A Novel Query Method for Spatial Data in Mobile Cloud Computing Environment

With the development of network communication, a 1000-fold increase in traffic demand from 4G to 5G, it is critical to provide efficient and fast spatial data access interface for applications in mobile environment. In view of the low I/O efficiency and high latency of existing methods, this paper presents a memory-based spatial data query method that uses the distributed memory file system Alluxio to store data and build a two-level index based on the Alluxio key-value structure; moreover, it aims to solve the problem of low efficiency of traditional method; according to the characteristics of Spark computing framework, a data input format for spatial data query is proposed, which can selectively read the file data and reduce the data I/O. The comparative experiments show that the memory-based file system Alluxio has better I/O performance than the disk file system; compared with the traditional distributed query method, the method we proposed reduces the retrieval time greatly.

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Shared-Disk File System

Encyclopedia of Database Systems ◽

10.1007/978-0-387-39940-9_3599 ◽

2009 ◽

pp. 2637-2637

Keyword(s):

File System ◽

Disk File

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Upper Mississippi River Restoration Program Long Term Resource Monitoring element—Spatial data query tool

Fact Sheet ◽

10.3133/fs20183077 ◽

2018 ◽

Author(s):

Jason J. Rohweder

Keyword(s):

Mississippi River ◽

Spatial Data ◽

River Restoration ◽

Upper Mississippi River ◽

Resource Monitoring ◽

Data Query ◽

Query Tool

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DEVELOPMENT OF A HETEROGENIC DISTRIBUTED ENVIRONMENT FOR SPATIAL DATA PROCESSING USING CLOUD TECHNOLOGIES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b4-385-2016 ◽

2016 ◽

Vol XLI-B4 ◽

pp. 385-390

Author(s):

A. S. Garov ◽

I. P. Karachevtseva ◽

E. V. Matveev ◽

A. E. Zubarev ◽

I. V. Florinsky

Keyword(s):

Data Processing ◽

Spatial Data ◽

3D Visualization ◽

New Technology ◽

Main Idea ◽

Remote Sensing Data ◽

Data Access ◽

Working Environment ◽

Software Environment ◽

Spatial Data Processing

We are developing a unified distributed communication environment for processing of spatial data which integrates web-, desktop- and mobile platforms and combines volunteer computing model and public cloud possibilities. The main idea is to create a flexible working environment for research groups, which may be scaled according to required data volume and computing power, while keeping infrastructure costs at minimum. It is based upon the "single window" principle, which combines data access via geoportal functionality, processing possibilities and communication between researchers. Using an innovative software environment the recently developed planetary information system (<a href="http://cartsrv.mexlab.ru/geoportal"target="_blank">http://cartsrv.mexlab.ru/geoportal</a>) will be updated. The new system will provide spatial data processing, analysis and 3D-visualization and will be tested based on freely available Earth remote sensing data as well as Solar system planetary images from various missions. Based on this approach it will be possible to organize the research and representation of results on a new technology level, which provides more possibilities for immediate and direct reuse of research materials, including data, algorithms, methodology, and components. The new software environment is targeted at remote scientific teams, and will provide access to existing spatial distributed information for which we suggest implementation of a user interface as an advanced front-end, e.g., for virtual globe system.

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Integrating cellular automata and discrete global grid systems: a case study into wildfire modelling

AGILE: GIScience Series ◽

10.5194/agile-giss-1-6-2020 ◽

2020 ◽

Vol 1 ◽

pp. 1-23

Author(s):

Majid Hojati ◽

Colin Robertson

Keyword(s):

Big Data ◽

Spatial Analysis ◽

Cellular Automata ◽

Spatial Data ◽

Data Model ◽

Data Access ◽

Environmental Modeling ◽

Modeling Framework ◽

Global Grid

Abstract. With new forms of digital spatial data driving new applications for monitoring and understanding environmental change, there are growing demands on traditional GIS tools for spatial data storage, management and processing. Discrete Global Grid System (DGGS) are methods to tessellate globe into multiresolution grids, which represent a global spatial fabric capable of storing heterogeneous spatial data, and improved performance in data access, retrieval, and analysis. While DGGS-based GIS may hold potential for next-generation big data GIS platforms, few of studies have tried to implement them as a framework for operational spatial analysis. Cellular Automata (CA) is a classic dynamic modeling framework which has been used with traditional raster data model for various environmental modeling such as wildfire modeling, urban expansion modeling and so on. The main objectives of this paper are to (i) investigate the possibility of using DGGS for running dynamic spatial analysis, (ii) evaluate CA as a generic data model for dynamic phenomena modeling within a DGGS data model and (iii) evaluate an in-database approach for CA modelling. To do so, a case study into wildfire spread modelling is developed. Results demonstrate that using a DGGS data model not only provides the ability to integrate different data sources, but also provides a framework to do spatial analysis without using geometry-based analysis. This results in a simplified architecture and common spatial fabric to support development of a wide array of spatial algorithms. While considerable work remains to be done, CA modelling within a DGGS-based GIS is a robust and flexible modelling framework for big-data GIS analysis in an environmental monitoring context.

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Multistage Dynamic Packet Access Mechanism of Internet of Things

Mobile Information Systems ◽

10.1155/2018/3601604 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16

Author(s):

Yi Meng ◽

Chen QingKui ◽

Zhang Gang

Keyword(s):

Internet Of Things ◽

Resource Competition ◽

Data Access ◽

The Internet ◽

Network Access ◽

Feature Maps ◽

Control Command ◽

Packet Access ◽

Low Efficiency ◽

The Internet Of Things

In the scenario of mass control commands requesting for network access, confined by the best-effort network service mode, it is easy to bring about resource competition and thus a phenomenon of access failure on major and urgent service request at the data access center for the Internet of Things. In this event, the dynamic diversification of control command is unable to access the necessary resources on a comparatively fair basis, causing low efficiency in heterogeneous resource utilization at the access center. This paper defines the problem of group request dynamic resource allocation and further converts it into the problem of 0-1 integer and linear programming and proposes a multistage dynamic packet access strategy. This strategy works first on dynamic group division on the users’ mass control requests using the high ability of self-organizing feature maps and then searches for the optimized matching resources based on the frog-leaping algorithm which has a better capacity for global searching for the best resources. This paper analyzes the feasibility of this strategy and its astringency. The experimental results demonstrate that the strategy can effectively improve the success rate of access to the data center for the Internet of Things and reduce network blockage and response delay.

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An implementation and performance analysis of spatial data access methods

[1989] Proceedings. Fifth International Conference on Data Engineering ◽

10.1109/icde.1989.47268 ◽

2003 ◽

Cited By ~ 45

Author(s):

D. Greene

Keyword(s):

Performance Analysis ◽

Spatial Data ◽

Data Access ◽

Access Methods ◽

And Performance

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Towards a Rwandan NSDI

Geographic Information Systems ◽

10.4018/978-1-4666-2038-4.ch121 ◽

2013 ◽

pp. 2040-2050

Author(s):

Felicia O. Akinyemi

Keyword(s):

Spatial Data ◽

Information Technologies ◽

Data Use ◽

Data Access ◽

Data Infrastructure ◽

Geographic Information Technologies ◽

Key Players ◽

Data Policy ◽

Governmental Institutions ◽

Multi Stakeholder

Awareness of the importance of spatial data in achieving development strategies is high in Rwanda. Government and non-governmental institutions are aspiring to use Geographic Information Technologies (GITs) in their day-to-day activities. The non-existence of a National Spatial Data Infrastructure (NSDI) in Rwanda brings to light serious issues for consideration. Still lacking is a spatial data policy relating to spatial data use. A mechanism to ease spatial data access and sharing is imperative. This paper describes SDI related efforts in Rwanda in a bid to establish the NSDI. Employing a multi-stakeholder approach to drive the process is advocated. To support this, SDI models in some countries are presented that could be applicable to the Rwandan context. Key players with potential roles in the NSDI were identified.

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Data Broadcasting in a Mobile Environment

Wireless Information Highways ◽

10.4018/978-1-59140-568-9.ch004 ◽

2011 ◽

pp. 96-154 ◽

Cited By ~ 5

Author(s):

A.R. Hurson ◽

Y. Jiao

Keyword(s):

Wireless Communication ◽

Mobile Devices ◽

High Performance ◽

Data Access ◽

Data Retrieval ◽

Mobile Environment ◽

Retrieval Method ◽

Shared Data ◽

Communication Techniques ◽

Public Data

The advances in mobile devices and wireless communication techniques have enabled anywhere, anytime data access. Data being accessed can be categorized into three classes: private data, shared data, and public data. Private and shared data are usually accessed through on-demand-based approaches, while public data can be most effectively disseminated using broadcasting. In the mobile computing environment, the characteristics of mobile devices and limitations of wireless communication technology pose challenges on broadcasting strategy as well as data-retrieval method designs. Major research issues include indexing scheme, broadcasting over single and parallel channels, data distribution and replication strategy, conflict resolution, and data retrieval method. In this chapter, we investigate solutions proposed for these issues. High performance and low power consumption are the two main objectives of the proposed schemes. Comprehensive simulation results are used to demonstrate the effectiveness of each solution and compare different approaches.

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