eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-07-01
4301
1
14
10.4230/DagSemProc.04301.1
article
04301 Abstracts Collection – Cache-Oblivious and Cache-Aware Algorithms
Arge, Lars
Bender, Michael A.
Demaine, Erik
Leiserson, Charles
Mehlhorn, Kurt
The Dagstuhl Seminar 04301 ``Cache-Oblivious and Cache-Aware Algorithms'' was held
in the International Conference and Research Center (IBFI), Schloss Dagstuhl, from 18.07.2004 to 23.07.2004.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04301/DagSemProc.04301.1/DagSemProc.04301.1.pdf
Cache oblivious
cache aware
external memory
I/O-efficient algorithms
data structures
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-07-01
4301
1
2
10.4230/DagSemProc.04301.2
article
A Simple Algorithm for I/O-efficiently Pruning Dense Spanners
Gudmundsson, Joachim
Vahrenhold, Jan
Given a geometric graph $G=(S,E)$ in $R^d$ with
constant dilation $t$, and a positive constant
$\epsilon$, we show how to construct a
$(1+\epsilon)$-spanner of $G$ with $O(|S|)$ edges
using $O(sort(|E|))$ I/O operations.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04301/DagSemProc.04301.2/DagSemProc.04301.2.pdf
No keywords
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-07-01
4301
1
26
10.4230/DagSemProc.04301.3
article
The Priority R-Tree: A Practically Efficient and Worst-Case-Optimal R-Tree
Arge, Lars
de Berg, Mark
Haverkort, Herman J.
Yi, Ke
The query efficiency of a data structure that stores a set of objects, can normally be assessed by analysing the number of objects, pointers etc. looked at when answering a query. However, if the data structure is too big to fit in main memory, data may need to be fetched from disk. In that case, the query efficiency is easily dominated by moving the disk head to the correct locations, rather than by reading the data itself.
To reduce the number of disk accesses, once can group the data into blocks, and strive to bound the number of different blocks accessed rather than the number of individual data objects read. An R-tree is a general-purpose data structur that stores a hierarchical grouping of geometric objects into blocks. Many heuristics have been designed to determine which objects should be grouped together, but none of these heuristics could give a guarantee on the resulting worst-case query time.
We present the Priority R-tree, or PR-tree, which is the first R-tree variant that always answers a window query by accessing $O((N/B)^{1-1/d} + T/B)$ blocks, where $N$ is the number of $d$-dimensional objects stored, $B$ is the number of objects per block, and $T$ is the number of objects whose bounding boxes intersect the query window. This is provably asymptotically optimal. Experiments show that the PR-tree performs similar to the best known heuristics on real-life and relatively nicely distributed data, but outperforms them significantly on more extreme data.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04301/DagSemProc.04301.3/DagSemProc.04301.3.pdf
R-Trees