Parallel Insertion and Indexing Method for Large Amount of Spatiotemporal Data Using Dynamic Multilevel Grid Technique

Park, Sangdeok; Ko, Dae-Sik; Song, Seokil

doi:10.3390/app9204261

Cited by 6 publications

(5 citation statements)

References 13 publications

(19 reference statements)

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“…Each grid is then assigned a code, which is used to uniquely identify the spatial area. Codes of grids at the same level are not random, but are sorted according to the Z-order space-filling curve (a spatial indexing method), which will result in neighboring grids having codes with close values [36]. Eventually, the high-dimensional spatial information is converted into a one-dimensional aerospace coordinate code, which is convenient for the identification, storage, and calculation of spatial information (Figure 2).…”

Section: Geosot-3d Grid Model and Coding Methodsmentioning

confidence: 99%

A Space-Interconnection Algorithm for Satellite Constellation Based on Spatial Grid Model

Hou

Cheng

et al. 2020

Remote Sensing

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With the rapid development of large-scale satellite constellations and the increasing demand for rapid communication and emergency rescue using global satellite-based Internet, there have been new requirements for efficient algorithms for inter-communication between satellites. As the constellations of low-orbit satellites become larger, the complexities of real-time inter-satellite calculation and path planning are becoming more complicated and are increasing geometrically. To address the bottlenecks in large-scale node space computing, we introduced a global space grid. Based on this grid, an efficient calculation method of spatial inter-connection between satellite constellations is proposed, according to the concept of “storage for computing” and the high computational efficiency of the spatial grid model. This strategy includes the following parts: (1) the introduction of the GeoSOT-3D global grid model into aerospace and the construction of the aerospace grid indexing BigTable; (2) a set of algorithms for satellite visibility analysis according to the visible grid look-up table and the secondary grid index; and (3) planning inter-satellite routing by querying the grid’s inherent visibility. The idea at the basis of this method is to employ the “space for time” concept to convert the high-dimensional floating operations into one-dimensional matching operations by querying the inherent “visible” attribute of the grid. In our study, we simulated thousands of satellites, discretized their trajectories into grids, and pre-calculated the visibility between grid cells to plan the routing path for the ground data transmission. The theoretical analysis and experimental verification show that the algorithm is feasible and efficient, and it can significantly improve the computational efficiency of inter-satellite connection. We hope that the method can be used in emergency communications, disaster warning, and maritime rescue, and can contribute to the next generation of satellite internet and “satellite-ground” integrated networks.

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Section: Geosot-3d Grid Model and Coding Methodsmentioning

confidence: 99%

A Space-Interconnection Algorithm for Satellite Constellation Based on Spatial Grid Model

Hou

Cheng

et al. 2020

Remote Sensing

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“…Many recent studies have proposed an index for moving objects and they involved parallelism to improve the response time of query operations. 8,[51][52][53][54][55][56][57][58][59] We have not included such studies in our scope as these studies are based on parallelism rather than concurrency control. And, focus of this article is on the resolution of conflicts while processing concurrent update and query operations.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…However, our focus is on designing of consistent index for moving objects not just a concurrency control algorithm. Many recent studies have proposed an index for moving objects and they involved parallelism to improve the response time of query operations 8,51–59 . We have not included such studies in our scope as these studies are based on parallelism rather than concurrency control.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Exploiting hashing for concurrent query processing and indexing of current location of moving objects

Chaudhry,

Yousaf

2024

Concurrency and Computation

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SummaryDue to recent developments in location‐based services and mobile computing, the need for indices for moving objects has been strengthened to improve the response time of a query operation. With a single index in place for managing both the update and query operations for moving objects, the index needs to be updated each time the object moves. This deteriorates the performance of concurrent query operations. It is critical to handle the conflicts between continuous update and query operations effectively using appropriate concurrency control protocol otherwise, inconsistent results will be reported. Many indices have been proposed in the literature for moving objects but they lack the support for processing concurrent operations. Further, the consistent indices in the literature are based on tree structure that have computationally expensive split/merge operations that can negatively affect the response time of query processing algorithms. Moreover such tree based indices are proposed for historical and future timeline data. As the scope of this article is on current timeline and concurrent continuous query operations, therefore, we exploit state of the art hash‐based indices in the literature and presented the two consistent versions. The comparative analysis of the indices is performed and meaningful findings are also presented along.

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“…Park et al [93] establish a dynamic multilevel grid index in Accumulo, which can improve the parallelism and adapt to the skew of spatio-temporal data. The main idea is as follows: first use H-Curve technology to map the spatial characteristics of spatio-temporal data stream timestamp, id, location, Record to 1-D data cellID, convert the data into timestamp, id, cellID, location, record.…”

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

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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

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Parallel Insertion and Indexing Method for Large Amount of Spatiotemporal Data Using Dynamic Multilevel Grid Technique

Cited by 6 publications

References 13 publications

A Space-Interconnection Algorithm for Satellite Constellation Based on Spatial Grid Model

A Space-Interconnection Algorithm for Satellite Constellation Based on Spatial Grid Model

Exploiting hashing for concurrent query processing and indexing of current location of moving objects

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

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