Python Web Server for Sensor Data Visualization

Pohanka, Tomáš

doi:10.5593/sgem2016/b21/s08.101

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are typical scenarios where database replications are used, e.g., secure lower latency for long-distance communication, separate raw data storage and user data storage, secure high availability and performance. Replication mechanisms are commonly used for web applications (e.g., sensor web and WebGIS), or as georeplication for lower intercontinental latency [6,7]. Replication can be improperly considered as a mechanism for creating data backups.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Replication Mechanisms on Selected Database Systems

Pohanka

Pechanec

2020

IJGI

View full text Add to dashboard Cite

This paper is focused on comparing database replication over spatial data in PostgreSQL and MySQL. Database replication means solving various problems with overloading a single database server with writing and reading queries. There are many replication mechanisms that are able to handle data differently. Criteria for objective comparisons were set for testing and determining the bottleneck of the replication process. The tests were done over the real national vector spatial datasets, namely, ArcCR500, Data200, Natural Earth and Estimated Pedologic-Ecological Unit. HWMonitor Pro was used to monitor the PostgreSQL database, network and system load. Monyog was used to monitor the MySQL activity (data and SQL queries) in real-time. Both database servers were run on computers with the Microsoft Windows operating system. The results from the provided tests of both replication mechanisms led to a better understanding of these mechanisms and allowed informed decisions for future deployment. Graphs and tables include the statistical data and describe the replication mechanisms in specific situations. PostgreSQL with the Slony extension with asynchronous replication synchronized a batch of changes with a high transfer speed and high server load. MySQL with synchronous replication synchronized every change record with low impact on server performance and network bandwidth.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluation of Replication Mechanisms on Selected Database Systems

Pohanka

Pechanec

2020

IJGI

View full text Add to dashboard Cite

show abstract

Python for Geospatial Data Analysis

Sunitha,

Suma,

Galety

et al. 2024

Advances in Geospatial Technologies

View full text Add to dashboard Cite

This chapter serves as a valuable resource for computer science enthusiasts, researchers, and practitioners seeking an understanding of geospatial analysis with Python. The chapter begins with an introduction to geospatial analysis, highlighting the significance of geospatial data across various domains. It establishes Python's stand in this field, positioning it as a powerful tool for geospatial analyses. The subsequent sections explore fundamental concepts, such as vector versus raster data, coordinate reference systems and projections, geometric objects, topological relationships, and spatial operations. Further, prominent Python libraries for geospatial analysis are explored. GeoPandas is introduced, detailing its capabilities in working with geospatial data, handling geometric data structures, and leveraging spatial operations. Shapely is examined for its role in geometric manipulations. Fiona is explored as a library for handling geospatial data. Discussion on Folium showcases its utility in creating interactive and customized maps.

show abstract

Python Web Server for Sensor Data Visualization

Cited by 2 publications

References 0 publications

Evaluation of Replication Mechanisms on Selected Database Systems

Evaluation of Replication Mechanisms on Selected Database Systems

Python for Geospatial Data Analysis

Contact Info

Product

Resources

About