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

Li, Chengming; Wu, Zheng; Wu, Pengda; Zhao, Zhijin

doi:10.3390/ijgi8110512

Cited by 9 publications

(6 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Within a partition, rows are organized together by the rest of the primary keys (e.g., the clustering key or sort key): Cassandra-specific partitioning keys and clustering keys. The cluster key structure can realize multigranularity and multilevel spatio-temporal indexing [82], which effectively promotes the search mechanism.…”

Section: A Key-column Databasementioning

confidence: 99%

A Survey of Spatio-Temporal Big Data Indexing Methods in Distributed Environment

Tian

Zhai

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

With the widespread use of mobile and sensing devices, and the popularity of online map-based services, such as navigation services, the volume of spatio-temporal data is growing rapidly. Conventional big data technologies in existing distributed systems cannot effectively process spatio-temporal big data with temporal continuity and spatial proximity. How to construct an effective index for the application requirements of spatio-temporal data in a distributed environment has become one of the hotspots of spatio-temporal big data research. Many spatio-temporal indexing methods have been proposed to support efficient query processing of spatio-temporal data. In this article, the various spatio-temporal big data indexing methods proposed by domestic and foreign researchers from 2010 to 2020 are classified and summarized according to the distributed environment and application background, and the hot issues that need to be paid attention to in the future are proposed according to the changes in application requirements Index Terms-Big Data, distributed system, spatio-temporal index. I. INTRODUCTION W ITH the maturity and widespread use of perception technology, a large number of records containing temporal and spatial marker information are generated, which are called spatio-temporal data [1]. Spatio-temporal data present multisource, heterogeneous, and multidimensional scale characteristics. Space and time are the basic attributes of spatio-temporal data and the basic characteristics of spatio-temporal data processing. With the rapid development of mobile Internet, location services and other technologies and the popularization of mobile devices, e.g., traffic trajectories [2], social media [3], remote sensing image [4], [5], climate observation [6], and other data containing spatio-temporal information rapidly accumulate, are forming a special kind of spatio-temporal big data [7]. In a general sense, spatio-temporal big data refers to the massive spatio-temporal data collection that is difficult to carry out data management, scientific computing, and value discovery within Manuscript

show abstract

Section: A Key-column Databasementioning

confidence: 99%

A Survey of Spatio-Temporal Big Data Indexing Methods in Distributed Environment

Tian

Zhai

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…According to the different ways of spatial object organization, spatial index methods are generally divided into object mapping, object bounding, clipping and multiple layers [11]. We simply divide the spatio-temporal indexes into two categories: regularization and irregularization, The former has no overlapping spatio-temporal regions of nodes in the same layer, and the partition always runs through a subregion, such as quadtree tree [12], KD tree [13] and BD tree [14], and the latter such as BSP tree [15], MLS3 [16], R tree [17][18][19]. R-tree is actually a spatio-temporal index family.…”

Section: Spatio-temporal Indexmentioning

confidence: 99%

“…the experiment is to check the relative performance improvement of the reconstruction algorithm based on query distribution frequency. We set the architectural node number for[4,8,16,24,32,40,48,64,72,80,88,96] and carry out the above experiments. The experimental results are shown in Figure10.…”

mentioning

confidence: 99%

Adaptive Spatio-Temporal Query Strategies in Blockchain

Chen

Dao-lei

2022

IJGI

View full text Add to dashboard Cite

In various applications of blockchain, how to index spatio-temporal data more efficiently has become a subject of continuous attention. The existing spatio-temporal data query in the blockchain is realized by adding additional external storage or fixed spatio-temporal index in the block, without considering the distribution of the spatio-temporal query itself and the proof performance accompanying the query. We propose an adaptive spatio-temporal blockchain index method, called Verkle AR*-tree, which adds the verification of time and location in the blockchain without additional storage and realizes the spatio-temporal index with an encrypted signature. Verkle AR*-tree further provides an adaptive algorithm, which adjusts the tree structure according to the historical query to produce the optimized index structure. The experimental results based on the pokeman dataset show that compared with the existing static spatio-temporal index, our method can effectively increase the performance of the spatio-temporal query and the spatio-temporal commitment in the blockchain.

show abstract

“…When it comes to spatial-partitioning models, S2Geometry [32] has been used for spatial data indexing. First, Li et al's work [33] proposed an adaptive multilevel index tree, termed MLS3, for indexing spatiotemporal data in P2P networks. The authors leveraged and extended S2 Geometry to encode spatiotemporal information in the row key of Apache Cassandra [34].…”

Section: Related Workmentioning

confidence: 99%

ST-Trie: A Novel Indexing Scheme for Efficiently Querying Heterogeneous, Spatiotemporal IoT Data

Chu

Kim

et al. 2020

Sustainability

View full text Add to dashboard Cite

Recently, various environmental data, such as microdust pollution, temperature, humidity, etc., have been continuously collected by widely deployed Internet of Things (IoT) sensors. Although these data can provide great insight into developing sustainable application services, it is challenging to rapidly retrieve such data, due to their multidimensional properties and huge growth in volume over time. Existing indexing methods for efficiently locating those data expose several problems, such as high administrative cost, spatial overhead, and slow retrieval performance. To mitigate these problems, we propose a novel indexing scheme termed ST-Trie, for efficient retrieval over spatiotemporal IoT environment data. Given IoT sensor data with latitude, longitude, and time, the proposed scheme first converts the three-dimensional attributes to one-dimensional index keys. The scheme then builds a trie-based index, consisting of internal nodes inserted by the converted keys and leaf nodes containing the keys and pointers to actual IoT data. We leverage this index to process various types of queries. In our experiments with three real-world datasets, we show that the proposed ST-Trie index outperforms existing approaches by a substantial margin regarding response time. Furthermore, we show that the query processing performance via ST-Trie also scales very well with an increasing time interval. Finally, we demonstrate that when compressed, the ST-Trie index can significantly reduce its space overhead by approximately a factor of seven.

show abstract

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

Cited by 9 publications

References 33 publications

A Survey of Spatio-Temporal Big Data Indexing Methods in Distributed Environment

A Survey of Spatio-Temporal Big Data Indexing Methods in Distributed Environment

Adaptive Spatio-Temporal Query Strategies in Blockchain

ST-Trie: A Novel Indexing Scheme for Efficiently Querying Heterogeneous, Spatiotemporal IoT Data

Contact Info

Product

Resources

About