries. The queries are converted into imperative descriptions, multiple imperative descriptions are merged into a single execution plan, the plan is optimized to decrease execution time via common compiler optimization techniques, and, finally, the plan is optimized to decrease memory consumption. The last two steps are experimentally shown to effectively reduc the amount of time required while conserving memory space as a group of queries is processed by the database.},
	keywords = {multi-query optimization, Scientific databases},
	isbn = {1-58113-839-3},
	doi = {10.1145/1006209.1006224},
	url = {http://doi.acm.org/10.1145/1006209.1006224},
	author = {Aryangat,Suresh and Andrade,Henrique and Sussman, Alan}
}
@article {17864,
	title = {The design and evaluation of a high-performance earth science database},
	journal = {Parallel Computing},
	volume = {24},
	year = {1998},
	month = {1998/01//},
	pages = {65 - 89},
	abstract = {Earth scientists have encountered two major obstacles in their attempts to use remotely sensed imagery to analyze the earth{\textquoteright}s land cover dynamics. First, the volume of data involved is very large and second, significant preprocessing is needed before the data can be used. This is particularly so for studies that analyze global trends using data sets that cover multiple years. In this paper, we present the design of an earth science database as well as our early experiences with it. The primary design goal of this database is to facilitate efficient access to and preprocessing of large volumes of satellite data. Our initial design assumed that the main bottleneck in the system would be retrieving data from the disks. However, experimental results show that precise identification of all the data values corresponding to a query can take a significant amount of time. The problem is even more pronounced in designing the system to attempt to minimize time spent performing I/O. We therefore discuss a major redesign of the system that includes a reworking of the indexing scheme and a reorganization of the data on disks. Experimental results show that the redesigned system performs significantly better than the original system, providing interactive response times for local queries.},
	keywords = {Communication, High-performance I/O, load balancing, scalability, Scientific databases},
	isbn = {0167-8191},
	doi = {10.1016/S0167-8191(97)00117-8},
	url = {http://www.sciencedirect.com/science/article/pii/S0167819197001178},
	author = {Shock,Carter T. and Chang,Chialin and Moon,Bongki and Acharya,Anurag and Davis, Larry S. and Saltz,Joel and Sussman, Alan}
}
