Godec: An Open-Source Data Processing Framework for Deploying ML Data Flows in Edge-Computing Environments

2020 ◽  
pp. 84-93
Author(s):  
Ralf Meermeier ◽  
Le Zhang ◽  
Francis Keith ◽  
William Hartmann ◽  
Stavros Tsakalidis ◽  
...  
Keyword(s):  
Data Processing ◽  
Open Source ◽  
Edge Computing ◽  
Open Source Data ◽  
Data Flows ◽  
Source Data ◽  
Computing Environments ◽  
Processing Framework

Open Source Software (OSS) for Big Data

2021 ◽  
pp. 858-875
Author(s):  
Richard S. Segall
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Open Source ◽  
Open Source Software ◽  
Statistical Software ◽  
Big Data Processing ◽  
Open Source Data ◽  
Source Data ◽  
And Storage ◽  
Aggregation Analysis

This chapter discusses Open Source Software and associated technologies for the processing of Big Data. This includes discussions of Hadoop-related projects, the current top open source data tools and frameworks such as SMACK that is acronym for open source technologies Spark, Mesos, Akka, Cassandra, and Kafka that together compose the ingestion, aggregation, analysis, and storage layers for Big Data processing. Tabular summaries and categories for 38 Open Source Statistical Software (OSSS) are provided that include for each listing of features and URLs for free downloads. The current challenges of Big Data and Open Source Software are also discussed.

Open Source Software (OSS) for Big Data

2020 ◽  
pp. 50-72
Author(s):  
Richard S. Segall
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Open Source ◽  
Open Source Software ◽  
Statistical Software ◽  
Big Data Processing ◽  
Open Source Data ◽  
Source Data ◽  
And Storage ◽  
Aggregation Analysis

This chapter discusses Open Source Software and associated technologies for the processing of Big Data. This includes discussions of Hadoop-related projects, the current top open source data tools and frameworks such as SMACK that is acronym for open source technologies Spark, Mesos, Akka, Cassandra, and Kafka that together compose the ingestion, aggregation, analysis, and storage layers for Big Data processing. Tabular summaries and categories for 38 Open Source Statistical Software (OSSS) are provided that include for each listing of features and URLs for free downloads. The current challenges of Big Data and Open Source Software are also discussed.

Using Open-Source Data in Correlative Species Distribution Modeling of Marine Species

2018 ◽  
Vol 80 (6) ◽  
pp. 457-461
Author(s):  
Carlos A. Morales-Ramirez ◽  
Pearlyn Y. Pang
Keyword(s):  
Open Source ◽  
Species Distribution ◽  
Open Source Software ◽  
Species Distribution Modeling ◽  
Science Research ◽  
Marine Species ◽  
Distribution Modeling ◽  
Open Source Data ◽  
Rapid Changes ◽  
Source Data

Open-source data are information provided free online. It is gaining popularity in science research, especially for modeling species distribution. MaxEnt is an open-source software that models using presence-only data and environmental variables. These variables can also be found online and are generally free. Using all of these open-source data and tools makes species distribution modeling (SDM) more accessible. With the rapid changes our planet is undergoing, SDM helps understand future habitat suitability for species. Due to increasing interest in biogeographic research, SDM has increased for marine species, which were previously not commonly found in this modeling. Here we provide examples of where to obtain the data and how the modeling can be performed and taught.

Modelling urban energy requirements using open source data and models

2018 ◽  
Vol 231 ◽  
pp. 1100-1108 ◽  
Author(s):  
Alaa Alhamwi ◽  
Wided Medjroubi ◽  
Thomas Vogt ◽  
Carsten Agert
Keyword(s):  
Open Source ◽  
Energy Requirements ◽  
Open Source Data ◽  
Source Data ◽  
Urban Energy

scholarly journals Method to Characterize Potential UAS Encounters Using Open Source Data

Aerospace ◽  
2020 ◽  
Vol 7 (11) ◽  
pp. 158
Author(s):  
Andrew Weinert
Keyword(s):  
Open Source ◽  
Unmanned Aircraft ◽  
Unmanned Aerial Systems ◽  
Horizontal Distance ◽  
Geospatial Information ◽  
Open Source Data ◽  
Source Data ◽  
Aircraft Trajectories ◽  
Processing Techniques ◽  
Aerial Systems

As unmanned aerial systems (UASs) increasingly integrate into the US national airspace system, there is an increasing need to characterize how commercial and recreational UASs may encounter each other. To inform the development and evaluation of safety critical technologies, we demonstrate a methodology to analytically calculate all potential relative geometries between different UAS operations performing inspection missions. This method is based on a previously demonstrated technique that leverages open source geospatial information to generate representative unmanned aircraft trajectories. Using open source data and parallel processing techniques, we performed trillions of calculations to estimate the relative horizontal distance between geospatial points across sixteen locations.

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