scholarly journals Benchmarking geospatial database on Kubernetes cluster

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Bharti Sharma ◽  
Poonam Bansal ◽  
Mohak Chugh ◽  
Adisakshya Chauhan ◽  
Prateek Anand ◽  
...  
Keyword(s):  
Spatial Data ◽  
Query Language ◽  
Research Work ◽  
Nosql Databases ◽  
Geospatial Database ◽  
Computationally Expensive ◽  
Execution Times ◽  
Container Orchestration ◽  
Geospatial Databases ◽  
Geo Database

AbstractKubernetes is an open-source container orchestration system for automating container application operations and has been considered to deploy various kinds of container workloads. Traditional geo-databases face frequent scalability issues while dealing with dense and complex spatial data. Despite plenty of research work in the comparison of relational and NoSQL databases in handling geospatial data, there is a shortage of existing knowledge about the performance of geo-database in a clustered environment like Kubernetes. This paper presents benchmarking of PostgreSQL/PostGIS geospatial databases operating on a clustered environment against non-clustered environments. The benchmarking process considers the average execution times of geospatial structured query language (SQL) queries on multiple hardware configurations to compare the environments based on handling computationally expensive queries involving SQL operations and PostGIS functions. The geospatial queries operate on data imported from OpenStreetMap into PostgreSQL/PostGIS. The clustered environment powered by Kubernetes demonstrated promising improvements in the average execution times of computationally expensive geospatial SQL queries on all considered hardware configurations compared to their average execution times in non-clustered environments.

scholarly journals Performance Benchmarking of Key-Value Store NoSQL Databases

2018 ◽  
Vol 8 (6) ◽  
pp. 5333
Author(s):  
Omoruyi Osemwegie ◽  
Kennedy Okokpujie ◽  
Nsikan Nkordeh ◽  
Charles Ndujiuba ◽  
Samuel John ◽  
...  
Keyword(s):  
Data Storage ◽  
Web Applications ◽  
Query Language ◽  
Research Work ◽  
Database Systems ◽  
Nosql Databases ◽  
Performance Benchmarking ◽  
Nosql Database ◽  
Structured Query Language ◽  
Web Developers

<p>Increasing requirements for scalability and elasticity of data storage for web applications has made Not Structured Query Language NoSQL databases more invaluable to web developers. One of such NoSQL Database solutions is Redis. A budding alternative to Redis database is the SSDB database, which is also a key-value store but is disk-based. The aim of this research work is to benchmark both databases (Redis and SSDB) using the Yahoo Cloud Serving Benchmark (YCSB). YCSB is a platform that has been used to compare and benchmark similar NoSQL database systems. Both databases were given variable workloads to identify the throughput of all given operations. The results obtained shows that SSDB gives a better throughput for majority of operations to Redis’s performance.</p>

A Method of Structure Comparison Using Spatial Topological Patterns

2005 ◽  
Vol 277-279 ◽  
pp. 272-277
Author(s):  
Sung Hee Park ◽  
Keun Ho Ryu
Keyword(s):  
Spatial Data ◽  
Similarity Search ◽  
3D Structure ◽  
Structural Similarity ◽  
Query Protein ◽  
Data Types ◽  
Structure Comparison ◽  
3D Space ◽  
Structure Databases ◽  
Computationally Expensive

The problem of comparison of structural similarity has been complex and computationally expensive. The first step to solve comparison of structural similarity in 3D structure databases is to develop fast methods for structural similarity. Therefore, we propose a new method of comparing structural similarity in protein structure databases by using topological patterns of proteins. In our approach, the geometry of secondary structure elements in 3D space is represented by spatial data types and is indexed using Rtrees. Topological patterns are discovered by spatial topology relations based on the Rtree index join. An algorithm for a similarity search compares topological patterns of a query protein with those of proteins in structure databases by the intersection frequency of SSEs. Our experimental results show that the execution time of our method is three times faster than the generally known method DALITE. Our method can generate small candidate sets for more accurate alignment tools such as DALI and SSAP.

MonogDB

2017 ◽  
pp. 64-91
Author(s):  
Sonali Tidke
Keyword(s):  
Management System ◽  
Database Management ◽  
Query Language ◽  
Database Management System ◽  
Management Model ◽  
Nosql Databases ◽  
Performance Indexes ◽  
And Behavior ◽  
Relational Database Management ◽  
Partitioning Technique

MongoDB is a NoSQL type of database management system which does not adhere to the commonly used relational database management model. MongoDB is used for horizontal scaling across a large number of servers which may have tens, hundreds or even thousands of servers. This horizontal scaling is performed using sharding. Sharding is a database partitioning technique which partitions large database into smaller parts which are easy to manage and faster to access. There are hundreds of NoSQL databases available in the market. But each NoSQL product is different in terms of features, implementations and behavior. NoSQL and RDBMS solve different set of problems and have different requirements. MongoDB has a powerful query language which extends SQL to JSON enabling developers to take benefit of power of SQL and flexibility of JSON. Along with support for select/from/where type of queries, MongoDB supports aggregation, sorting, joins as well as nested array and collections. To improve query performance, indexes and many more features are also available.

Querying GML

2010 ◽  
pp. 11-19 ◽  
Author(s):  
Jose E. Córcoles ◽  
Pascual González
Keyword(s):  
Spatial Data ◽  
Query Language ◽  
Query Languages ◽  
Structured Data ◽  
Large Set ◽  
Topological Relationship ◽  
Xml Document ◽  
Spatial Operators

As a database format, XML (GML by extension) can be queried. In order to do this, we need a query language (of general use) to retrieve information from an XML document. Nevertheless, it is necessary to enrich the query language over XML features with spatial operators if we wish to apply it over spatial data encoded with GML. Otherwise, these query languages could only be used to query alphanumeric features of an XML document and not, for example, the topological relationship between two spatial regions. Today, there is a large set of query languages over XML. These query languages are different with respect to syntax, available operators and environment of applicability. However, they share the same features, that is, features of query languages over semi-structured data. With respect to GML, from the literature, it is known that four GML query languages have been proposed. The following chapter briefly describes these query languages over GML.

No-FSQL

2016 ◽  
Vol 5 (2) ◽  
pp. 54-63 ◽  
Author(s):  
Ines BenAli-Sougui ◽  
Minyar Sassi Hidri ◽  
Amel Grissa-Touzi
Keyword(s):  
Query Language ◽  
Nosql Databases ◽  
Database Model ◽  
Precise Data ◽  
Hands On ◽  
Ambiguous Data

NoSQL (Not only SQL) is an efficient database model for storing and manipulating huge quantities of precise data. However, most NoSQL databases scale well as data grows and often are flexible enough to accommodate imprecise and ambiguous data. This comprehensive hands-on guide presents fundamental concepts and practical solutions for using fuzziness with NoSQL to deals with fuzzy databases (FDB). In this paper, the authors present a graph-based fuzzy NoSQL model to deal with large fuzzy databases while extending the NoSQL one. The authors consider the cypher declarative query language proposed for Neo4j which is the current leader on this market to querying fuzzy databases.

SUBSUMPTION DEGREES BETWEEN ENTITY TYPES AND NAMES FOR APPROXIMATE KNOWLEDGE RETRIEVAL

2007 ◽  
Vol 15 (supp01) ◽  
pp. 21-42 ◽  
Author(s):  
TRU H. CAO ◽  
DAT T. HUYNH
Keyword(s):  
Query Language ◽  
Research Work ◽  
Population Based ◽  
Conceptual Graphs ◽  
Query Graph ◽  
Front End ◽  
The One ◽  
User Friendly ◽  
Query Modification ◽  
Source Of Information

The Web has become a huge and indispensable source of information to be used and shared globally, where knowledge is commonly represented and stored in RDF, or alternatively, in conceptual graphs. Managing and searching for web information have gone beyond the relational database model, as the data are semi-structured and inexact answers are often the case. Usually, approximate searching results are due to mismatching between entity types and names in a query and an answer. Firstly, this research work focuses on partial subsumption of a query graph to an answer graph, which is an unsymmetric measure in contrast to similarity. Secondly, it proposes a population-based method for defining subsumption degrees between entity types, one to another, and a class-sensitive soft TF-IDF method for entity names. Lastly, on the one hand, for a user-friendly interface and easily readable query expressions, conceptual graphs are employed at the front-end. On the other hand, in order to take the advantage of the existing platform of SeRQL, an exact RDF query language, the query modification tactic is used to retrieve the knowledge graphs that are close to a query graph, before the subsumption degrees of the query graph to those answer graphs are calculated.

scholarly journals Analysis of performance of selected geospatial analyses implemented on the basis of relational and NoSQL databases

2019 ◽  
Vol 51 (4) ◽  
pp. 167-179
Author(s):  
Marcin Pietroń
Keyword(s):  
Spatial Data ◽  
Large Data ◽  
Relational Model ◽  
Data Sets ◽  
Data Set ◽  
Nosql Databases ◽  
Long Time ◽  
The Right ◽  
Spatial Operators ◽  
Gis System

Abstract Databases are a basic component of every GIS system and many geoinformation applications. They also hold a prominent place in the tool kit of any cartographer. Solutions based on the relational model have been the standard for a long time, but there is a new increasingly popular technological trend – solutions based on the NoSQL database which have many advantages in the context of processing of large data sets. This paper compares the performance of selected spatial relational and NoSQL databases executing queries with selected spatial operators. It has been hypothesised that a non-relational solution will prove to be more effective, which was confirmed by the results of the study. The same spatial data set was loaded into PostGIS and MongoDB databases, which ensured standardisation of data for comparison purposes. Then, SQL queries and JavaScript commands were used to perform specific spatial analyses. The parameters necessary to compare the performance were measured at the same time. The study’s results have revealed which approach is faster and utilises less computer resources. However, it is difficult to clearly identify which technology is better because of a number of other factors which have to be considered when choosing the right tool.

scholarly journals PROVIDING R-TREE SUPPORT FOR MONGODB

2016 ◽  
Vol XLI-B4 ◽  
pp. 545-549 ◽  
Author(s):  
Longgang Xiang ◽  
Xiaotian Shao ◽  
Dehao Wang
Keyword(s):  
Spatial Data ◽  
Relational Databases ◽  
Spatial Index ◽  
Geospatial Information ◽  
Nosql Databases ◽  
Document Structure ◽  
Depth Analysis ◽  
Novel Method ◽  
Tree Index ◽  
Management Issues

Supporting large amounts of spatial data is a significant characteristic of modern databases. However, unlike some mature relational databases, such as Oracle and PostgreSQL, most of current burgeoning NoSQL databases are not well designed for storing geospatial data, which is becoming increasingly important in various fields. In this paper, we propose a novel method to provide R-tree index, as well as corresponding spatial range query and nearest neighbour query functions, for MongoDB, one of the most prevalent NoSQL databases. First, after in-depth analysis of MongoDB’s features, we devise an efficient tabular document structure which flattens R-tree index into MongoDB collections. Further, relevant mechanisms of R-tree operations are issued, and then we discuss in detail how to integrate R-tree into MongoDB. Finally, we present the experimental results which show that our proposed method out-performs the built-in spatial index of MongoDB. Our research will greatly facilitate big data management issues with MongoDB in a variety of geospatial information applications.

scholarly journals PROVIDING R-TREE SUPPORT FOR MONGODB

2016 ◽  
Vol XLI-B4 ◽  
pp. 545-549
Author(s):  
Longgang Xiang ◽  
Xiaotian Shao ◽  
Dehao Wang
Keyword(s):  
Spatial Data ◽  
Relational Databases ◽  
Spatial Index ◽  
Geospatial Information ◽  
Nosql Databases ◽  
Document Structure ◽  
Depth Analysis ◽  
Novel Method ◽  
Tree Index ◽  
Management Issues

Supporting large amounts of spatial data is a significant characteristic of modern databases. However, unlike some mature relational databases, such as Oracle and PostgreSQL, most of current burgeoning NoSQL databases are not well designed for storing geospatial data, which is becoming increasingly important in various fields. In this paper, we propose a novel method to provide R-tree index, as well as corresponding spatial range query and nearest neighbour query functions, for MongoDB, one of the most prevalent NoSQL databases. First, after in-depth analysis of MongoDB’s features, we devise an efficient tabular document structure which flattens R-tree index into MongoDB collections. Further, relevant mechanisms of R-tree operations are issued, and then we discuss in detail how to integrate R-tree into MongoDB. Finally, we present the experimental results which show that our proposed method out-performs the built-in spatial index of MongoDB. Our research will greatly facilitate big data management issues with MongoDB in a variety of geospatial information applications.

scholarly journals Efficient dam management using SQL and GIS

2020 ◽  
Vol 7 (2) ◽  
pp. 50-55
Author(s):  
Mario Jancetić ◽  
Nikola Kranjčić ◽  
Milan Rezo
Keyword(s):  
Spatial Data ◽  
Query Language ◽  
Security Risks ◽  
Management Tools ◽  
General Management ◽  
Gis Tools ◽  
Dam Management ◽  
Management Processes ◽  
Wide Range ◽  
Economical Aspect

This paper discusses use of SQL and GIS tools in nowadays dam management. Dam management requires the use of a highly-sophisticated measuring, monitoring and general management tools, since it is not only economical aspect of importance of these projects, but also about the security risks that require the highest possible caution and a precisely-developed control systems. Therefore, SQL and GIS are tools to be considered and implemented. GIS is widely used in spatial planning and connected management processes - because it allows easy way of storage, processing, analysis, modelling and display of spatial data. It has a wide range of features and is used in many areas. Structured Query Language (SQL) is a programming language for databases, written to be easy to understand and to use. SQL provides integration and presentation of data, optimization, easy reporting and analysis. In hand of trained professional analysts, SQL can make database search efficient and flexible, which is the key feature in demanding management processes as dam management).

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