Using Graph Database in Spatial Data Generation

Płuciennik, Tomasz; Płuciennik-Psota, Ewa

doi:10.1007/978-3-319-02309-0_69

Cited by 9 publications

(4 citation statements)

References 3 publications

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“…The spatial graph database we developed includes the semantic description of the manufacturing data, the indoor space and the navigation processes. It also incorporates space and time (Cattuto et al, 2013;Pluciennik & Pluciennik-Psota, 2014). The capability of Neo4j to store and query data with a spatiotemporal dimension leverages the potential for spatio-temporal analysis.…”

Section: Methodsmentioning

confidence: 99%

Spatially-Linked Manufacturing Data to Support Data Analysis

Schabus¹,

Scholz²

2017

giforum

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The paper presents a Linked Data approach within a manufacturing organization to foster sharing, reusing, integrating and the collaborative analysis of datasets originating from different business units and heterogeneous data sources. The paper relies on a semiconductor company that serves as case study. The authors elaborate on manufacturing data and their representation in a spatially-enabled graph database, and as Linked Data based on an ontology describing the indoor space and production processes. A graph database enables data sharing as well as the semantic search and retrieval of data utilizing web-based services. The results present the analysis of historic, future and spatio-temporal data as well as the analysis of similarities of semanticallyannotated linked manufacturing data.

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Section: Methodsmentioning

confidence: 99%

Spatially-Linked Manufacturing Data to Support Data Analysis

Schabus¹,

Scholz²

2017

giforum

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“…The use of intuitive objects and graphs in the design method is not discussed. In [10], a graph database is actually used to simplify the generation of GIS test data and virtual cities. A general survey and comparison of graph databases is presented in [11].…”

Section: A Related Workmentioning

confidence: 99%

A Graph-Based Design of an Open Database for 3D Models with Versioning

Ke¹,

Hoi²

2017

IJIET

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Abstract-We present a graph-based method for illustration and specification of the data structures to persist 3D models. We define the mapping from the object graphs to the relations in a relational database. Our ORM scheme fully opens the structures of the underlying relations so that the data can be easily manipulated and used by other tools and under other schemes. By this method, 3D models can be accumulated from large amount of contributors in the long run, and used by various types of applications, such as real-time rendering and geometric searching. Our graph notation and ORM scheme support arrays and variants, which are essential in the specification of complex data structures. Algorithms are provided to illustrate how the mappings are implemented, as well as how the database can be used. An independent versioning system is implemented on top of the 3D model database, showing the flexibility and advantage of this graph-based method. We implemented a minimal 3D model database following this design, and present the evaluation of the performance of loading models from the database.Index Terms-Object-relational mapping, class graph, 3D model, versioning, open database.

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“…Trees and graphs are well-known structures that use node-link diagrams to represent data relationships [35]. Trees usually store the topological relationships among spatial objects and use semantic information to perform the node selection and object association [36], and the multi-dimensional characteristics and complex relationships of the scene data are usually stored in the relational model, but joint operations are expensive during analysis and exploration [37,38]. Graph models naturally have the advantage of representing multi-dimensional attributes and relationships among objects, and they have been widely used in the fields of social networks, social security, intelligence recommendations, and biotechnology [39].…”

Section: Introductionmentioning

confidence: 99%

“…The experimental data are generated by a simulation programme that mainly includes three types of data: user data, trajectory data, and social network data [37]. The details of the experimental datasets are given in Table 1 and screenshots of the experimental data fragments are shown in Figure 7.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Graph-Based Spatio-Temporal Indexing Method for Task-Oriented Multi-Modal Scene Data Organization

Feng

Zhu

Liu

et al. 2018

IJGI

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Task-oriented scene data in big data and cloud environments of a smart city that must be time-critically processed are dynamic and associated with increasing complexities and heterogeneities. Existing hybrid tree-based external indexing methods are input/output (I/O)-intensive, query schema-fixed, and difficult when representing the complex relationships of real-time multi-modal scene data; specifically, queries are limited to a certain spatio-temporal range or a small number of selected attributes. This paper proposes a new spatio-temporal indexing method for task-oriented multi-modal scene data organization. First, a hybrid spatio-temporal index architecture is proposed based on the analysis of the characteristics of scene data and the driving forces behind the scene tasks. Second, a graph-based spatio-temporal relation indexing approach, named the spatio-temporal relation graph (STR-graph), is constructed for this architecture. The global graph-based index, internal and external operation mechanisms, and optimization strategy of the STR-graph index are introduced in detail. Finally, index efficiency comparison experiments are conducted, and the results show that the STR-graph performs excellently in index generation and can efficiently address the diverse requirements of different visualization tasks for data scheduling; specifically, the STR-graph is more efficient when addressing complex and uncertain spatio-temporal relation queries.

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Using Graph Database in Spatial Data Generation

Cited by 9 publications

References 3 publications

Spatially-Linked Manufacturing Data to Support Data Analysis

Spatially-Linked Manufacturing Data to Support Data Analysis

A Graph-Based Design of an Open Database for 3D Models with Versioning

An Efficient Graph-Based Spatio-Temporal Indexing Method for Task-Oriented Multi-Modal Scene Data Organization

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