How digital twins use huge data to reflect the real world

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Abstract and 1 introduction

1.1. Spatial DVD (SDTS)

1.2. Applications

1.3. Various components of SDTS

1.4. The scope of this work and contributions

2. Related work and 2.1. Digital twins and variables

2.2. Spatial Digital Twin Studies

3. Building blocks of spatial digital twins and 3.1. Get and process data

3.2. Data modeling, storage and management

3.3. Huge data analysis system

3.4. Maps and intermediate programs based on geographic information systems

3.5. Main functional ingredients

4. Other related modern technologies and 4.1. Amnesty International and ML

4.2. Blockchain

4.3. Cloud computing

5. Challenges and future work, and 5.1. Acquire multi -defined and accurate data

5.2. NLP Spatial Information and 5.3. Measurement with databases and huge data platforms for SDT

5.4. Spatial visions and 5.5. Multi -media

5.6. Build an simulator environment

5.7. Imagine complex and varied interactions

5.8. Reducing security and privacy concerns

6. Conclusion and references

3.3. Huge data analysis system

The rapid use and season of applications based on the mobile phone, cars that support GPS, independent cars, drones, Internet of Things, satellite images, and more to an unprecedented generation of temporal spatial data. For example, every day, nearly a billion tweets are created, with 30-40 % of them contain geographical location information. An independent vehicle at the bottom of the independent spectrum produces about 1.4 terabytes per hour[7]. To host this large volume of spatial and time data in SDT, and support various analytical and inquiries, recent research focuses on spatial extensions for huge data analysis systems such as Hadooop [51]Spark [52]And nosql [46]. Large data systems can make this SDT development system through distributed processing.

Hadoub [51] The Mapreduce framework is used for distributed processing for huge data. Due to the lack of support for spatial temporal data in Hadoop, a number of extensions like Hadoop-GIS [53] And Spatialhadoop [54] It was developed to deal with the types of spatial data such points, paths, etc. Since Hadoop is a disk -based system, performance can deteriorate with a large number of input/outputs. To solve this problem, major data -based data systems such as Spatialspark [55]Geospark [56] And so on has been developed. Another set of large spatial data processing systems appeared using the NosQL model, for example, MD-HBASE [57]Trajmesa [58]Etc.

Since the temporal spatial data grows at an unprecedented rate and SDTS needs to host different types of data to find visions and to help make predictive decisions, we need help from these large spatial data processing systems in building SDT. However, unlike database management systems that have contracts for research behind them and have been widely tested, these large spatial data processing systems are still in their growing phase and there is no standard study on the performance of these systems using different spatial data and data flow types.

3.4. Maps and intermediate programs based on geographic information systems

In this section, we discuss programs and tools that are used to visualize spatial (or geographical) data on the map, and to perform various spatial processes easily. We classified these tools and programs into three groups: GIS; Basic maps; Apis and libraries. These systems are necessary for smart, rapid and effective development of SDT. After that, we provide details.

3.4.1. Geographical Information Systems Program

The integrated geographic information system (GIS) provides an environment that integrates and interacts with spatial data, and tools for various operations on these data. It also provides platforms to find interesting patterns and visions of data and portray the map. Some widespread geographic information systems programs are Arcgis (from ESRI) [59]And qgis (open source) [60]. This GIS program has the support of all basic spatial data and analysis facilities and a map depicting. It can also be combined with DBMSS and other basic spatial data analysis platforms. This program also has programming support for different languages ​​such as Python, R, etc. [61].

As one of the main features of SDT is the perception and interaction with the urban environment, OpenSource 3D Globe, Cesium platform [62] It was developed. Cesium enables developers to build spatial geographical applications around 3D maps, Cesium is particularly improved for the perception of large data collections, such as terrain, buildings and actual time sensors, and creating overwhelming geographical geographical experiences. Cesiumjs is the open source Javascript Library to create global 3D balls and maps with the best possible performance, accuracy, visual quality, and ease of use. Digital twins in New South Wales [63] Use Cesium to build spatial digital twins.

3.4.2. Map services

On a wide level, we can display current map services such as OpenStreetmap, Bing Maps or Google Maps as a spatial digital twin. These platforms create and benefit from the digital representations of material road networks, rivers and attention points, and more than that to address various inquiries, such as locating the point of interest or finding a road to the destination. There are a number of different layers of maps including satellite maps, traffic maps, terrain maps, and 3D maps are widely available in these map services. In addition to these basic layers that can be used from various MAP services as applications interfaces, SDT needs a number of other map layers such as the Equipment Map layer, the internal map layer or any other layer based on the tool such as greenhouse gas emissions or energy use. Thus, the map layers play a vital role in developing the SDT. Thus, one can use map services widely used as a base for their SDT building.

3.4.3. Tools and interface programming interface and libraries

To date, we have seen a wide range of spatial technologies such as RDBMSS, Geojson Files, GIS software, huge spatial data analysis platforms that are used to manage and process spatial data. SDT depends on the integrated capabilities of many of these platforms. Thus, to build a SDT, we need strong support for libraries and a different programming programming interface to merge the SDT platform with standard GIS platforms, spatial technology, performing processing, analysis, mining, and different visualization. A variety of applications and libraries with spatial capabilities is available in almost every common language such as C ++/Java, Python, etc.

Low -level libraries like JTS [64]GIS [65]4j [66] It was developed using C ++ or Java for modeling and processing spatial data (for example, creating vector engineering). These founding libraries are used for spatial data modeling to create geographical information systems programs, handling large spatial data and analytical systems, etc., with high -level language popularity, such as Python and R, for large and processing data analyzes, a number of applications and libraries programming facades are available. These libraries are primarily designed for various spatial data tasks including bringing the I/O improved data, processing spatial data using different spatial algorithms, conducting the analysis using statistical and advanced ML, and depicting different types of spatial data. We briefly discuss some Python libraries and an application programming interface because Python seems to be the most popular language in processing huge data in the AI ​​& ML era.

Geopanda [67] It is a spatial extension of Pandas, the famous Python Library of Data Science. Skype. Spatial [68] It contains libraries for spatial algorithms and data structures such as the nearest neighbor search algorithm. Pysal spatial analysis library (PYSAL) [69] It is designed for spatial data analysis tasks such as assembly, hot point analysis, correlation, perception, etc. It includes support for dealing with both vectors and bitten spatial data. To manage and process the path data, Python has traja [70] library. Regardless of the above libraries, there are many other spatial libraries that can be useful for the development of SDT (for example, see [71] For more details).

Regardless of the above programs and tools, some commercial software solutions [12] It was recently developed by combining many spatial tools and technologies above, which can also be used as a starting base for SDT development. Microsoft Azure Digital Twins platform [14] One enables the creation of a digital twin in the form of a graph of knowledge, which allows developers to create a digital twin data form to represent real assets and their relationships with each other. It also allows smooth complementarity with Internet Internet devices to feed continuous data. 3dexperience [15] The Basic System (by Dassault Systems) provides tools to create 3D models as well as a number of simulation tools to simulate real life creatures and their processes. Hxdr platform [72] (By Hexagonis) is a Saas platform that focuses mainly on providing the necessary tools to create a similar 3D copy of urban environments. Forest scheme [73] By Bentely Systems is the city scale app software.