Darren Erik Vengroff

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Michael T. GoodrichjS Edward F. Grovefll Roberto Tamassia*t Darren Erik Vengrofftt Jeffrey Scott Vitterqt We present a collection of new techniques for designing and analyzing efficient external-memory algorithms for graph problems and illustrate how these techniques can be applied to a wide variety of specific problems. Our results include:(More)
We examine I/O-efficient data structures that provide indexing support for new data models. The database languages of these models include concepts from constraint programming (e.g., relational tuples are generalized to conjunctions of constraints) and from object-oriented programming (e.g., objects are organized in class hierarchies). Let(More)
In the design of algorithms for large-scale applications it is essential to consider the problem of minimizing I/O communication. Geographical information systems (GIS) are good examples of such large-scale applications as they frequently handle huge amounts of spatial data. In this paper we develop efficient external-memory algorithms for a number of(More)
In this paper we give new techniques for designing e cient algorithms for computational geometry problems that are too large to be solved in internal memory. We use these techniques to develop optimal and practical algorithms for a number of important largescale problems. We discuss our algorithms primarily in the context of single processor/single disk(More)
Exploring the design space of branch predictors can consume tremendous computational resources. In order to mitigate this problem we present a new nonclustered sampling technique for rapidly evaluating the performance of a large number of branch predictors in a single rapid pass through a trace. The predictors studied in this single pass need not closely(More)
In this paper we introduce RecLab, a system designed to enable developers to build and test recommendation algorithms for eCommerce websites. RecLab supports a variety of context and feedback that recommenders can take advantage of to improve the quality of their recommendations. RecLab is unique in that recommenders built on top of RecLab APIs can run in(More)
We present a novel approach to fully dynamic management of physical disk blocks in Unix file systems. By adding a single system call, zero, to an existing file system, we permit applications to create holes, that is, regions of files to which no physical disk blocks are allocated, far more flexibly than previously possible. zero can create holes in the(More)