Academic Journal
A Distributed Storage and Indexing Framework Based on Hierarchical Space-Time Grid Encoding for Digital Cities.
| Title: | A Distributed Storage and Indexing Framework Based on Hierarchical Space-Time Grid Encoding for Digital Cities. |
|---|---|
| Authors: | Zhang, Huangchuang, Xing, Weiming, Zhang, Kai |
| Source: | ISPRS International Journal of Geo-Information; Sep2026, Vol. 15 Issue 9, p398, 30p |
| Subject Terms: | Digital twin, Spacetime, Nonrelational databases, Cloud storage, K-nearest neighbor classification |
| Abstract: | The rapid prolife ration of digital twin cities has resulted in an unprecedented increase in the volume, heterogeneity, and dynamics of space-time data, posing significant challenges to scalable storage, efficient indexing, and high-performance query processing in distributed environments. Existing distributed frameworks generally treat data partitioning and indexing as independent processes, making it difficult to simultaneously preserve space-time locality, achieve balanced data distribution, and support efficient multidimensional queries. To address these challenges, this paper proposes a distributed space-time storage and indexing framework based on hierarchical space-time grid encoding. The proposed framework integrates Hadoop distributed file system (HDFS) and HBase to establish a unified architecture for data partitioning, storage organization, indexing, and query processing. Specifically, a space-time grid–based partitioning strategy is designed to preserve space-time locality while maintaining load balance across distributed storage nodes. Furthermore, a hybrid interleaved space-time encoding scheme together with an optimized HBase RowKey is developed to construct a unified space-time index, enabling efficient space-time range queries and space-time k-nearest neighbor (KNN) queries in large-scale distributed environments. Extensive experiments conducted on the T-Drive trajectory dataset demonstrate that the proposed framework consistently outperforms several representative distributed space-time indexing approaches in terms of query efficiency, scalability, and overall system performance while maintaining stable indexing performance under increasing data volumes. The proposed framework provides an efficient and scalable solution for the management and retrieval of massive space-time data and offers a practical infrastructure for digital twin city applications. [ABSTRACT FROM AUTHOR] |
| Copyright of ISPRS International Journal of Geo-Information is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: A Distributed Storage and Indexing Framework Based on Hierarchical Space-Time Grid Encoding for Digital Cities. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Huangchuang%22">Zhang, Huangchuang</searchLink><br /><searchLink fieldCode="AR" term="%22Xing%2C+Weiming%22">Xing, Weiming</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Kai%22">Zhang, Kai</searchLink> – Name: TitleSource Label: Source Group: Src Data: ISPRS International Journal of Geo-Information; Sep2026, Vol. 15 Issue 9, p398, 30p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Digital+twin%22">Digital twin</searchLink><br /><searchLink fieldCode="DE" term="%22Spacetime%22">Spacetime</searchLink><br /><searchLink fieldCode="DE" term="%22Nonrelational+databases%22">Nonrelational databases</searchLink><br /><searchLink fieldCode="DE" term="%22Cloud+storage%22">Cloud storage</searchLink><br /><searchLink fieldCode="DE" term="%22K-nearest+neighbor+classification%22">K-nearest neighbor classification</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The rapid prolife ration of digital twin cities has resulted in an unprecedented increase in the volume, heterogeneity, and dynamics of space-time data, posing significant challenges to scalable storage, efficient indexing, and high-performance query processing in distributed environments. Existing distributed frameworks generally treat data partitioning and indexing as independent processes, making it difficult to simultaneously preserve space-time locality, achieve balanced data distribution, and support efficient multidimensional queries. To address these challenges, this paper proposes a distributed space-time storage and indexing framework based on hierarchical space-time grid encoding. The proposed framework integrates Hadoop distributed file system (HDFS) and HBase to establish a unified architecture for data partitioning, storage organization, indexing, and query processing. Specifically, a space-time grid–based partitioning strategy is designed to preserve space-time locality while maintaining load balance across distributed storage nodes. Furthermore, a hybrid interleaved space-time encoding scheme together with an optimized HBase RowKey is developed to construct a unified space-time index, enabling efficient space-time range queries and space-time k-nearest neighbor (KNN) queries in large-scale distributed environments. Extensive experiments conducted on the T-Drive trajectory dataset demonstrate that the proposed framework consistently outperforms several representative distributed space-time indexing approaches in terms of query efficiency, scalability, and overall system performance while maintaining stable indexing performance under increasing data volumes. The proposed framework provides an efficient and scalable solution for the management and retrieval of massive space-time data and offers a practical infrastructure for digital twin city applications. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of ISPRS International Journal of Geo-Information is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/ijgi15090398 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 30 StartPage: 398 Subjects: – SubjectFull: Digital twin Type: general – SubjectFull: Spacetime Type: general – SubjectFull: Nonrelational databases Type: general – SubjectFull: Cloud storage Type: general – SubjectFull: K-nearest neighbor classification Type: general Titles: – TitleFull: A Distributed Storage and Indexing Framework Based on Hierarchical Space-Time Grid Encoding for Digital Cities. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Huangchuang – PersonEntity: Name: NameFull: Xing, Weiming – PersonEntity: Name: NameFull: Zhang, Kai IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22209964 Numbering: – Type: volume Value: 15 – Type: issue Value: 9 Titles: – TitleFull: ISPRS International Journal of Geo-Information Type: main |
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