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- Alok Aggarwal, Jeffrey Scott Vitter
- Commun. ACM
- 1988

We provide tight upper and lower bounds, up to a constant factor, for the number of inputs and outputs (I/OS) between internal memory and secondary storage required for five sorting-related problems: sorting, the fast Fourier transform (FFT), permutation networks, permuting, and matrix transposition. The bounds hold both in the worst case and in the average… (More)

- Roberto Grossi, Ankur Gupta, Jeffrey Scott Vitter
- SODA
- 2003

We present a novel implementation of compressed suffix arrays exhibiting new tradeoffs between search time and space occupancy for a given text (or sequence) of <i>n</i> symbols over an alphabet σ, where each symbol is encoded by lg|σ| bits. We show that compressed suffix arrays use just nH<inf>h</inf> + σ bits, while… (More)

- Jeffrey Scott Vitter, Elizabeth A. M. Shriver
- Algorithmica
- 1994

We provide the first optimal algorithms in terms of the number of input/outputs (I/Os) required between internal memory and multiple secondary storage devices for the problems of sorting, FFT, matrix transposition, standard matrix multiplication, and related problems. Our two-level memory model is new and gives a realistic treatmentof parallel block… (More)

- Jeffrey Scott Vitter
- ACM Trans. Math. Softw.
- 1985

We introduce fast algorithms for selecting a random sample of <italic>n</italic> records without replacement from a pool of <italic>N</italic> records, where the value of <italic>N</italic> is unknown beforehand. The main result of the paper is the design and analysis of Algorithm Z; it does the sampling in one pass using constant space and in… (More)

- Roberto Grossi, Jeffrey Scott Vitter
- SIAM J. Comput.
- 2000

The proliferation of online text, such as on the World Wide Web and in databases, motivates the need for space-efficient index methods that support fast search. Consider a text T of n binary symbols to index. Given any query pattern P of m binary symbols, the goal is to search f?r P in T quickly, with T being fully scanned only once, nafiaely, when the… (More)

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)

- Jeffrey Scott Vitter
- ACM Comput. Surv.
- 1998

Data sets in large applications are often too massive to fit completely inside the computers internal memory. The resulting input/output communication (or I/O) between fast internal memory and slower external memory (such as disks) can be a major performance bottleneck. In this article we survey the state of the art in the design and analysis of external… (More)

- Yossi Matias, Jeffrey Scott Vitter, Min Wang
- SIGMOD Conference
- 1998

Query optimization is an integral part of relational database management systems. One important task in query optimization is selectivity estimation, that is, given a query <italic>P</italic>, we need to estimate the fraction of records in the database that satisfy <italic>P</italic>. Many commercial database systems maintain histograms to approximate the… (More)

- Lars Arge, Jeffrey Scott Vitter
- SIAM J. Comput.
- 2003

In this paper we present the external interval tree, an optimal external memory data structure for answering stabbing queries on a set of dynamically maintained intervals. The external interval tree can be used in an optimal solution to the dynamic interval management problem, which is a central problem for object-oriented and temporal databases and for… (More)

- Jeffrey Scott Vitter, Min Wang
- SIGMOD Conference
- 1999

Computing multidimensional aggregates in high dimensions is a performance bottleneck for many OLAP applications. Obtaining the exact answer to an aggregation query can be prohibitively expensive in terms of time and/or storage space in a data warehouse environment. It is advantageous to have fast, approximate answers to OLAP aggregation queries.
In this… (More)