57 research outputs found
A Robust Scheme for Multilevel Extendible Hashing
Dynamic hashing, while surpassing other access methods for uniformly distributed data, usually performs badly for non-uniformly distributed data. We propose a robust scheme for multi-level extendible hashing allowing efficient processing of skewed data as well as uniformly distributed data. In order to test our access method we implemented it and compared it to several existing hashing schemes. The results of the experimental evaluation demonstrate the superiority of our approach in both index size and performance
Performance comparison of point and spatial access methods
In the past few years a large number of multidimensional point access methods, also called
multiattribute index structures, has been suggested, all of them claiming good performance. Since no
performance comparison of these structures under arbitrary (strongly correlated nonuniform, short
"ugly") data distributions and under various types of queries has been performed, database
researchers and designers were hesitant to use any of these new point access methods. As shown in
a recent paper, such point access methods are not only important in traditional database applications.
In new applications such as CAD/CIM and geographic or environmental information systems, access
methods for spatial objects are needed. As recently shown such access methods are based on point
access methods in terms of functionality and performance. Our performance comparison naturally
consists of two parts. In part I we w i l l compare multidimensional point access methods, whereas in
part I I spatial access methods for rectangles will be compared. In part I we present a survey and
classification of existing point access methods. Then we carefully select the following four methods
for implementation and performance comparison under seven different data files (distributions) and
various types of queries: the 2-level grid file, the BANG file, the hB-tree and a new scheme, called
the BUDDY hash tree. We were surprised to see one method to be the clear winner which was the
BUDDY hash tree. It exhibits an at least 20 % better average performance than its competitors and is
robust under ugly data and queries. In part I I we compare spatial access methods for rectangles.
After presenting a survey and classification of existing spatial access methods we carefully selected
the following four methods for implementation and performance comparison under six different data
files (distributions) and various types of queries: the R-tree, the BANG file, PLOP hashing and the
BUDDY hash tree. The result presented two winners: the BANG file and the BUDDY hash tree.
This comparison is a first step towards a standardized testbed or benchmark. We offer our data and
query files to each designer of a new point or spatial access method such that he can run his
implementation in our testbed
Advance of the Access Methods
The goal of this paper is to outline the advance of the access methods in the last ten years as well as
to make review of all available in the accessible bibliography methods
Online Data Structures in External Memory
The original publication is available at www.springerlink.comThe data sets for many of today's computer applications are
too large to t within the computer's internal memory and must instead
be stored on external storage devices such as disks. A major performance
bottleneck can be the input/output communication (or I/O) between
the external and internal memories. In this paper we discuss a variety of
online data structures for external memory, some very old and some very
new, such as hashing (for dictionaries), B-trees (for dictionaries and 1-D
range search), bu er trees (for batched dynamic problems), interval trees
with weight-balanced B-trees (for stabbing queries), priority search trees
(for 3-sided 2-D range search), and R-trees and other spatial structures.
We also discuss several open problems along the way
Signature Files: An Integrated Access Method for Formatted and Unformatted Databases
The signature file approach is one of the most powerful information storage and retrieval techniques which is used for finding the data objects that are relevant to the user queries. The main idea of all signature based schemes is to reflect the essence of the data items into bit pattern (descriptors or signatures) and store them in a separate file which acts as a filter to eliminate the non aualifvine data items for an information reauest. It provides an integrated access method for both formattid and formatted databases. A complative
overview and discussion of the proposed signatnre generation methods and the major signature file organization schemes are presented. Applications of the signature techniques to formatted and unformatted databases, single and multiterm query cases, serial and paratlei architecture. static and dynamic environments are provided with a special emphasis on the multimedia databases where the pioneering prototype systems
using signatnres yield highly encouraging results
Main Memory Implementations for Binary Grouping
An increasing number of applications depend on efficient storage and analysis features for XML data. Hence, query optimization and efficient evaluation techniques for the emerging XQuery standard become more and more important. Many XQuery queries require nested expressions. Unnesting them often introduces binary grouping. We introduce several algorithms implementing binary grouping and analyze their time and space complexity. Experiments demonstrate their performance
Dynamic Spanning Trees for Connectivity Queries on Fully-dynamic Undirected Graphs (Extended version)
Answering connectivity queries is fundamental to fully dynamic graphs where
edges and vertices are inserted and deleted frequently. Existing work proposes
data structures and algorithms with worst-case guarantees. We propose a new
data structure, the dynamic tree (D-tree), together with algorithms to
construct and maintain it. The D-tree is the first data structure that scales
to fully dynamic graphs with millions of vertices and edges and, on average,
answers connectivity queries much faster than data structures with worst case
guarantees
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