Integrating Big Spatio-Temporal Data Using Collaborative Semantic Data Management

Abstract. To enrich urban digital twins and better understand city evolution, the integration of heterogeneous, spatio-temporal data has become a large area of research in the enrichment of 3D and 4D (3D + Time) semantic city models. These models, which can represent the 3D geospatial data of a city and their evolving semantic relations, may require data-driven integration approaches to provide temporal and concurrent views of the urban landscape. However, data integration often requires the transformation or conversion of data into a single shared data format, which can be prone to semantic data loss. To combat this, this paper proposes a model-centric ontology-based data integration approach towards limiting semantic data loss in 4D semantic urban data transformations to semantic graph formats. By integrating the underlying conceptual models of urban data standards, a unified spatio-temporal data model can be created as a network of ontologies. Transformation tools can use this model to map datasets to interoperable semantic graph formats of 4D city models. This paper will firstly illustrate how this approach facilitates the integration of rich 3D geospatial, spatio-temporal urban data and semantic web standards with a focus on limiting semantic data loss. Secondly, this paper will demonstrate how semantic graphs based on these models can be implemented for spatial and temporal queries toward 4D semantic city model enrichment.

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Memory-Efficient Storing of Timestamps for Spatio-Temporal Data Management in Columnar In-Memory Databases

Database Systems for Advanced Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-030-73194-6_36 ◽

2021 ◽

pp. 542-557

Author(s):

Keven Richly

Keyword(s):

Data Management ◽

Temporal Data ◽

Spatio Temporal ◽

Memory Efficient

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Spatio‐temporal data management using object lifecycles: A case study of the Australian capital territory spatial data management system

Journal of Spatial Science ◽

10.1080/14498596.2006.9635063 ◽

2006 ◽

Vol 51 (1) ◽

pp. 43-58 ◽

Cited By ~ 4

Author(s):

K.M. Stock

Keyword(s):

Data Management ◽

Spatial Data ◽

Management System ◽

Data Management System ◽

Temporal Data ◽

Australian Capital Territory ◽

Australian Capital ◽

Spatial Data Management ◽

Spatio Temporal

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Architecture of RFID Spatio-Temporal Data Management

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.314-316.2425 ◽

2011 ◽

Vol 314-316 ◽

pp. 2425-2428 ◽

Cited By ~ 40

Author(s):

Yong Hui Wang ◽

Huan Liang Sun ◽

Jing Ke Xu

Keyword(s):

Data Management ◽

Asset Management ◽

Data Model ◽

Temporal Data ◽

Rfid Technology ◽

Key Technology ◽

Memory Cache ◽

Rfid Middleware ◽

Spatio Temporal ◽

The Internet Of Things

With the development of RFID technology, we can identify, locate, track and monitor items in supply chain, retail store, and asset management applications. RFID has become a key technology in the Internet of Things. But RFID data can’t be effectively managed by only using traditional data model because they have their own unique characteristics, such as aggregation, location, temporal and history-oriented, which have to be fully considered and integrated into the data model. Therefore, the architecture of RFID spatio-temporal data management is proposed in this paper. We provide a brief overview of RFID technology and highlight a few of the spatio-temporal data management challenges, such as RFID middleware, RFID event processing, In-memory cache.

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Spatio-Temporal Data Management for Moving Objects

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.123.1155 ◽

2003 ◽

Vol 123 (6) ◽

pp. 1155-1165

Author(s):

Yiqun Wang ◽

Hiroshi Nozawa ◽

Yoshinori Hijikata ◽

Mie Nakatani ◽

Shogo Nishida

Keyword(s):

Data Management ◽

Moving Objects ◽

Temporal Data ◽

Spatio Temporal

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A Survey on Big Data Processing Frameworks for Mobility Analytics

ACM SIGMOD Record ◽

10.1145/3484622.3484626 ◽

2021 ◽

Vol 50 (2) ◽

pp. 18-29

Author(s):

Christos Doulkeridis ◽

Akrivi Vlachou ◽

Nikos Pelekis ◽

Yannis Theodoridis

Keyword(s):

Big Data ◽

Data Processing ◽

Data Management ◽

Spatial Data ◽

State Of The Art ◽

Temporal Data ◽

Big Data Processing ◽

Mobility Data ◽

New Challenges ◽

Spatio Temporal

In the current era of big spatial data, the vast amount of produced mobility data (by sensors, GPS-equipped devices, surveillance networks, radars, etc.) poses new challenges related to mobility analytics. A cornerstone facilitator for performing mobility analytics at scale is the availability of big data processing frameworks and techniques tailored for spatial and spatio-temporal data. Motivated by this pressing need, in this paper, we provide a survey of big data processing frameworks for mobility analytics. Particular focus is put on the underlying techniques; indexing, partitioning, query processing are essential for enabling efficient and scalable data management. In this way, this report serves as a useful guide of state-of-the-art methods and modern techniques for scalable mobility data management and analytics.

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