@conference {17880,
	title = {Optimizing retrieval and processing of multi-dimensional scientific datasets},
	booktitle = {Parallel and Distributed Processing Symposium, 2000. IPDPS 2000. Proceedings. 14th International},
	year = {2000},
	month = {2000///},
	pages = {405 - 410},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We have developed the Active Data Repository (ADR), an infrastructure that integrates storage, retrieval, and processing of large multi-dimensional scientific datasets on distributed memory parallel machines with multiple disks attached to each node. In earlier work, we proposed three strategies for processing range queries within the ADR framework. Our experimental results show that the relative performance of the strategies changes under varying application characteristics and machine configurations. In this work we investigate approaches to guide and automate the selection of the best strategy for a given application and machine configuration. We describe analytical models to predict the relative performance of the strategies where input data elements are uniformly distributed in the attribute space of the output dataset, restricting the output dataset to be a regular d-dimensional array},
	keywords = {active data repository, Area measurement, Computer science, Data analysis, distributed memory parallel machines, Educational institutions, Information retrieval, Information Storage and Retrieval, infrastructure, Microscopy, Microwave integrated circuits, multi-dimensional scientific datasets retrieval, PARALLEL PROCESSING, Pathology, range queries, regular d-dimensional array, Satellites, Tomography},
	isbn = {0-7695-0574-0},
	doi = {10.1109/IPDPS.2000.846013},
	author = {Chang,Chialin and Kurc, T. and Sussman, Alan and Saltz, J.}
}
@article {16330,
	title = {Empirically guided software development using metric-based classification trees},
	journal = {IEEE Software},
	volume = {7},
	year = {1990},
	month = {1990/03//},
	pages = {46 - 54},
	abstract = {The identification of high-risk components early in the life cycle is addressed. A solution that casts this as a classification problem is examined. The proposed approach derives models of problematic components, based on their measurable attributes and those of their development processes. The models provide a basis for forecasting which components are likely to share the same high-risk properties, such as being error-prone or having a high development cost. Developers can use these classification techniques to localize the troublesome 20\% of the system. The method for generating the models, called automatic generation of metric-based classification trees, uses metrics from previous releases or projects to identify components that are historically high-risk.},
	keywords = {Application software, Area measurement, automatic generation, classification problem, Classification tree analysis, Costs, empirically guided software development, Error correction, life cycle, measurable attributes, metric-based classification trees, Predictive models, Programming, software engineering, Software measurement, software metrics, Software systems},
	isbn = {0740-7459},
	doi = {10.1109/52.50773},
	author = {Porter, Adam and Selby,R. W}
}
