Index structures for flexible querying in fuzzy spatial databases

2005 ◽  
Author(s):  
A. Sozer ◽  
A. Yazici
Keyword(s):  
Spatial Databases ◽  
Index Structures ◽  
Flexible Querying

Spatial databases for the geology of the northern Rocky Mountains - Idaho, Montana, and Washington

2005 ◽  
Author(s):  
Michael L. Zientek ◽  
Pamela D. Derkey ◽  
Robert J. Miller ◽  
J. Douglas Causey ◽  
Arthur A. Bookstrom ◽  
...  
Keyword(s):  
Rocky Mountains ◽  
Spatial Databases ◽  
Northern Rocky Mountains

Feasibility of Longest Prefix Matching using Learned Index Structures

2021 ◽  
Vol 48 (4) ◽  
pp. 45-48
Author(s):  
Shunsuke Higuchi ◽  
Junji Takemasa ◽  
Yuki Koizumi ◽  
Atsushi Tagami ◽  
Toru Hasegawa
Keyword(s):  
Machine Learning ◽  
Data Structure ◽  
Index Structures ◽  
Packet Forwarding ◽  
Fundamental Idea ◽  
Search Operation ◽  
Longest Prefix Matching ◽  
Ip Packet ◽  
Computation Speed

This paper revisits longest prefix matching in IP packet forwarding because an emerging data structure, learned index, is recently presented. A learned index uses machine learning to associate key-value pairs in a key-value store. The fundamental idea to apply a learned index to an FIB is to simplify the complex longest prefix matching operation to a nearest address search operation. The size of the proposed FIB is less than half of an existing trie-based FIB while it achieves the computation speed nearly equal to the trie-based FIB. Moreover, the computation speed of the proposal is independent of the length of IP prefixes, unlike trie-based FIBs.

scholarly journals Efficient Retrieval of Music Recordings Using Graph-Based Index Structures

Signals ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 336-352
Author(s):  
Frank Zalkow ◽  
Julian Brandner ◽  
Meinard Müller
Keyword(s):  
Nearest Neighbor ◽  
Response Times ◽  
Negative Impact ◽  
Nearest Neighbor Search ◽  
Index Structure ◽  
Search Problem ◽  
Index Structures ◽  
Music Retrieval ◽  
Retrieval Systems ◽  
Music Recordings

Flexible retrieval systems are required for conveniently browsing through large music collections. In a particular content-based music retrieval scenario, the user provides a query audio snippet, and the retrieval system returns music recordings from the collection that are similar to the query. In this scenario, a fast response from the system is essential for a positive user experience. For realizing low response times, one requires index structures that facilitate efficient search operations. One such index structure is the K-d tree, which has already been used in music retrieval systems. As an alternative, we propose to use a modern graph-based index, denoted as Hierarchical Navigable Small World (HNSW) graph. As our main contribution, we explore its potential in the context of a cross-version music retrieval application. In particular, we report on systematic experiments comparing graph- and tree-based index structures in terms of the retrieval quality, disk space requirements, and runtimes. Despite the fact that the HNSW index provides only an approximate solution to the nearest neighbor search problem, we demonstrate that it has almost no negative impact on the retrieval quality in our application. As our main result, we show that the HNSW-based retrieval is several orders of magnitude faster. Furthermore, the graph structure also works well with high-dimensional index items, unlike the tree-based structure. Given these merits, we highlight the practical relevance of the HNSW graph for music information retrieval (MIR) applications.

scholarly journals Selective caching: a persistent memory approach for multi-dimensional index structures

2021 ◽  
Author(s):  
Muhammad Attahir Jibril ◽  
Philipp Götze ◽  
David Broneske ◽  
Kai-Uwe Sattler
Keyword(s):  
Main Memory ◽  
Index Structure ◽  
Index Structures ◽  
Cloud Infrastructure ◽  
General Technique ◽  
Persistent Memory ◽  
The Cost ◽  
Cloud Applications ◽  
Memory Layout ◽  
Analytical Index

AbstractAfter the introduction of Persistent Memory in the form of Intel’s Optane DC Persistent Memory on the market in 2019, it has found its way into manifold applications and systems. As Google and other cloud infrastructure providers are starting to incorporate Persistent Memory into their portfolio, it is only logical that cloud applications have to exploit its inherent properties. Persistent Memory can serve as a DRAM substitute, but guarantees persistence at the cost of compromised read/write performance compared to standard DRAM. These properties particularly affect the performance of index structures, since they are subject to frequent updates and queries. However, adapting each and every index structure to exploit the properties of Persistent Memory is tedious. Hence, we require a general technique that hides this access gap, e.g., by using DRAM caching strategies. To exploit Persistent Memory properties for analytical index structures, we propose selective caching. It is based on a mixture of dynamic and static caching of tree nodes in DRAM to reach near-DRAM access speeds for index structures. In this paper, we evaluate selective caching on the OLAP-optimized main-memory index structure Elf, because its memory layout allows for an easy caching. Our experiments show that if configured well, selective caching with a suitable replacement strategy can keep pace with pure DRAM storage of Elf while guaranteeing persistence. These results are also reflected when selective caching is used for parallel workloads.

Review on Renewable Energy Potential in Australian Subtropical Region (Central and North Queensland)

2011 ◽  
Vol 347-353 ◽  
pp. 3846-3855 ◽  
Author(s):  
Ali Baniyounes ◽  
Gang Liu ◽  
M. G. Rasul ◽  
M. M. K. Khan
Keyword(s):  
Renewable Energy ◽  
Wind Speed ◽  
Spatial Databases ◽  
Cost Effective ◽  
Climatic Conditions ◽  
Energy Resources ◽  
Climatic Data ◽  
Renewable Energy Resources ◽  
Subtropical Region ◽  
Energy Potential

In Australia the future demand for energy is predicted to increase rapidly. Conventional energy resources soaring prices and environmental impact have increased the interest in renewable energy technology. As a result of that the Australian government is promoting renewable energy; such as wind, geothermal, solar and hydropower. These types of energy are believed to be cost-effective and environmentally friendly. Renewable energy availability is controlled by climatic conditions such as solar radiation, wind speed and temperature. This paper aims to assess the potential of renewable energy resources, in particular wind and solar energy in an Australian subtropical region (Central and North Queensland) namely, Gladstone, Emerald, Rockhampton, Yeppoon, Townsville, and Cairns. Analysis is done by using the latest statistical state of Queensland energy information, along with measured data history of wind speed, solar irradiations, air temperature, relative humidity, and atmospheric pressure for those sites. This study has also shown that national assessments of solar and wind energy potential can be improved by improving local climatic data assessments using spatial databases of Central and North Queensland areas.

scholarly journals Optimal-Location-Selection Query Processing in Spatial Databases

2009 ◽  
Vol 21 (8) ◽  
pp. 1162-1177 ◽  
Author(s):  
Yunjun Gao ◽  
Baihua Zheng ◽  
Gencai Chen ◽  
Qing Li
Keyword(s):  
Query Processing ◽  
Spatial Databases ◽  
Optimal Location ◽  
Location Selection
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