Spatial Temporal Trajectory Similarity Join

Dan, Tangpeng; Luo, Changyin; Li, Yanhong; Zheng, Bolong; Li, Guohui

doi:10.1007/978-3-030-26075-0_19

Cited by 8 publications

(6 citation statements)

References 5 publications

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“…It does not degrade over time and remains balanced. The structure consists of the tree in which each path from the root to the leaf has the same length [17]. Three node types are present: root, internal node, and leaf node.…”

Section: Index Structures and Access Methodsmentioning

confidence: 99%

“…B+tree index approach is an extension on the leaf layer, where individual nodes are chained together, forming a linked list. Thus, such a layer holds sorted data based on the attributes, which are there indexed [16,17]. B+tree index approach is an extension on the leaf layer, where individual nodes are chained together, forming a linked list.…”

Section: Index Structures and Access Methodsmentioning

confidence: 99%

“…Other index techniques mostly arise from this category and improve either modeled data group or data shapes to be processed, like inverted B+tree key, its unique version, table, or cluster index. Different structural approaches are based on a bitmap or hash indexes, which cannot be, however, universally used due to specific requirements and limitations [16][17][18].…”

Section: Index Structures and Access Methodsmentioning

confidence: 99%

“…From the physical implementation, it can be defined either as a varray, nested table as the table attribute or by the table itself. Based on the performance evaluation, the most suitable solution provides the direct table [17,18]. The reason is mostly based on indexing.…”

Section: Log Buffermentioning

confidence: 99%

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Data Block and Tuple Identification Using Master Index

Kvet

2020

Sensors

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Relational databases are still very often used as a data storage, even for the sensor oriented data. Each data tuple is logically stored in the table referenced by relationships between individual tables. From the physical point of view, data are stored in the data files delimited by the tablespaces. Files are block-oriented. When retrieving data, particular blocks must be identified and transferred into the memory for the evaluation and processing. This paper deals with storage principles and proposes own methods for effective data block location and identification if no suitable index for the query is present in the system. Thanks to that, the performance of the whole system is optimized, and the processing time and costs are significantly lowered. The proposed solution is based on the master index, which points just to the blocks with relevant data. Thus, no sequential block scanning is necessary for consuming many system resources. The paper analyzes the impact of block size, which can have a significant impact on sensor oriented data, as well.

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Section: Index Structures and Access Methodsmentioning

confidence: 99%

Section: Index Structures and Access Methodsmentioning

confidence: 99%

Section: Index Structures and Access Methodsmentioning

confidence: 99%

Section: Log Buffermentioning

confidence: 99%

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Data Block and Tuple Identification Using Master Index

Kvet

2020

Sensors

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“…Zhang et al [39] studied the spatial keyword join query problem under MapReduce framework, proposed the spatial text object filtering algorithm based on the combination of prefix filtering and grid partitioning technology, and proposed two optimization methods which can improve the performance of spatial keyword join query. Dan et al [40] mainly focused on spatialtemporal trajectory similarity join problem and proposed a novel two-level grid index which takes both spatial and temporal information into account. Zhu et al [41] were the first to exploit the Spatial Visual Similarity Join problem for Geo-Multimedia aiming to find similar geo-image pairs in both aspects of geo-location and visual content.…”

Section: Spatial Data Similarity Joinmentioning

confidence: 99%

Projection Based Large Scale High-Dimensional Data Similarity Join Using MapReduce Framework

Zhang

Cui

et al. 2020

IEEE Access

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Similarity join has been widely used in many data analysis and data mining applications, we mainly focus on the scalability and performance problem of similarity join query on massive highdimensional data set. p-stable distribution based projection scheme can implement dimension reduction effectively. Three novel approaches based on projection scheme are proposed to deal with massive highdimensional data similarity join problem: Single projection method, Multiple projection method and Projection space partitioning method. Comprehensive experimental tests were performed to evaluate the performance of the above approaches. The experimental results show that the proposed approaches in this paper have good performance and scalability.

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Flower Master Index for Relational Database Selection and Joining

Kvet

2020

Towards Digital Intelligence Society

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Spatial Temporal Trajectory Similarity Join

Cited by 8 publications

References 5 publications

Data Block and Tuple Identification Using Master Index

Data Block and Tuple Identification Using Master Index

Projection Based Large Scale High-Dimensional Data Similarity Join Using MapReduce Framework

Flower Master Index for Relational Database Selection and Joining

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