Darren Erik Vengroff

External-memory computational geometry

In this paper we give new techniques for designing efficient 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 large-scale problems. We discuss our algorithms primarily in the context of single processor/single disk machines, a domain in which they are not only the first known optimal results but also of tremendous practical value. Our methods also produce the first known optimal algorithms for a wide range of two-level and hierarchical multilevel memory models, including parallel models. The algorithms are optimal both in terms of I/O cost and internal computation.<<ETX>>

Indexing for Data Models with Constraints and Classes

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

External-memory algorithms for processing line segments in geographic information systems

n

Efficient 3-D range searching in external memory

be the size of the database,

Indexing for data models with constraints and classes (extended abstract)

c

Supporting I/O-efficient scientific computation in TPIE

the number of classes,

Partial sampling with reverse state reconstruction: A new technique for branch predictor performance estimation

B

External-memory graph algorithms

the secondary storage page size, and

Indexing for Data Models with Constraints and Classes.

t

I/O-Efficient Scientific Computation Using TPIE

the size of the output of a query. Indexing by one attribute in the constraint data model (for a fairly general type of constraints) is equivalent to external dynamic interval management, which is a special case of external dynamic 2-dimensional range searching. We present a semi-dynamic data structure for this problem which has optimal worst-case space