Chinese Open Source Data Collection, Big Data, And Private Enterprise Work For State Intelligence and Security: The Case of Shenzhen Zhenhua

2020 ◽  
Author(s):  
Christopher Balding
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Open Source ◽  
Private Enterprise ◽  
Open Source Data ◽  
Source Data

scholarly journals Open Source Data Collection in the Developing World

Computer ◽  
2009 ◽  
Vol 42 (10) ◽  
pp. 97-99 ◽  
Author(s):  
Yaw Anokwa ◽  
Carl Hartung ◽  
Waylon Brunette ◽  
Gaetano Borriello ◽  
Adam Lerer
Keyword(s):  
Data Collection ◽  
Open Source ◽  
Developing World ◽  
Open Source Data ◽  
Source Data

scholarly journals Comparative Study of Open Source Data Mining Software for Big Data

2016 ◽  
Vol 07 (03) ◽  
pp. 31-33
Author(s):  
ATIF AZIZ ◽  
◽  
RAJEEV ARYA ◽  
SANA SHAFIQUE ◽  
◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Comparative Study ◽  
Open Source ◽  
Open Source Data ◽  
Source Data

scholarly journals Indian Premier League Dataset Analytics using Hadoop-Hive

2019 ◽  
Vol 9 (2) ◽  
pp. 3999-4004
Keyword(s):  
Big Data ◽  
Open Source ◽  
Database Systems ◽  
Huge Amount ◽  
Premier League ◽  
Open Source Data ◽  
Open Source Framework ◽  
Source Data ◽  
Set Up ◽  
Processing Techniques

Big Data is a term used to represent huge volume of both unstructured and structured data which cannot be processed by the traditional data processing techniques. This data is too huge, grows exponentially and doesn't fit into the structure of the traditional database systems. Analyzing Big Data is a very challenging task since it involves the processing of huge amount of data. As the industry or its business grows, the data related to the industries also tend to grow on a larger scale. Prominent data analysis tools are required to analyze the data in order to gain value out of it. Hadoop is a sought-after open source framework that uses MapReduce techniques to store and process huge datasets. However, the programs written using MapReduce techniques are not flexible and also require maintenance. This problem is overcome by making use of HiveQL. In order to execute queries in HiveQL, the platform required is Hive. It is an open-source data warehousing set-up built on Hadoop. HiveQL queries are compiled into MapReduce jobs that are executed utilizing Hadoop. In this paper we have analyzed the Indian Premier League dataset using HiveQL and compared its execution time with that of traditional SQL queries. It was found that the HiveQL provided better performance with larger dataset while SQL performed better with smaller datasets

Rethinking Data Collection on Logistics Facilities

2017 ◽  
Vol 2609 (1) ◽  
pp. 67-76 ◽  
Author(s):  
Adeline Heitz ◽  
Pierre Launay ◽  
Adrien Beziat
Keyword(s):  
Spatial Distribution ◽  
Data Collection ◽  
Open Source ◽  
Urban Regions ◽  
Open Source Data ◽  
Source Data ◽  
Paris Region

The aim of this paper is to propose a new methodology for collecting data on logistics facilities in urban regions. This methodology was used to build a database of warehouses and transport terminals in the Paris region for 2015, which added to the various existing databases by using open source data. The new and existing databases were compared so that the limitations of the latter could be considered, and preliminary conclusions were made about the distinctive characteristics of the new methodology. Then, the new database was used to study the spatial distribution of logistics facilities in the Paris region. Finally, a new typology of logistics facilities was proposed to characterize the different logistics sectors without relying on activity codes.

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.

scholarly journals Automated open-source data collection and processing: an example of OpenStreetMap and bike-sharing

2019 ◽  
Vol 41 ◽  
pp. 688-693
Author(s):  
David Duran-Rodas ◽  
Emmanouil Chaniotakis ◽  
Constantinos Antoniou
Keyword(s):  
Data Collection ◽  
Open Source ◽  
Open Source Data ◽  
Source Data ◽  
Bike Sharing
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