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Twitter data emotion analysis using Hadoop and metaheuristic optimized Graphical Neural Network. [PDF]
Front Artif IntellWang X, Li Y, Chen F.
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Communications of the ACM, 2016
This open source computing framework unifies streaming, batch, and interactive big data workloads to unlock new applications.
Matei Zaharia +13 more
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This open source computing framework unifies streaming, batch, and interactive big data workloads to unlock new applications.
Matei Zaharia +13 more
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Apache Spark and Apache Ignite Performance Analysis
2019 22nd International Conference on Control Systems and Computer Science (CSCS), 2019Big Data represents an actual research topic. More and more it becomes part of people life's through different applications that are used daily, such as stock exchange, news, social media, health-care. All these applications make use of Big Data technologies for storing and processing information.
Cristiana-Stefania Stan +4 more
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2021
Apache Spark is a data analytics platform that has made big data accessible and brings large-scale data processing into the reach of every developer. With Apache Spark, it is as easy to read from a single CSV file on your local machine as it is to read from a million CSV files in a data lake.
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Apache Spark is a data analytics platform that has made big data accessible and brings large-scale data processing into the reach of every developer. With Apache Spark, it is as easy to read from a single CSV file on your local machine as it is to read from a million CSV files in a data lake.
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Performance comparison of Apache Hadoop and Apache Spark
Proceedings of the Third International Conference on Advanced Informatics for Computing Research, 2019The term 'Big Data' is a broad term used for the data sets, which is enormous and traditional data processing applications find it hard to process. Both Apache Spark and Apache Hadoop are one of the significant parts of the big data family. Some of the researchers view both frameworks as the rivals but it is not that easy to compare these two as they ...
Amritpal Singh +2 more
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2019
Apache Spark performs in-memory computation. The data structure used is Resilient Distributed Datasets (RDDs). These RDDs are partitioned using inbuilt Hash and Range Partitioning. We propose a partition scheme which uses modular division on keys of elements with numbers from 2 to 10.
H. S. Sreeyuktha, J. Geetha Reddy
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Apache Spark performs in-memory computation. The data structure used is Resilient Distributed Datasets (RDDs). These RDDs are partitioned using inbuilt Hash and Range Partitioning. We propose a partition scheme which uses modular division on keys of elements with numbers from 2 to 10.
H. S. Sreeyuktha, J. Geetha Reddy
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2016
If our stack were a vehicle, now we have reached the engine. As an engine, we will disarm it, analyze it, master it, improve it, and run it to the limit.
Raul Estrada, Isaac Ruiz
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If our stack were a vehicle, now we have reached the engine. As an engine, we will disarm it, analyze it, master it, improve it, and run it to the limit.
Raul Estrada, Isaac Ruiz
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Balanced Graph Partitioning with Apache Spark
2014A significant part of the data produced every day by online services is structured as a graph. Therefore, there is the need for efficient processing and analysis solutions for large scale graphs. Among the others, the balanced graph partitioning is a well known NP-complete problem with a wide range of applications.
Carlini, Emanuele +4 more
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