Decomposition tree: a spatio-temporal indexing method for movement big data

He, Zhenwen; Wu, Chunming; Liu, Gang; Zheng, Zufang; Tian, Yuan

doi:10.1007/s10586-015-0475-3

Cited by 27 publications

(13 citation statements)

References 25 publications

(37 reference statements)

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“…The main two models for spatial organization of multi-source remote sensing data are: (1) the satellite orbit stripe or scene organization based on the spatio-temporal recording system; and (2) data tiling organization based on the globally meshed grid [13,14].…”

Section: Spatial Organization Of Remote Sensing Datamentioning

confidence: 99%

Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure

Fan

Yan

et al. 2017

Remote Sensing

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Since Landsat-1 first started to deliver volumes of pixels in 1972, the volumes of archived data in remote sensing data centers have increased continuously. Due to various satellite orbit parameters and the specifications of different sensors, the storage formats, projections, spatial resolutions, and revisit periods of these archived data are vastly different. In addition, the remote sensing data received continuously by each data center arrives at a faster code rate; it is best to ingest and archive the newly received data to ensure users have access to the latest data retrieval and distribution services. Hence, an excellent data integration, organization, and management program is urgently needed. However, the multi-source, massive, heterogeneous, and distributed storage features of remote sensing data have not only caused difficulties for integration across distributed data center spatial infrastructures, but have also resulted in the current modes of data organization and management being unable meet the rapid retrieval and access requirements of users. Hence, this paper proposes an object-oriented data technology (OODT) and SolrCloud-based remote sensing data integration and management framework across a distributed data center spatial infrastructure. In this framework, all of the remote sensing metadata in the distributed sub-centers are transformed into the International Standardization Organization (ISO) 19115-based unified format, and then ingested and transferred to the main center by OODT components, continuously or at regular intervals. In the main data center, in order to improve the efficiency of massive data retrieval, we proposed a logical segmentation indexing (LSI) model-based data organization approach, and took SolrCloud to realize the distributed index and retrieval of massive metadata. Finally, a series of distributed data integration, retrieval, and comparative experiments showed that our proposed distributed data integration and management program is effective and promises superior results. Specifically, the LSI model-based data organization and the SolrCloud-based distributed indexing schema was able to effectively improve the efficiency of massive data retrieval.

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Section: Spatial Organization Of Remote Sensing Datamentioning

confidence: 99%

Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure

Fan

Yan

et al. 2017

Remote Sensing

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“…The various types of indexing and the query process sing are mentioned in [38] and further consideration in spatio-temporal is security mentioned in [34][35][36][37] these papers are mentioned various advanced security features. The PPFI [26], MSMON [32], PPFN* [33] and D-Tree [28] are the main past, present and future data prediction indexing methods and these methods are considered as comparison purpose.…”

Section: Related Workmentioning

confidence: 99%

“…The Most of the spatial data updating indexing methods [28] are based on the integrated binary tree, R-tree, R*-Tree, Oct tree, Quad tree, Grid tree and Hex tree. Those derived from R-tree employ a minimum bounding rectangle (MBR) or a timeparameterized minimum-bounding rectangle (TPMBR) to represent a moving object.…”

Section: Introductionmentioning

confidence: 99%

DTNH Indexing Method: Past Present and Future Data Prediction for Spatio-Temporal Data

John¹,

S²,

Rajesh³

2017

IJIES

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Indexing methods are developed to effectively process user queries in many real-time and moving object management applications. The existing spatial data updating indexing methods are based on the Integrated binary Tree, R-Tree, R*-Tree, Oct-Tree, Quad-Tree, Grid-Tree and Hex-Tree. The depth of these trees is unbalanced and overlapping, hence the performance is reduced in the multi-structure indexing methods. D-Tree (Decompose -Tree) based multi-structure spatio-temporal index method is proposed to find the present, past and future data. The new multistructural model called DTNH-Tree used to find the present, past and future data. It consists of D-Tree, TB*-Tree, NTTree and hash table. The D-Tree indexing is used to get the spatial data and manage the moving objects in the road network. A set of TB*-Tree is used to index the history of moving object on road networks. A set of NT -Trees is used to manage the current position of the recently updated data and find present data of the moving objects. NT-Tree indexes the present and future information of the moving objects. Finally the set of hash tables is used for updating the data continuously. The proposed multi-structure indexing method supports different types of query processing compared to the existing indexing methods. Experimental results exhibits better updation and query performance compared to the MSMON-Tree and PPF*-Tree.

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“…Multi-sensor fusion has always been concerned for complementary information enhancement [1], especially for the remote sensing big data era [2][3][4][5]. With increasing frequency, different types of remote sensing satellites are used to monitor the Earth's surface.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Fusion of Remote Sensing Images with Structural Sparsity and Semi-Coupled Dictionary Learning

et al. 2016

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Fusion of remote sensing images with different spatial and temporal resolutions is highly needed by diverse earth observation applications. A small number of spatiotemporal fusion methods using sparse representation appear to be more promising than traditional linear mixture methods in reflecting abruptly changing terrestrial content. However, one of the main difficulties is that the results of sparse representation have reduced expressional accuracy; this is due in part to insufficient prior knowledge. For remote sensing images, the cluster and joint structural sparsity of the sparse coefficients could be employed as a priori knowledge. In this paper, a new optimization model is constructed with the semi-coupled dictionary learning and structural sparsity to predict the unknown high-resolution image from known images. Specifically, the intra-block correlation and cluster-structured sparsity are considered for single-channel reconstruction, and the inter-band similarity of joint-structured sparsity is considered for multichannel reconstruction, and both are implemented with block sparse Bayesian learning. The detailed optimization steps are given iteratively. In the experimental procedure, the red, green, and near-infrared bands of Landsat-7 and Moderate Resolution Imaging Spectrometer (MODIS) satellites are put to fusion with root mean square errors to check the prediction accuracy. It can be concluded from the experiment that the proposed methods can produce higher quality than state-of-the-art methods.

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Decomposition tree: a spatio-temporal indexing method for movement big data

Cited by 27 publications

References 25 publications

Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure

Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure

DTNH Indexing Method: Past Present and Future Data Prediction for Spatio-Temporal Data

Spatiotemporal Fusion of Remote Sensing Images with Structural Sparsity and Semi-Coupled Dictionary Learning

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