Integrated k-NN Query Processing Based on Geospatial Data Services

2005 ◽  
pp. 554-559
Author(s):  
Guifen Tang ◽  
Luo Chen ◽  
Yunxiang Liu ◽  
Shulei Liu ◽  
Ning Jing
Keyword(s):  
Query Processing ◽  
Geospatial Data ◽  
Data Services

Framework for GeoSpatial Query Processing by Integrating Cassandra With Hadoop

2019 ◽  
pp. 353-388
Author(s):  
S. Vasavi ◽  
Mallela Padma Priya ◽  
Anu A. Gokhale
Keyword(s):  
Response Time ◽  
Query Processing ◽  
Geospatial Data ◽  
Distributed Data ◽  
Nosql Databases ◽  
Storage And Retrieval ◽  
Query Response Time ◽  
Partitioning Algorithm ◽  
Hadoop Framework ◽  
Partitioning Technique

We are moving towards digitization and making all our devices, such as sensors and cameras, connected to internet, producing bigdata. This bigdata has variety of data and has paved the way to the emergence of NoSQL databases, like Cassandra, for achieving scalability and availability. Hadoop framework has been developed for storing and processing distributed data. In this chapter, the authors investigated the storage and retrieval of geospatial data by integrating Hadoop and Cassandra using prefix-based partitioning and Cassandra's default partitioning algorithm (i.e., Murmur3partitioner) techniques. Geohash value is generated, which acts as a partition key and also helps in effective search. Hence, the time taken for retrieving data is optimized. When users request spatial queries like finding nearest locations, searching in Cassandra database starts using both partitioning techniques. A comparison on query response time is made so as to verify which method is more effective. Results show the prefix-based partitioning technique is more efficient than Murmur3 partitioning technique.

Framework for GeoSpatial Query Processing by Integrating Cassandra With Hadoop

2018 ◽  
pp. 1-41
Author(s):  
S. Vasavi ◽  
Mallela Padma Priya ◽  
Anu A. Gokhale
Keyword(s):  
Response Time ◽  
Query Processing ◽  
Geospatial Data ◽  
Distributed Data ◽  
Nosql Databases ◽  
Storage And Retrieval ◽  
Query Response Time ◽  
Partitioning Algorithm ◽  
Hadoop Framework ◽  
Partitioning Technique

We are moving towards digitization and making all our devices, such as sensors and cameras, connected to internet, producing bigdata. This bigdata has variety of data and has paved the way to the emergence of NoSQL databases, like Cassandra, for achieving scalability and availability. Hadoop framework has been developed for storing and processing distributed data. In this chapter, the authors investigated the storage and retrieval of geospatial data by integrating Hadoop and Cassandra using prefix-based partitioning and Cassandra's default partitioning algorithm (i.e., Murmur3partitioner) techniques. Geohash value is generated, which acts as a partition key and also helps in effective search. Hence, the time taken for retrieving data is optimized. When users request spatial queries like finding nearest locations, searching in Cassandra database starts using both partitioning techniques. A comparison on query response time is made so as to verify which method is more effective. Results show the prefix-based partitioning technique is more efficient than Murmur3 partitioning technique.

Efficient spark-based framework for big geospatial data query processing and analysis

2017 ◽  
Author(s):  
Isam Mashhour Aljawarneh ◽  
Paolo Bellavista ◽  
Antonio Corradi ◽  
Rebecca Montanari ◽  
Luca Foschini ◽  
...  
Keyword(s):  
Query Processing ◽  
Geospatial Data ◽  
Data Query

scholarly journals Web-Based Geospatial Resource Sharing Through GeoPW

2014 ◽  
Vol XL-6 ◽  
pp. 131-135
Author(s):  
X. Zhai ◽  
L. Jiang ◽  
P. Yue
Keyword(s):  
Resource Sharing ◽  
Geospatial Data ◽  
Web Based ◽  
Data Services ◽  
Web Environment ◽  
Geospatial Services ◽  
User Friendly ◽  
Web Platform ◽  
The Web ◽  
Geoprocessing Services

As Web-related technologies have matured in recent years, an increasing amount of geospatial resources (e.g. geospatial services, workflows, and geospatial data) are available in the distributed Web environment. Consequently, effective and efficient sharing and management of geospatial resources on the Web are necessary for better utilizing these resources for education and scientific research. This matches the vision of Geoprocessing Web, which emphasizes the sharing and access of geoprocessing utilities from the perspectives of communication, collaboration, and participation. Previous work on GeoPW has provided a large number of geoprocessing services over the Web. In this paper, GeoPW goes further to offer a Web platform for sharing geospatial resources. The paper presents the design, implementation, and functions of the platform, which offers a user-friendly environment for publication, discovery, and communication of geospatial data, services, and workflows.

The Evolution of SDI Geospatial Data Clearinghouses

2011 ◽  
pp. 802-809 ◽  
Author(s):  
Caitlin Kelly Maurie
Keyword(s):  
Spatial Data ◽  
Data Transfer ◽  
Google Earth ◽  
Spatial Data Infrastructure ◽  
Geospatial Data ◽  
Time Data ◽  
Geospatial Information ◽  
Data Infrastructure ◽  
Data Services ◽  
The World

Geospatial data and the technologies that drive them have altered the landscape of our understanding of the world around us. The data, software and services related to geospatial information have given us the opportunity to visualize existing phenomena, to understand connections, and to address problems from environmental management to emergency response. From the everpresent Google Earth images we are shown in our televised weather reports to the 3D flyovers of war zones on the news, geospatial information is everywhere. In the decade or so since U.S. President William Clinton set the stage by announcing the establishment of the National Spatial Data Infrastructure (NSDI), the concept of the geospatial data clearinghouse has shifted dramatically to fulfill the increasing need to streamline government processes, increase collaboration, and to meet the demands of data users and data developers (Clinton, 1994). The announcement of the NSDI gave birth to a Global Spatial Data Infrastructure (GSDI) movement that would be supported by a network of SDIs or geospatial data clearinghouses from local, state, and national levels. From this point on, the evolution of the geospatial data clearinghouse has been rapid and punctuated with challenges to both the developer and the user. From the earliest incarnations of these now pervasive resources as simple FTP data transfer sites to the latest developments in Internet Map Services and real time data services, geospatial data clearinghouses have provided the backbone for the exponential growth of Geographic Information Systems (GIS). In this section, the authors will examine the background of the geospatial data clearinghouse movement, address the basic phases of clearinghouse development, and review the trends that have taken the world’s clearinghouses from FTP to Internet Map Services and beyond.

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