Computational Model for Efficient Processing of Geofield Queries

Bajerski, Piotr; Kozielski, Stanisław

doi:10.1007/978-3-642-00563-3_60

Cited by 13 publications

(2 citation statements)

References 3 publications

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“…The study [114] reported that researchers first became aware of spatial data in Geographic Information Systems foundational writing. Two problems with effective geospatial query data processing were investigated in the research [115,116]. The first was that conventional optimization methods were inadequate for solving geographical queries.…”

Section: Empirical Studies Analysismentioning

confidence: 99%

SQL and NoSQL Database Software Architecture Performance Analysis and Assessments—A Systematic Literature Review

Khan

Kumar

Cheng

et al. 2023

BDCC

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The competent software architecture plays a crucial role in the difficult task of big data processing for SQL and NoSQL databases. SQL databases were created to organize data and allow for horizontal expansion. NoSQL databases, on the other hand, support horizontal scalability and can efficiently process large amounts of unstructured data. Organizational needs determine which paradigm is appropriate, yet selecting the best option is not always easy. Differences in database design are what set SQL and NoSQL databases apart. Each NoSQL database type also consistently employs a mixed-model approach. Therefore, it is challenging for cloud users to transfer their data among different cloud storage services (CSPs). There are several different paradigms being monitored by the various cloud platforms (IaaS, PaaS, SaaS, and DBaaS). The purpose of this SLR is to examine the articles that address cloud data portability and interoperability, as well as the software architectures of SQL and NoSQL databases. Numerous studies comparing the capabilities of SQL and NoSQL of databases, particularly Oracle RDBMS and NoSQL Document Database (MongoDB), in terms of scale, performance, availability, consistency, and sharding, were presented as part of the state of the art. Research indicates that NoSQL databases, with their specifically tailored structures, may be the best option for big data analytics, while SQL databases are best suited for online transaction processing (OLTP) purposes.

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Section: Empirical Studies Analysismentioning

confidence: 99%

SQL and NoSQL Database Software Architecture Performance Analysis and Assessments—A Systematic Literature Review

Khan

Kumar

Cheng

et al. 2023

BDCC

View full text Add to dashboard Cite

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“…By spatial coding, as Fig. 2 shows, each spatial object will be encoded with unique order code to manage (Abel and Smith, 1983;Bajerski, 2008;Bajerski and Kozielski, 2009). Aside from the fact that spatial coding improves the overall manageability of spatial datasets, it also improves spatial data processing in two ways.…”

Section: Spatial Codingmentioning

confidence: 99%

Spatial coding-based approach for partitioning big spatial data in Hadoop

Yao

Mokbel

Alarabi

et al. 2017

Computers & Geosciences

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Spatial data partitioning (SDP) plays a powerful role in distributed storage and parallel computing for spatial data. However, due to skew distribution of spatial data and varying volume of spatial vector objects, it leads to a significant challenge to ensure both optimal performance of spatial operation and data balance in the cluster. To tackle this problem, we proposed a spatial coding-based approach for partitioning big spatial data in Hadoop. This approach, firstly, compressed the whole big spatial data based on spatial coding matrix to create a sensing information set (SIS), including spatial code, size, count and other information. SIS was then employed to build spatial partitioning matrix, which was used to spilt all spatial objects into different partitions in the cluster finally. Based on our approach, the neighbouring spatial objects can be partitioned into the same block. At the same time, it also can minimize the data skew in Hadoop distributed file system (HDFS). The presented approach with a case study in this paper is compared against random sampling based partitioning, with three measurement standards, namely, the spatial index quality, data skew in HDFS, and range query performance. The experimental results show that our method based on spatial coding technique can improve the query performance of big spatial data, as well as the data balance in HDFS. We implemented and deployed this approach in Hadoop, and it is also able to support efficiently any other distributed big spatial data systems.

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