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
Computer ◽  
2009 ◽  
Vol 42 (10) ◽  
pp. 97-99 ◽  
Author(s):  
Yaw Anokwa ◽  
Carl Hartung ◽  
Waylon Brunette ◽  
Gaetano Borriello ◽  
Adam Lerer

2016 ◽  
Vol 07 (03) ◽  
pp. 31-33
Author(s):  
ATIF AZIZ ◽  
◽  
RAJEEV ARYA ◽  
SANA SHAFIQUE ◽  
◽  
...  

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


Author(s):  
Adeline Heitz ◽  
Pierre Launay ◽  
Adrien Beziat

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.


Author(s):  
Richard S. Segall

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.


Author(s):  
Richard S. Segall

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.


2019 ◽  
Vol 41 ◽  
pp. 688-693
Author(s):  
David Duran-Rodas ◽  
Emmanouil Chaniotakis ◽  
Constantinos Antoniou

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