An Improved Spatio-temporal Index Structure for Predictive Queries

2009 ◽  
Author(s):  
Meng Gao ◽  
Yingyuan Xiao
Keyword(s):  
Index Structure ◽  
Temporal Index ◽  
Spatio Temporal

scholarly journals An Adaptive Construction Method of Hierarchical Spatio-Temporal Index for Vector Data under Peer-to-Peer Networks

2019 ◽  
Vol 8 (11) ◽  
pp. 512 ◽  
Author(s):  
Li ◽  
Wu ◽  
Wu ◽  
Zhao
Keyword(s):  
Data Storage ◽  
High Efficiency ◽  
Construction Method ◽  
Vector Data ◽  
Temporal Index ◽  
Temporal Features ◽  
Nosql Database ◽  
Multi Level ◽  
Spatio Temporal ◽  
Index Level

Spatio-temporal indexing is a key technique in spatio-temporal data storage and management. Indexing methods based on spatial filling curves are popular in research on the spatio-temporal indexing of vector data in the Not Relational (NoSQL) database. However, the existing methods mostly focus on spatial indexing, which makes it difficult to balance the efficiencies of time and space queries. In addition, for non-point elements (line and polygon elements), it remains difficult to determine the optimal index level. To address these issues, this paper proposes an adaptive construction method of hierarchical spatio-temporal index for vector data. Firstly, a joint spatio-temporal information coding based on the combination of the partition and sort key strategies is presented. Secondly, the multilevel expression structure of spatio-temporal elements consisting of point and non-point elements in the joint coding is given. Finally, an adaptive multi-level index tree is proposed to realize the spatio-temporal index (Multi-level Sphere 3, MLS3) based on the spatio-temporal characteristics of geographical entities. Comparison with the XZ3 index algorithm proposed by GeoMesa proved that the MLS3 indexing method not only reasonably expresses the spatio-temporal features of non-point elements and determines their optimal index level, but also avoids storage hotspots while achieving spatio-temporal retrieval with high efficiency.

scholarly journals Nonstandard Action of Diffeomorphisms and Gravity’s Anti-Newtonian Limit

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 752
Author(s):  
Max Niedermaier
Keyword(s):  
Scalar Field ◽  
Parallel Transport ◽  
Einstein Gravity ◽  
Orthonormal Frame ◽  
Newtonian Limit ◽  
Index Structure ◽  
Diffeomorphism Group ◽  
Tensor Calculus ◽  
Ehresmann Connection ◽  
Spatio Temporal

A tensor calculus adapted to the Anti-Newtonian limit of Einstein gravity is developed. The limit is defined in terms of a global conformal rescaling of the spatial metric. This enhances spacelike distances compared to timelike ones and in the limit effectively squeezes the lightcones to lines. Conventional tensors admit an analogous Anti-Newtonian limit, which however transforms according to a non-standard realization of the spacetime Diffeomorphism group. In addition to the type of the tensor the transformation law depends on, a set of integer-valued weights is needed to ensure the existence of a nontrivial limit. Examples are limiting counterparts of the metric, Einstein, and Riemann tensors. An adapted purely temporal notion of parallel transport is presented. By introducing a generalized Ehresmann connection and an associated orthonormal frame compatible with an invertible Carroll metric, the weight-dependent transformation laws can be mapped into a universal one that can be read off from the index structure. Utilizing this ‘decoupling map’ and a realization of the generalized Ehresmann connection in terms of scalar field, the limiting gravity theory can be endowed with an intrinsic Levi–Civita type notion of spatio-temporal parallel transport.

Spatio-temporal Index

2008 ◽  
pp. 1121-1121
Author(s):  
Shashi Shekhar ◽  
Hui Xiong
Keyword(s):  
Temporal Index ◽  
Spatio Temporal

scholarly journals A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

2021 ◽  
Author(s):  
Shengnan Ke ◽  
Jun Gong ◽  
Songnian Li ◽  
Qing Zhu ◽  
Xintao Liu ◽  
...  
Keyword(s):  
Hash Table ◽  
Index Structure ◽  
Hybrid Index ◽  
Temporal Data ◽  
Trajectory Data ◽  
Generation Efficiency ◽  
Tree Structures ◽  
Data Indexing ◽  
Indexing Method ◽  
Spatio Temporal

In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.

scholarly journals MIDET: A Method for Indexing Traffic Events

2021 ◽  
Author(s):  
Mariana M. G. Duarte ◽  
Marcos V. Pontarolo ◽  
Rebeca Schroeder ◽  
Carmem S. Hara
Keyword(s):  
Traffic Control ◽  
Web Applications ◽  
Grid Cell ◽  
Real Data ◽  
Index Structure ◽  
Temporal Data ◽  
Bitmap Index ◽  
Traffic Lights ◽  
Spatio Temporal ◽  
Control Traffic

Traffic events announcements such as jams and road closures are continuously reported by mobile and Web applications. This collection of spatio-temporal data is an important source of information for urban planning, and can be used to orchestrate a number of actions to mprove the mobility, such as traffic control, traffic lights synchronization and preventive maintenance. Such analysis usually involves computation of spatial relationships among data, and may involve location of landmarks, roads and different types of events. In this paper, we propose a Method for Indexing Traffic Events (MIDET) for querying spatio-temporal data, whose location can be represented as a point or collection of points. MIDET is based on a fixed-grid space-oriented partitioning. In order to tackle the data skew, each grid cell is associated with a set of blocks containing event records. Moreover, a bitmap index is used for filtering out blocks without retrieving the actual data. MIDET provides the following benefits: adoption of a simple bulk loading process to manage dynamic insertion streams, and in-memory spatial joins. We conducted an experimental study using real data obtained from Waze. MIDET’s query performance was compared with Postgis, which adopts an R-tree index structure.

Research on Modeling and Indexing of Trajectories of Moving Objects in Road Networks

2013 ◽  
Vol 756-759 ◽  
pp. 1234-1239
Author(s):  
Yan Ling Zheng
Keyword(s):  
Road Network ◽  
Moving Objects ◽  
Road Networks ◽  
Experimental Results ◽  
Index Structure ◽  
Index Structures ◽  
The Road ◽  
The Past ◽  
Spatio Temporal ◽  
Temporal Trajectory

Proposed a new index structure, named MG2R*, can efficiently store and retrieve the past, present and future positions of network-constrained moving objects. It is a two-tier structure. The upper is a MultiGrid-R*-Tree (MGRT for short) that is used to index the road network. The lower is a group of independent R*-Tree. Each R*-Tree is relative to a route in the road network, can index the spatiotemporal trajectory of the moving objects in the road. Moreover, moving objects query is implemented based on this index structure. It compared to other index structures for road-network-based moving objects, such as MON-Tree, the experimental results shown that the MG2R* can effectively improve the query performance of the spatio-temporal trajectory of network-constrained moving objects.

Reverse Nearest Neighbors query of moving objects based on HRNN-tree

2019 ◽  
Vol 13 ◽  
Author(s):  
Miao Wang ◽  
Xiaotong Wang ◽  
Songyang Li ◽  
Xiaodong Liu ◽  
Song Li
Keyword(s):  
Quantitative Analysis ◽  
Spatial Analysis ◽  
Experimental Evaluation ◽  
Moving Objects ◽  
Nearest Neighbors ◽  
Spatial Processing ◽  
Temporal Database ◽  
Temporal Index ◽  
Spatio Temporal

Reverse nearest neighbors query as an indispensable part of spatial retrieve plays an important role in spatial analysis and spatial processing. In this paper, we target the problem of reverse nearest neighbors query for moving objects. We devised a spatio-temporal index suitable for this problem firstly and then an algorithm for reverse nearest neighbors search is proposed based on this index. The results of experimental evaluation demonstrate that this algorithm outperforms others for reverse nearest neighbors queries of moving objects. This work can better improve and enhance the power of quantitative analysis and process for spatio-temporal database.

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