MR-Tree: A Cache-Conscious Main Memory Spatial Index Structure for Mobile GIS

2005 ◽  
pp. 167-180 ◽  
Author(s):  
Kyung-Chang Kim ◽  
Suk-Woo Yun
Keyword(s):  
Main Memory ◽  
Spatial Index ◽  
Index Structure ◽  
Mobile Gis

scholarly journals A Hybrid Approach Combining R*-Tree and k-d Trees to Improve Linked Open Data Query Performance

2021 ◽  
Vol 11 (5) ◽  
pp. 2405
Author(s):  
Yuxiang Sun ◽  
Tianyi Zhao ◽  
Seulgi Yoon ◽  
Yongju Lee
Keyword(s):  
Flash Memory ◽  
Query Language ◽  
Hybrid Approach ◽  
Open Data ◽  
Main Memory ◽  
Linked Open Data ◽  
Index Structure ◽  
Identification Algorithm ◽  
Distributed Computing Systems ◽  
Query Performance

Semantic Web has recently gained traction with the use of Linked Open Data (LOD) on the Web. Although numerous state-of-the-art methodologies, standards, and technologies are applicable to the LOD cloud, many issues persist. Because the LOD cloud is based on graph-based resource description framework (RDF) triples and the SPARQL query language, we cannot directly adopt traditional techniques employed for database management systems or distributed computing systems. This paper addresses how the LOD cloud can be efficiently organized, retrieved, and evaluated. We propose a novel hybrid approach that combines the index and live exploration approaches for improved LOD join query performance. Using a two-step index structure combining a disk-based 3D R*-tree with the extended multidimensional histogram and flash memory-based k-d trees, we can efficiently discover interlinked data distributed across multiple resources. Because this method rapidly prunes numerous false hits, the performance of join query processing is remarkably improved. We also propose a hot-cold segment identification algorithm to identify regions of high interest. The proposed method is compared with existing popular methods on real RDF datasets. Results indicate that our method outperforms the existing methods because it can quickly obtain target results by reducing unnecessary data scanning and reduce the amount of main memory required to load filtering results.

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.

scholarly journals Efficient Geo-Textual Hybrid Indexing Techniques for Moving Objects and Queries

2020 ◽  
Vol 8 (6) ◽  
pp. 4419-4428
Keyword(s):  
Moving Objects ◽  
Traffic Monitoring ◽  
Spatial Index ◽  
Index Structure ◽  
Maintenance Cost ◽  
Hybrid Index ◽  
Indexing Techniques ◽  
Inverted List ◽  
Textual Data ◽  
Future Position

Advancements of various Geographic Information Technologies have resulted in huge growth in Geo-Textual data. Many Indexing and searching algorithms are developed to handle this Geo-Textual data which contains spatial, textual and temporal information. In past, Indexing and searching algorithms are developed for the applications in which the object trajectory or velocity vector is known in advance and hence we can predict the future position of the objects. There are real time applications like emergency management systems, traffic monitoring, where the objects movements are unpredictable and hence future position of the objects cannot be predicted. Techniques are required to answer the geo-textual kNN query where the velocity vectors or trajectories of moving and moving queries are not known. In case of moving objects, capturing current position of the object and maintaining spatial index optimally is very much essential. The hybrid indexing techniques used earlier are based on R-tree spatial index. The nodes of the R-tree index structure are split or merged to maintain the locations of continuously moving objects, increasing the maintenance cost as compared to the grid index. In this paper a solution is proposed for creating and maintaining hybrid index for moving objects and queries based on grid and inverted list hybrid indexing techniques. The method is also proposed for finding Geo-Textual nearest neighbours for static and moving queries using hybrid index and conceptual partitioning of the grid. The overall gain reported by the experimental work using hybrid index over the non- hybrid index is 30 to 40 percent depending on the grid size chosen for mapping the data space and on the parameters of queries.

Study and Optimization of T-Tree Index in Main Memory Database

2013 ◽  
Vol 427-429 ◽  
pp. 2531-2535 ◽  
Author(s):  
Feng Dong Sun ◽  
Quan Guo ◽  
Lan Wang
Keyword(s):  
High Performance ◽  
Main Memory ◽  
Memory Access ◽  
Index Structure ◽  
Index Structures ◽  
Clock Speed ◽  
Main Memory Database ◽  
Tree Index ◽  
Overall Performance

The bottleneck is not the disk I/O but CUP clock speed faster than the memory speed in main memory database .In order to achieve high performance in main memory database ,it is a good approach to design new index structures to improve the memory access speed .This chapter presents a T-tree index structure and its algorithms in main memory database firstly .Then presents two results on Optimization of T-tree index ,including T-tail tree and TTB-tree. Our results indicate that the T-Tree provides good overall performance in main memory.

scholarly journals An Index Structure for Main-memory Storage Systems using The Level Pre-fetching

2007 ◽  
Vol 3 (1) ◽  
pp. 19-23
Author(s):  
Seok-Jae Lee ◽  
Jong-Hyun Yoon ◽  
Seok-Il Song ◽  
Jae-Soo Yoo
Keyword(s):  
Storage Systems ◽  
Main Memory ◽  
Memory Storage ◽  
Index Structure
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