An efficient integer coding index algorithm for multi-scale time information management

Tong, Xiaochong; Cheng, Chengqi; Wang, Rong; Ding, Lei; Zhang, Yong; Lai, Guangling; Wang, Lin; Chen, Bin

doi:10.1016/j.datak.2019.01.003

Cited by 19 publications

(13 citation statements)

References 3 publications

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“…In our study, a spatio-temporal secondary indexing model is established; that is, the spatial code is first sorted and then the time code in each grid is sorted (Figure 3b). In the indexing model (Figure 3b), Codei is the ith grid code in the aerospace, Tcodei is the ith time code (using the coding method in [37]), and Datai is the spatial data or entity.…”

Section: Aerospace Grid Indexing Bigtablementioning

confidence: 99%

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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: Aerospace Grid Indexing Bigtablementioning

confidence: 99%

“…In the indexing model (Figure 3b), Code i is the ith grid code in the aerospace, Tcode i is the ith time code (using the coding method in [37]), and Data i is the spatial data or entity.…”

Section: Traditional Satellite Visibility Analysismentioning

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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“…Near-real-time or real-time analyses of Earth observation data are also imminent or asked in applications [11,93]. Time has the same problem as space partition and space scale [94,95]. Comparatively, the time dimension is more challenging to deal with.…”

Section: Spatiotemporal Dggs Framework For Eo Datamentioning

confidence: 99%

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Yao

Xia

et al. 2019

Remote Sensing

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In the era of big data, the explosive growth of Earth observation data and the rapid advancement in cloud computing technology make the global-oriented spatiotemporal data simulation possible. These dual developments also provide advantageous conditions for discrete global grid systems (DGGS). DGGS are designed to portray real-world phenomena by providing a spatiotemporal unified framework on a standard discrete geospatial data structure and theoretical support to address the challenges from big data storage, processing, and analysis to visualization and data sharing. In this paper, the trinity of big Earth observation data (BEOD), cloud computing, and DGGS is proposed, and based on this trinity theory, we explore the opportunities and challenges to handle BEOD from two aspects, namely, information technology and unified data framework. Our focus is on how cloud computing and DGGS can provide an excellent solution to enable big Earth observation data. Firstly, we describe the current status and data characteristics of Earth observation data, which indicate the arrival of the era of big data in the Earth observation domain. Subsequently, we review the cloud computing technology and DGGS framework, especially the works and contributions made in the field of BEOD, including spatial cloud computing, mainstream big data platform, DGGS standards, data models, and applications. From the aforementioned views of the general introduction, the research opportunities and challenges are enumerated and discussed, including EO data management, data fusion, and grid encoding, which are concerned with analysis models and processing performance of big Earth observation data with discrete global grid systems in the cloud environment.Remote Sens. 2020, 12, 62 2 of 15 (CEOS), over 500 EO satellites have been launched in the last half-century, and more than 150 satellites will be launched in the next 12 years [2,3]. China has launched more than 60 satellites since 1970 for comprehensive observation of the Earth's systems, including HuanJing (HJ), FengYun (FY), China-brazil earth resource satellite (CBERS), and GaoFen (GF) series. From the European Space Agency (ESA), in terms of Sentinel family, a total of 50,964,670 products had been downloaded by users since the start of data access operations, with a total data volume of 41. 35 PB [4]. The big Earth observation data (BEOD) has gradually promoted the development of global industries, research institutions, and application sectors, which has had a profound impact on the study of the Earth system [5,6], contributing to human activities, environmental monitoring, and climate changes, and also provided abundant data resource for the construction of digital Earth [7][8][9].BEOD also poses challenges to uses in terms of problem complexity, automatic analysis, and processing efficiency [10][11][12][13]. Fortunately, the rapid advancement of cloud computing technology in recent years provides strong computing power, especially for the efficiency of big geospatial data management and process...

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“…The above works proposed means of organizing and retrieving spatial data based on GeoSOT, but they only encode spatial information for the object without encoding its time information. In [29], a multiscale time partition and integer coding (MTIC) method is proposed. When using this method to index the time information of an object, high efficiency queries of multiple scales can be achieved.…”

Section: Related Workmentioning

confidence: 99%

“…This experiment was carried out to evaluate the GeoSOT ST-index's scalability and to demonstrate the extension of query consumption time observed under different data volumes. We randomly selected the time period of interest "2011-03-07 09: 29 The results are shown in Figure 9. Under a fixed query range, as the amount of query data increased, the three indexes consumed more query time, but the GeoSOT ST-index showed a more gradual increase in the amount of time consumed.…”

Section: Comparisons Of Scalability Performancementioning

confidence: 99%

GeoSOT-Based Spatiotemporal Index of Massive Trajectory Data

Qian

Cheng

et al. 2019

IJGI

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With the rapid development of global positioning technologies and the pervasiveness of intelligent mobile terminals, trajectory data have shown a sharp growth trend both in terms of data volume and coverage. In recent years, increasing numbers of LBS (location based service) applications have provided us with trajectory data services such as traffic flow statistics and user behavior pattern analyses. However, the storage and query efficiency of massive trajectory data are increasingly creating a bottleneck for these applications, especially for large-scale spatiotemporal query scenarios. To solve this problem, we propose a new spatiotemporal indexing method to improve the query efficiency of massive trajectory data. First, the method extends the GeoSOT spatial partitioning scheme to the time dimension and forms a global space–time subdivision scheme. Second, a novel multilevel spatiotemporal grid index, called the GeoSOT ST-index, was constructed to organize trajectory data hierarchically. Finally, a spatiotemporal range query processing method is proposed based on the index. We implement and evaluate the index in MongoDB. By comparing the range query efficiency and scalability of our index with those of the other two space–time composite indexes, we found that our approach improves query efficiency levels by approximately 40% and has better scalability under different data volumes.

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An efficient integer coding index algorithm for multi-scale time information management

Cited by 19 publications

References 3 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

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

GeoSOT-Based Spatiotemporal Index of Massive Trajectory Data

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