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

Learning Multi-dimensional Indexes

Scanning and filtering over multi-dimensional tables are key operations in modern analytical database engines. To optimize the performance of these operations, databases often create clustered indexes over a single dimension or multi-dimensional indexes such as R-trees, or use complex sort orders (e.g., Z-ordering). However, these schemes are often hard to tune and their performance is inconsistent across different datasets and queries. In this paper, we introduce Flood, a multi-dimensional in-memory index that automatically adapts itself to a particular dataset and workload by jointly optimizing the index structure and data storage. Flood achieves up to three orders of magnitude faster performance for range scans with predicates than state-of-the-art multi-dimensional indexes or sort orders on real-world datasets and workloads. Our work serves as a building block towards an end-to-end learned database system

Multidimensional Data Structures and their Benchmarking

Cílem této práce je nastudovat existující implementace (knihovny, balíčky, . . . ) vícerozměrných datových struktur a porovnat jejich možnosti a výkon s vícerozměrnými datovými strukturami implementovanými v databázovém frameworku RadegastDB, vyvíjeném na Katedře informatiky Vysoké školy báňské - Technické univerzity Ostrava. Součástí práce je vytvoření webové aplikace, která zvolené knihovny bude testovat a výsledky testů vizualizuje a uchová v databázi pro další analýzu. Poslední částí práce je implementace vybraného rozšíření databázového frameworku RadegastDB a její porovnání s implementacemi ostatních knihoven.Main purpose of this thesis is studying of existing implementations (libraries, packages, . . . ) of multidimensional data structures and comparing of their abilities and power with multidimensional data structures implemented in database framework RadegastDB, developed by Department of Computer Science at the VŠB - Technical University Ostrava. Part of this work includes creation of web application that tests choosen libraries and displays results of testing and store these results in database for the next analysis. The last part is implementation of choosen extension of database framework RadegastDB and its comparing with implementations of other libraries.460 - Katedra informatikyvýborn

Compression Techniques for the Grid File

Computing and Information Scienc

A Survey on Spatial Indexing

Spatial information processing has been a centre of attention of research in the previous decade. In spatial databases, data related with spatial coordinates and extents are retrieved based on spatial proximity. A large number of spatial indexes have been proposed to make ease of efficient indexing of spatial objects in large databases and spatial data retrieval. The goal of this paper is to review the advance techniques of the access methods. This paper tries to classify the existing multidimensional access methods, according to the types of indexing, and their performance over spatial queries. K-d trees out performs quad tress without requiring additional memory usage

Cracking KD-Tree : the first multidimensional adaptive indexing

Orientador: Prof. Dr. Eduardo Cunha de AlmeidaDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 03/10/2018Inclui referências: p. 48-51Área de concentração: Ciência da ComputaçãoResumo: A criação de índices é um das decisões mais difíceis no processo de criação de esquemas em bancos de dados. Dada uma carga de trabalho, o administrador do banco de dados precisa decidir quais índices criar levando em consideração os custos para construção e manutenção deles. Esse problema se torna ainda mais difícil quando é necessário lidar buscas em múltiplas dimensões em sistemas exploratórios, onde não se tem uma carga de trabalho disponível e o número de possíveis índices é ainda maior. Técnicas de indexação adaptativas, como Sideways Cracking e Quasii, são capazes de responder buscas de intervalo em múltiplas dimensões. Nessa dissertação nós propomos uma alternativa, a Cracking KD-Tree, que é uma estrutura de dados adaptativa usada para buscas em múltiplas dimensões. Comparando-a com outras técnicas adaptativas de indexação, nossa estrutura de dados teve eficiência melhor ou comparável, com respeito a tempo total de resposta para executar a carga de trabalho. Com 2 atributos nós fomos 6.7x mais rápidos que o Sideways Cracking e 1.4x que o Quasii. Com 16 atributos, a Cracking KD-Tree foi 19x mais rápida que o Sideways Cracking e 1.7x mais rápida que o Quasii. Palavras-chave: Particionamento de Banco de Dados. Índice Multidimensional. Banco de Dados.Abstract: Index creation is one of the main difficult decisions in database schema design. Given a workload, the database administrator has to decide which indexes to create taking into consideration the costs to build and maintain them. This problem becomes even more difficult when dealing with multidimensional queries in exploratory systems, where there is no workload available and the number of possible indexes is bigger. State of the art adaptive indexing techniques, such as Sideways Cracking and Quasii, are capable of answering multidimensional range queries. In this dissertation we propose an alternative, the Cracking KD-Tree, which is an adaptive data structure used for multidimensional queries. Comparing it with other adaptive indexing techniques, our data structure had more or comparable efficiency with respect to total workload response time. With 2 attributes we were 6.7x faster than Sideways Cracking and 1.4x than Quasii. With 16 attributes, the Cracking KD-Tree was 19x faster than Sideways Cracking and 1.7x faster than Quasii. Keywords: Database Cracking. Multidimensional Index. Database Systems

Grid File Approach to Large Multidimensional Dynamic Data Structures

Computing and Information Science

New Efficient Spatial Index Structures, PML-Tree and SMR-Tree, for Spatial Databases

Computer Scienc