The Spatio-Temporal Data Modeling and Application Based on Graph Database

In order to realize the visualization,spatial analysis,automatic modeling and dynamic updating of model,the mine spatial environment and traditional 3D spatial data models were analyzed.Then an entity-oriented spatio-temporal hybrid data model was designed.It integrated the 3D parameter skeleton model,Topology-concerned and Entity-oriented 3D Vector Data Model(TEVDM) and Octree-Block Model(OBM).The TEVDM and OBM were used to express the boundary and internal property of orebodys.The Parametric Entity-Network Data Model(PENDM) was introduced into the model to describe the skeleton of the roadway engineering and production systems.Particularly,a behavior model was introduced into the model to describe the personnel and devices with behavior characteristics.The model also included the complete spatial feature which could describe the set of semantically related entities.Finally,some samples of using this model were given to describe the mines' spatial phenomenon and carry out spatial analysis.The samples show this model has better practicability in mining than traditional models.

Spatial data model is a key factor in GIS research. Traditional spatial data models based on Euclidean geometry frameworks are limited in terms of unifying space-time and different objects of different dimensions owing to the complexity associated with calculating topological relationships. Thus, a new spatial data model based on geometric algebra (GA-based spatial data model) is developed, which introduces GA into the spatial data model by using multidimensional vectors to unify space-time and multidimensional objects. To link traditional spatial data models and GA-based spatial data models, in this paper, a GA-based spatial data description language (GASDL) is proposed. GASDL is based on the Extensible Markup Language (XML), and it mainly describes data in terms of geometry, topological relationships and properties. GASDL considers the characteristics of both the traditional spatial data model and the GA-based spatial data model. By using GASDL, data can be imported and exported between traditional spatial data models and GA-based spatial data models. Thus, GIS software applications that use traditional spatial data models and GA-based data models can be connected.

Land subsidence, defined as a land sinking or a gradual inward caving of land, presents a common disturbance observed in many areas of the world. In Louisiana, this specific problem posed a serious threat to the populace living there. Considered by denizens to be an adverse impact of land use, the extant Louisiana subsidence causes serious problems that tend to worsen, such as excessive wetland formation or land loss. Unless researchers find appropriate treatments to address this increasingly serious problem, the present issues will be exacerbated. To visualize the spatio-temporal subsidence patterns, this study used data collected by high-precision GPS stations and processed high-accuracy land elevation data in coastal Louisiana by means of a GIS-based spatio-temporal data model. I used the Kriged Kalman Filter (KKF) to map the spatial temporal field of land elevation change in southern Louisiana from 2011 to 2013, which showed a clear subsidence area after 2012. The coincidence of the Bayou Corne Sinkhole enabled a validation of the GPS data and the spatio-temporal data model. In addition, the spatial pattern for subsidence was predicted by Regression-Kriging and based on observed GPS data in tandem with the data on contributing subsidence factors. The prediction results using Regression-Kriging had high and acceptable accuracy. I applied the geographically weighted regression (GWR) model to show the spatial heterogeneity of contributing factors to subsidence in the study site. The statistical results showed that spatial heterogeneity for the data of contributing factors vi would be useful to recognize the agglomeration of communities in the study area. The regionalization work of these contributing factors could also be helpful to form location-based subsidence mitigation policies. This research contributes to the knowledge of GIS data modeling by incorporating a spatio-temporal interpolation—the Kriged Kalman filter (KKF)—into mapping and monitoring the land elevation change. This technique overcomes the problems of traditional spatial interpolation methods that disregard the time dependency of the geospatial data. The second contribution of this research is to predict the spatial pattern of subsidence using the information in regard to the subsidence factors at GPS stations. A cross-scale subsidence prediction, drawn solely on point based data from GPS stations, was made possible by Regression-Kriging. The third contribution of this research is that the spatial statistical models used for data analysis enable location-based policy-making. In other words, the local government can embrace smart policies that are specifically effective for certain regions to prevent further land

We propose constraint databases as an intermediate level facilitating the interoperability of spatiotemporal data models. Constraint query languages are used to express translations between different data models. We illustrate our approach in the context of a number of temporal, spatial, and spatiotemporal data models.

Spatio-temporal data model is a data model which completely simulates the objective world in terms of time, space and semantic features. It is an abstraction and representation of the objective real world. Urban history is a branch of world history. It draws outline for the course of urban initiation, development and maturing with the development of civilization as a clue. "Beijing Studies" is an important branch of the urban history studies and there are a large number of research results related to history, geography, culture, technology and many other aspects. This paper focuses on studying the spatio-temporal data characteristics and conceptual framework during the historical evolution of Beijing city, extracting the typical Beijing city historical events to establish a multi-dimension star snapshot data model (MDSSDM) based on historical events to present spatial characteristics and historical characteristics for urban form and urban spatial structure of Beijing city.

River water quality (RWQ) data has obvious characteristics of spatial and temporal distribution, and tables are conventionally exploited for storage of multi-period monitoring data of RWQ; however, neither effective visualization nor accurate analysis of the obtained data can be realized due to its dispersion character. In this paper, a novel spatiotemporal data model is proposed for RWQ data to realize conveniently data representation and spatiotemporal analysis. In this model, a spatial point, containing both location and dynamic water quality information, is considered as the basic element of river spaces, and methods of expanding a point to a line segment, a flat surface and a cube are designed respectively so as to make this model be applicable to different generalizations of river spaces. Moreover, a temporal data storage structure is designed so that efficient inquiry and advanced analysis of RWQ data can be guaranteed and the occupied memory space can be reduced. Finally, case studies are conducted by performing 3D visualization, trend analysis and anomaly identification on RWQ data, the result of which showing that tridimensional representation of RWQ data can be realized efficiently, the computational complexity is reduced significantly and the occupied memory space of monitoring data is effectively economized. Accordingly, the proposed spatiotemporal data model can contribute to the efficient visualization and advanced analysis of RWQ data.

This study investigated how to manage and use the spatio-temporal data during land use. To realize spatio-temporal data mining, the key issue to be solved in technology is how to organize spatio-temporal data. The spatio-temporal data model is just generated to solve this problem. According to analysis of spatio-temporal data changing character, two factors are very important in spatio-temporal data model. One is object, and the other is event. The object is an essential element that comprises spatio-temporal data information. The event is a fundamental factor that result in changing spatio-temporal data. A new spatio-temporal data model based on both object and event (OESTDM) was put forward to meet the demand. How to organize data, build relations between objects and storage data in spatio-temporal database are also emphatically discussed. At last, a dynamic land use information system based on OESTDM is presented. The system realized the function of managing spatio-temporal data, the retrospect of history information, tracing land parcel change, Area-Data-Change-Statistic and Analysis, and so on efficiently, by taking the land parcel data of some regions in Zhejiang province for study.

An important step in data analysis is the exploration of data. For traditional relational databases one of the most powerful tools for performing such analysis is the relational database and the aggregates and rankings that they can compute: for instance, simple statistics such as the average number of links between two types of entities (relations) are easily computed using a query on a relational database and may already provide valuable information. However, for the exploration of graph data, relational databases may not be most practical and scalable. For instance, a statistic such as the shortest path between two given nodes cannot be computed by a relational database. Surprisingly, however, tools for querying graph and network databases are much less well developed than for relational data, and only recently an increasing number of studies are devoted to graph or network databases. Our position is that the development of such graph databases is important both to make basic graph mining easier and to prepare data for more complex types of analysis. An important component of such databases is the language that is used to enable aggregating queries, such as shortest path queries.In this paper, we propose an extension to a previously proposed query language. This extension allows for querying and analyzing databases by using aggregates and ranking. A notable feature of our language is that it also supports probabilistic graph queries by conceiving of such queries as aggregating queries. We demonstrate its value on a simple data analysis task.

Dynamic monitoring of land use is a perennial and persistent process now in Shanghai. Therefore, the cumulated amount of monitoring data will be very large. It is an exigent problem how to manage and use this data effectively. The key issue is finding a suitable spatio-temporal data model that must take into account space, time and attribute factor adequately. In dynamic monitoring of land use, it is change that is of direct interest. With analyzing the feature of land use dynamic monitoring and the shortage of some spatio-temporal data models when they are used in the field, this paper proposes a Hierarchical Spatio-Temporal Data Model (HSDM) that stores elements of change and makes these available for direct query and analysis.

Recent interest in spatiotemporal data modeling has been primarily on data structures and access mechanisms, content based description and query, or extensions of existing data models to satisfy the requirements of a specific representation of space and time. However, there is a need for a general conceptual data modeling language suitable for specification and analysis of applications based on different spatiotemporal representations. In this paper, an extension of a standard object-oriented modeling language, Unified Modeling Language (UML), is proposed which includes support of object and field-based representations of space, event-based and periodic representations of time, and alternative interpolation semantics. A new concept of attribute groups is introduced to allow concise specification of common spatiotemporal properties and constraints.

BackgroundHigh-throughput transcriptomic data generated by microarray experiments is the most abundant and frequently stored kind of data currently used in translational medicine studies. Although microarray data is supported in data warehouses such as tranSMART, when querying relational databases for hundreds of different patient gene expression records queries are slow due to poor performance. Non-relational data models, such as the key-value model implemented in NoSQL databases, hold promise to be more performant solutions. Our motivation is to improve the performance of the tranSMART data warehouse with a view to supporting Next Generation Sequencing data.ResultsIn this paper we introduce a new data model better suited for high-dimensional data storage and querying, optimized for database scalability and performance. We have designed a key-value pair data model to support faster queries over large-scale microarray data and implemented the model using HBase, an implementation of Google's BigTable storage system. An experimental performance comparison was carried out against the traditional relational data model implemented in both MySQL Cluster and MongoDB, using a large publicly available transcriptomic data set taken from NCBI GEO concerning Multiple Myeloma. Our new key-value data model implemented on HBase exhibits an average 5.24-fold increase in high-dimensional biological data query performance compared to the relational model implemented on MySQL Cluster, and an average 6.47-fold increase on query performance on MongoDB.ConclusionsThe performance evaluation found that the new key-value data model, in particular its implementation in HBase, outperforms the relational model currently implemented in tranSMART. We propose that NoSQL technology holds great promise for large-scale data management, in particular for high-dimensional biological data such as that demonstrated in the performance evaluation described in this paper. We aim to use this new data model as a basis for migrating tranSMART's implementation to a more scalable solution for Big Data.

The data structure of GIS is complex and it is difficult for the traditional GIS data model to express the existence of unstructured data. Meanwhile, traditional GIS data model can not express the complex relationship such as inheritance and aggregation which widely used in GIS. This article will propose a new way of establishing mineral geography data model based on Geodatabase. Through the analysis minerals geography data, the division feature classes and the definition of feature classes and relationship classes, the characteristics of mineral dynamic data structures will be analyzed on the base of Geodatabase data structure. The objects, elements and the corresponding relationships between spatial entities will be established by the use of UML and CASE tools. The mineral geography database will be automatically generated and the accuracy of mapping relationships between data will be ensured by the use of this method.

A considerable amount of data emerging in spatiotemporal applications requires much attention on managing spatiotemporal data both from academia and industry. On the other hand, XML has several excellent characteristics due to its simplicity, readability, and portability. However, although XML has been employed to model and handle spatiotemporal data, the study of spatiotemporal XML data has only recently started and still merits further attention. In this paper, we study spatiotemporal data model for sea surface meteorological data in XML, and then propose marking dictionaries for sea surface meteorological data in XML. The feature of our approach is that our spatiotemporal data model is application-oriented. Moreover, such approach could provide consolidations of application-oriented spatiotemporal models.

The real-time accurate description of all spatial features of railway and their spatiotemporal relationships is a crucial factor in realizing comprehensive management and related decision-making within the entire life cycle of railways. Nevertheless, available spatiotemporal data models mainly use static historical sequence data, which are insufficient to support multi-source heterogeneous real-time sensed data; they lack a systematic depiction of the interactive relationships among multiple feature entities, and are limited to low-level descriptive analysis. Therefore, this study proposes a data-model-knowledge integrated representation data model for a digital twin railway, which explicitly describes the spatiotemporal, and interaction relationships among railway features through a conceptual knowledge graph. This study first analyzes the characteristics of railway features from above ground to underground, and then constructs a conceptual model to clearly describe the complex relationships among railway features. Secondly, a logical model is developed to illustrate the basic data structure. Thirdly, an ontology model is constructed as a basic framework for further deepening the domain knowledge graph. Finally, considering the prevention of landslides as an example, it demonstrates the abundant spatiotemporal relationships among railway related features. The results of this study bring more clear understanding of the complex interactive relationships of railway entities.

In developing NoSQL databases, a major motivation is to achieve better efficient query performance compared with relational databases. The graph database is a NoSQL paradigm where navigation is based on links instead of joining tables. Links can be implemented as pointers, and following a pointer is a constant time operation, whereas joining tables is more complicated and slower, even in the presence of foreign keys. Therefore, link-based navigation has been seen as a more efficient query approach than using join operations on tables. Existing studies strongly support this assumption. However, query complexity has received less attention. For example, in enterprise information systems, queries are usually complex so data need to be collected from several tables or by traversing paths of graph nodes of different types. In the present study, we compared the query performance of a graph-based database system (Neo4j) and relational database systems (MySQL and MariaDB). The effect of different efficiency issues (e.g., indexing and optimization) were included in the comparison in order to investigate the most efficient solutions for different query types. The outcome is that although Neo4j is more efficient for simple queries, MariaDB is essentially more efficient when the complexity of queries increases. The study also highlighted how dramatically the efficiency of relational database has grown during the last decade.