Kin b abst
•
0 likes•145 views
Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. This document analyzes using Hadoop to process a large newspaper database, taking advantage of Hadoop's ability to handle large amounts of unstructured and semi-structured data through distributed computing. The analysis explores how Hadoop can scale to process the entire newspaper database and extract useful information and insights.
Report
Share
Report
Share
Download to read offline
Recommended
Facebook Hadoop Data & Applications
This document discusses Facebook's use of Hadoop and Hive for storing and analyzing large amounts of user-generated data. Key points include:
- Facebook stores petabytes of user data including statuses, photos, videos in its Hadoop/Hive warehouse and other Hadoop clusters.
- The data is used for business intelligence to inform strategies and decisions, and power artificial intelligence like recommendations and ads optimization.
- Hive is used for ad hoc querying, building machine learning models at scale, and performing text analytics on large corpora.
- Examples demonstrate how metrics dashboards and recommendation systems were built on Hadoop/Hive.
Hadoop info
hadoop architecture,hadoop,advantages of hadoop,disadvantages of hadoop,uses of hadoop,why hadoop,doug cutting,big data,hdfs,
Which NoSQL Database to Combine with Spark for Real Time Big Data Analytics?
Which NoSQL Database to Combine with Spark for Real Time Big Data Analytics?IJCSIS Research Publications
Big Data is an evolution of Business Intelligence (BI).
Whereas traditional BI relies on data warehouses limited in size
(some terabytes) and it hardly manages unstructured data and
real-time analysis, the era of Big Data opens up a new technological
period offering advanced architectures and infrastructures
allowing sophisticated analyzes taking into account these new
data integrated into the ecosystem of the business . In this article,
we will present the results of an experimental study on the performance
of the best framework of Big Analytics (Spark) with the
most popular databases of NoSQL MongoDB and Hadoop. The
objective of this study is to determine the software combination
that allows sophisticated analysis in real time.Apache hadoop introduction and architecture
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable and distributed processing of large data sets across clusters of commodity hardware. The core of Hadoop is a storage part known as Hadoop Distributed File System (HDFS) and a processing part known as MapReduce. HDFS provides distributed storage and MapReduce enables distributed processing of large datasets in a reliable, fault-tolerant and scalable manner. Hadoop has become popular for distributed computing as it is reliable, economical and scalable to handle large and varying amounts of data.
Report Hadoop Map Reduce
This document provides an overview of MapReduce and Apache Hadoop. It discusses the history and components of Hadoop, including HDFS and MapReduce. It then walks through an example MapReduce job, the WordCount algorithm, to illustrate how MapReduce works. The WordCount example counts the frequency of words in documents by having mappers emit <word, 1> pairs and reducers sum the counts for each word.
IJET-V3I2P14
Big data is a popular term used to describe the large volume of data which includes structured, semi-structured and unstructured
data. Now-a-days, unstructured data is growing in an explosive speed with the development of Internet and social networks like Twitter,Facebook
& Yahoo etc., In order to process such colossal of data a software is required that does this efficiently and this is where Hadoop steps in. Hadoop
has become one of the most used frameworks when dealing with big data. It is used to analyze and process big data. In this paper, Apache Flume
is configured and integrated with spark streaming for streaming the data from twitter application. The streamed data is stored into Apache
Cassandra. After retrieving the data, the data is going to be analyzed by using the concept of Apache Zeppelin. The result will be displayed on
Dashboard and the dashboard result is also going to be analyzed and validating using JSON
Hadoop 2.0 and yarn
Prime Dimensions offers Hadoop 2.0 infrastructure including YARN and Tez, which allow multiple applications beyond MapReduce to run on Hadoop clusters. YARN splits the responsibilities of the JobTracker into separate resource management and application management processes for improved scalability. Tez and Spark can also integrate with YARN to enable in-memory analytics and establish virtualized data environments. YARN, Tez, and Spark represent an evolution of Hadoop's functionality to meet increasing demands for performance and scalability in big data analytics across industries.
Hive
Hive allows users to access and analyze large datasets stored in Hadoop clusters. Rakesh Kumar discusses using Hive to query and summarize data stored in Hadoop. Hive provides a SQL-like interface to query large datasets in a distributed environment and simplify big data analysis for data analysts and engineers.
Recommended
Facebook Hadoop Data & Applications
This document discusses Facebook's use of Hadoop and Hive for storing and analyzing large amounts of user-generated data. Key points include:
- Facebook stores petabytes of user data including statuses, photos, videos in its Hadoop/Hive warehouse and other Hadoop clusters.
- The data is used for business intelligence to inform strategies and decisions, and power artificial intelligence like recommendations and ads optimization.
- Hive is used for ad hoc querying, building machine learning models at scale, and performing text analytics on large corpora.
- Examples demonstrate how metrics dashboards and recommendation systems were built on Hadoop/Hive.
Hadoop info
hadoop architecture,hadoop,advantages of hadoop,disadvantages of hadoop,uses of hadoop,why hadoop,doug cutting,big data,hdfs,
Which NoSQL Database to Combine with Spark for Real Time Big Data Analytics?
Which NoSQL Database to Combine with Spark for Real Time Big Data Analytics?IJCSIS Research Publications
Big Data is an evolution of Business Intelligence (BI).
Whereas traditional BI relies on data warehouses limited in size
(some terabytes) and it hardly manages unstructured data and
real-time analysis, the era of Big Data opens up a new technological
period offering advanced architectures and infrastructures
allowing sophisticated analyzes taking into account these new
data integrated into the ecosystem of the business . In this article,
we will present the results of an experimental study on the performance
of the best framework of Big Analytics (Spark) with the
most popular databases of NoSQL MongoDB and Hadoop. The
objective of this study is to determine the software combination
that allows sophisticated analysis in real time.Apache hadoop introduction and architecture
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable and distributed processing of large data sets across clusters of commodity hardware. The core of Hadoop is a storage part known as Hadoop Distributed File System (HDFS) and a processing part known as MapReduce. HDFS provides distributed storage and MapReduce enables distributed processing of large datasets in a reliable, fault-tolerant and scalable manner. Hadoop has become popular for distributed computing as it is reliable, economical and scalable to handle large and varying amounts of data.
Report Hadoop Map Reduce
This document provides an overview of MapReduce and Apache Hadoop. It discusses the history and components of Hadoop, including HDFS and MapReduce. It then walks through an example MapReduce job, the WordCount algorithm, to illustrate how MapReduce works. The WordCount example counts the frequency of words in documents by having mappers emit <word, 1> pairs and reducers sum the counts for each word.
IJET-V3I2P14
Big data is a popular term used to describe the large volume of data which includes structured, semi-structured and unstructured
data. Now-a-days, unstructured data is growing in an explosive speed with the development of Internet and social networks like Twitter,Facebook
& Yahoo etc., In order to process such colossal of data a software is required that does this efficiently and this is where Hadoop steps in. Hadoop
has become one of the most used frameworks when dealing with big data. It is used to analyze and process big data. In this paper, Apache Flume
is configured and integrated with spark streaming for streaming the data from twitter application. The streamed data is stored into Apache
Cassandra. After retrieving the data, the data is going to be analyzed by using the concept of Apache Zeppelin. The result will be displayed on
Dashboard and the dashboard result is also going to be analyzed and validating using JSON
Hadoop 2.0 and yarn
Prime Dimensions offers Hadoop 2.0 infrastructure including YARN and Tez, which allow multiple applications beyond MapReduce to run on Hadoop clusters. YARN splits the responsibilities of the JobTracker into separate resource management and application management processes for improved scalability. Tez and Spark can also integrate with YARN to enable in-memory analytics and establish virtualized data environments. YARN, Tez, and Spark represent an evolution of Hadoop's functionality to meet increasing demands for performance and scalability in big data analytics across industries.
Hive
Hive allows users to access and analyze large datasets stored in Hadoop clusters. Rakesh Kumar discusses using Hive to query and summarize data stored in Hadoop. Hive provides a SQL-like interface to query large datasets in a distributed environment and simplify big data analysis for data analysts and engineers.
Hadoop
This document provides an overview of big data, Hadoop, and related concepts:
- Big data refers to large datasets that cannot be processed efficiently by traditional systems due to their size. Sources include social media, smartphones, machines, and log files.
- Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It implements the MapReduce programming model.
- Key Hadoop components include HDFS for storage, MapReduce for distributed processing, and related projects like Pig, Hive, HBase, Flume, Oozie, and Sqoop. Companies use Hadoop for applications involving large datasets, such as log analysis, recommendations, and business intelligence
Hadoop Case Studies in the Real World
Apache Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It allows for processing of structured, semi-structured, and unstructured data using simple programming models. Hadoop can handle failures at the application layer and provides highly available services across large clusters of computers that may be prone to failures. Case studies show Hadoop has been used for fraud detection in banking, analyzing customer spending, predicting weather patterns and climate changes, processing astronomical images, and more wherever high volumes of unstructured data are growing rapidly.
Big data and Hadoop
This document provides an overview of big data and Hadoop. It discusses why Hadoop is useful for extremely large datasets that are difficult to manage in relational databases. It then summarizes what Hadoop is, including its core components like HDFS, MapReduce, HBase, Pig, Hive, Chukwa, and ZooKeeper. The document also outlines Hadoop's design principles and provides examples of how some of its components like MapReduce and Hive work.
Harnessing Hadoop and Big Data to Reduce Execution Times
Asserting that Big Data is vital to business is an understatement. Organizations have generated more and more data for years, but struggle to use it effectively. Clearly Big Data has more important uses than ensuring compliance with regulatory requirements. In addition, data is being generated with greater velocity, due to the advent of new pervasive devices (e.g., smartphones, tablets, etc.), social Web sites (e.g., Facebook, Twitter, LinkedIn, etc.) and other sources like GPS, Google Maps, heat/pressure sensors, etc.
THE SOLUTION FOR BIG DATA
Discussed the huge problems facing on Data storages and enclosed the Technology for it.Discussed drawbacks and future goals of it.
Hadoop: Distributed data processing
The document summarizes a presentation given by Amr Awadallah of Cloudera on Hadoop. It discusses how current storage systems are unable to perform computation, and how Hadoop addresses this through its marriage of HDFS for scalable storage and MapReduce for distributed processing. It provides an overview of Hadoop's history and design principles such as managing itself, scaling performance linearly, and moving computation to data.
Facebooks Petabyte Scale Data Warehouse using Hive and Hadoop
Facebooks Petabyte Scale Data Warehouse using Hive and Hadoop.
More info here
http://www.royans.net/arch/hive-facebook/
How to deal with nested lists in R?
This document discusses how to deal with nested lists in R using the purrr, furrr, and future packages. It summarizes working with nested list data from a JSON response, including adding custom IDs to nested data frames and parallelizing the process using future_map to speed it up. Anonymous functions are also discussed as they are used with the apply functions in purrr, and examples are provided of their syntax.
Hadoop: Distributed Data Processing
Hadoop is a scalable distributed system for storing and processing large datasets across commodity hardware. It consists of HDFS for storage and MapReduce for distributed processing. A large ecosystem of additional tools like Hive, Pig, and HBase has also developed. Hadoop provides significantly lower costs for data storage and analysis compared to traditional systems and is well-suited to unstructured or structured big data. It has seen wide adoption at companies like Yahoo, Facebook, and eBay for applications like log analysis, personalization, and fraud detection.
Hadoop Technologies
The document discusses various Hadoop technologies including HDFS, MapReduce, Pig/Hive, HBase, Flume, Oozie, Zookeeper, and HBase. HDFS provides reliable storage across multiple machines by replicating data on different nodes. MapReduce is a framework for processing large datasets in parallel. Pig and Hive provide high-level languages for analyzing data stored in Hadoop. Flume collects log data as it is generated. Oozie manages Hadoop jobs. Zookeeper allows distributed coordination. HBase provides a fault-tolerant way to store large amounts of sparse data.
Building a geospatial processing pipeline using Hadoop and HBase and how Mons...
Monsanto uses geospatial data and analytics to improve sustainable agriculture. They process vast amounts of spatial data on Hadoop to generate prescription maps that optimize seeding rates. Their previous SQL-based system could only handle a small fraction of the data and took over 30 days to process. Monsanto's new Hadoop/HBase architecture loads the entire US dataset in 18 hours, representing significant cost savings over the SQL approach. This foundational system provides agronomic insights to farmers and supports Monsanto's vision of doubling yields by 2030 through information-driven farming.
Hadoop for beginners free course ppt
This is a power point presentation on Hadoop and Big Data. This covers the essential knowledge one should have when stepping into the world of Big Data.
This course is available on hadoop-skills.com for free!
This course builds a basic fundamental understanding of Big Data problems and Hadoop as a solution. This course takes you through:
• This course builds Understanding of Big Data problems with easy to understand examples and illustrations.
• History and advent of Hadoop right from when Hadoop wasn’t even named Hadoop and was called Nutch
• What is Hadoop Magic which makes it so unique and powerful.
• Understanding the difference between Data science and data engineering, which is one of the big confusions in selecting a carrier or understanding a job role.
• And most importantly, demystifying Hadoop vendors like Cloudera, MapR and Hortonworks by understanding about them.
This course is available for free on hadoop-skills.com
Hadoop Ecosystem Architecture Overview
Hadoop Ecosystems overview and diagrams - helps to understand list of subprojects in hadoop in diagramatic way.
Introduction to Hadoop Technology
This document discusses Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes how Hadoop uses HDFS for distributed storage and fault tolerance, YARN for resource management, and MapReduce for parallel processing of large datasets. It provides details on the architecture of HDFS including the name node, data nodes, and clients. It also explains the MapReduce programming model and job execution involving map and reduce tasks. Finally, it states that as data volumes continue rising, Hadoop provides an affordable solution for large-scale data handling and analysis through its distributed and scalable architecture.
Hadoop Report
The document provides an abstract for a paper on the Hadoop framework. It discusses how Hadoop is a software framework that supports data-intensive distributed applications under an open source license. It was inspired by Google's MapReduce and Google File System papers. The paper will represent the history, development, and current situation of Hadoop technology. It is now maintained by the Apache Software Foundation via Cloudera. The paper will include chapters on an introduction to Hadoop, its history, key technologies like MapReduce and HDFS, other related Apache projects, and instructions for setting up a single node Hadoop cluster.
Hadoop Seminar Report
1. The document discusses the evolution of computing from mainframes to smaller commodity servers and PCs. It then introduces cloud computing as an emerging technology that is changing the technology landscape, with examples like Google File System and Amazon S3.
2. It discusses the need for large data processing due to increasing amounts of data from sources like the stock exchange, Facebook, genealogy sites, and scientific experiments.
3. Hadoop is introduced as a framework for distributed computing and reliable shared storage and analysis of large datasets using its Hadoop Distributed File System (HDFS) for storage and MapReduce for analysis.
Hadoop for High-Performance Climate Analytics - Use Cases and Lessons Learned
Scientific data services are a critical aspect of the NASA Center for Climate Simulation’s mission (NCCS). Hadoop, via MapReduce, provides an approach to high-performance analytics that is proving to be useful to data intensive problems in climate research. It offers an analysis paradigm that uses clusters of computers and combines distributed storage of large data sets with parallel computation. The NCCS is particularly interested in the potential of Hadoop to speed up basic operations common to a wide range of analyses. In order to evaluate this potential, we prototyped a series of canonical MapReduce operations over a test suite of observational and climate simulation datasets. The initial focus was on averaging operations over arbitrary spatial and temporal extents within Modern Era Retrospective- Analysis for Research and Applications (MERRA) data. After preliminary results suggested that this approach improves efficiencies within data intensive analytic workflows, we invested in building a cyberinfrastructure resource for developing a new generation of climate data analysis capabilities using Hadoop. This resource is focused on reducing the time spent in the preparation of reanalysis data used in data-model intercomparison, a long sought goal of the climate community. This paper summarizes the related use cases and lessons learned.
Harnessing Hadoop: Understanding the Big Data Processing Options for Optimizi...
This document discusses big data processing options for optimizing analytical workloads using Hadoop. It provides an overview of Hadoop and its core components HDFS and MapReduce. It also discusses the Hadoop ecosystem including tools like Pig, Hive, HBase, and ecosystem projects. The document compares building Hadoop clusters to using appliances or Hadoop-as-a-Service offerings. It also briefly mentions some Hadoop competitors for real-time processing use cases.
Hadoop Presentation - PPT
This document outlines the objectives, key concepts, and curriculum for a Big Data and Hadoop training module. The objectives are to understand what Big Data is, the Hadoop ecosystem and its features, career opportunities, and the training curriculum. It defines Big Data, Hadoop, and the Hadoop ecosystem. It discusses the V's of Big Data and domains where Big Data is applicable. It also outlines job roles in the Big Data industry, potential employers, career paths, and the 10-module training curriculum covering topics like Hadoop, MapReduce, Pig, Hive, HBase, Zookeeper and Oozie.
Sermon slides lethal faith (ps 119.9-11) copy
To have a lethal faith in your battle against sin, you must store up Scripture deep within.
This message was given on March 20, 2016 at New City Church in Calgary by Pastor John Ferguson. For more info, please visit: www.newcitychurch.ca
February 2016 - Market Snapshot - Santa Clara County
The document reports on real estate trends in Santa Clara County, California from January 1998 to February 2016, including statistics on housing inventory levels, closed housing sales, median and average home prices, percentage of sale price to listing price, months of inventory, and average days on market for single family homes and condo/townhouses. The data is provided by MLSListings Inc and covers a period of 18 years.
More Related Content
What's hot
Hadoop
This document provides an overview of big data, Hadoop, and related concepts:
- Big data refers to large datasets that cannot be processed efficiently by traditional systems due to their size. Sources include social media, smartphones, machines, and log files.
- Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It implements the MapReduce programming model.
- Key Hadoop components include HDFS for storage, MapReduce for distributed processing, and related projects like Pig, Hive, HBase, Flume, Oozie, and Sqoop. Companies use Hadoop for applications involving large datasets, such as log analysis, recommendations, and business intelligence
Hadoop Case Studies in the Real World
Apache Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It allows for processing of structured, semi-structured, and unstructured data using simple programming models. Hadoop can handle failures at the application layer and provides highly available services across large clusters of computers that may be prone to failures. Case studies show Hadoop has been used for fraud detection in banking, analyzing customer spending, predicting weather patterns and climate changes, processing astronomical images, and more wherever high volumes of unstructured data are growing rapidly.
Big data and Hadoop
This document provides an overview of big data and Hadoop. It discusses why Hadoop is useful for extremely large datasets that are difficult to manage in relational databases. It then summarizes what Hadoop is, including its core components like HDFS, MapReduce, HBase, Pig, Hive, Chukwa, and ZooKeeper. The document also outlines Hadoop's design principles and provides examples of how some of its components like MapReduce and Hive work.
Harnessing Hadoop and Big Data to Reduce Execution Times
Asserting that Big Data is vital to business is an understatement. Organizations have generated more and more data for years, but struggle to use it effectively. Clearly Big Data has more important uses than ensuring compliance with regulatory requirements. In addition, data is being generated with greater velocity, due to the advent of new pervasive devices (e.g., smartphones, tablets, etc.), social Web sites (e.g., Facebook, Twitter, LinkedIn, etc.) and other sources like GPS, Google Maps, heat/pressure sensors, etc.
THE SOLUTION FOR BIG DATA
Discussed the huge problems facing on Data storages and enclosed the Technology for it.Discussed drawbacks and future goals of it.
Hadoop: Distributed data processing
The document summarizes a presentation given by Amr Awadallah of Cloudera on Hadoop. It discusses how current storage systems are unable to perform computation, and how Hadoop addresses this through its marriage of HDFS for scalable storage and MapReduce for distributed processing. It provides an overview of Hadoop's history and design principles such as managing itself, scaling performance linearly, and moving computation to data.
Facebooks Petabyte Scale Data Warehouse using Hive and Hadoop
Facebooks Petabyte Scale Data Warehouse using Hive and Hadoop.
More info here
http://www.royans.net/arch/hive-facebook/
How to deal with nested lists in R?
This document discusses how to deal with nested lists in R using the purrr, furrr, and future packages. It summarizes working with nested list data from a JSON response, including adding custom IDs to nested data frames and parallelizing the process using future_map to speed it up. Anonymous functions are also discussed as they are used with the apply functions in purrr, and examples are provided of their syntax.
Hadoop: Distributed Data Processing
Hadoop is a scalable distributed system for storing and processing large datasets across commodity hardware. It consists of HDFS for storage and MapReduce for distributed processing. A large ecosystem of additional tools like Hive, Pig, and HBase has also developed. Hadoop provides significantly lower costs for data storage and analysis compared to traditional systems and is well-suited to unstructured or structured big data. It has seen wide adoption at companies like Yahoo, Facebook, and eBay for applications like log analysis, personalization, and fraud detection.
Hadoop Technologies
The document discusses various Hadoop technologies including HDFS, MapReduce, Pig/Hive, HBase, Flume, Oozie, Zookeeper, and HBase. HDFS provides reliable storage across multiple machines by replicating data on different nodes. MapReduce is a framework for processing large datasets in parallel. Pig and Hive provide high-level languages for analyzing data stored in Hadoop. Flume collects log data as it is generated. Oozie manages Hadoop jobs. Zookeeper allows distributed coordination. HBase provides a fault-tolerant way to store large amounts of sparse data.
Building a geospatial processing pipeline using Hadoop and HBase and how Mons...
Monsanto uses geospatial data and analytics to improve sustainable agriculture. They process vast amounts of spatial data on Hadoop to generate prescription maps that optimize seeding rates. Their previous SQL-based system could only handle a small fraction of the data and took over 30 days to process. Monsanto's new Hadoop/HBase architecture loads the entire US dataset in 18 hours, representing significant cost savings over the SQL approach. This foundational system provides agronomic insights to farmers and supports Monsanto's vision of doubling yields by 2030 through information-driven farming.
Hadoop for beginners free course ppt
This is a power point presentation on Hadoop and Big Data. This covers the essential knowledge one should have when stepping into the world of Big Data.
This course is available on hadoop-skills.com for free!
This course builds a basic fundamental understanding of Big Data problems and Hadoop as a solution. This course takes you through:
• This course builds Understanding of Big Data problems with easy to understand examples and illustrations.
• History and advent of Hadoop right from when Hadoop wasn’t even named Hadoop and was called Nutch
• What is Hadoop Magic which makes it so unique and powerful.
• Understanding the difference between Data science and data engineering, which is one of the big confusions in selecting a carrier or understanding a job role.
• And most importantly, demystifying Hadoop vendors like Cloudera, MapR and Hortonworks by understanding about them.
This course is available for free on hadoop-skills.com
Hadoop Ecosystem Architecture Overview
Hadoop Ecosystems overview and diagrams - helps to understand list of subprojects in hadoop in diagramatic way.
Introduction to Hadoop Technology
This document discusses Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes how Hadoop uses HDFS for distributed storage and fault tolerance, YARN for resource management, and MapReduce for parallel processing of large datasets. It provides details on the architecture of HDFS including the name node, data nodes, and clients. It also explains the MapReduce programming model and job execution involving map and reduce tasks. Finally, it states that as data volumes continue rising, Hadoop provides an affordable solution for large-scale data handling and analysis through its distributed and scalable architecture.
Hadoop Report
The document provides an abstract for a paper on the Hadoop framework. It discusses how Hadoop is a software framework that supports data-intensive distributed applications under an open source license. It was inspired by Google's MapReduce and Google File System papers. The paper will represent the history, development, and current situation of Hadoop technology. It is now maintained by the Apache Software Foundation via Cloudera. The paper will include chapters on an introduction to Hadoop, its history, key technologies like MapReduce and HDFS, other related Apache projects, and instructions for setting up a single node Hadoop cluster.
Hadoop Seminar Report
1. The document discusses the evolution of computing from mainframes to smaller commodity servers and PCs. It then introduces cloud computing as an emerging technology that is changing the technology landscape, with examples like Google File System and Amazon S3.
2. It discusses the need for large data processing due to increasing amounts of data from sources like the stock exchange, Facebook, genealogy sites, and scientific experiments.
3. Hadoop is introduced as a framework for distributed computing and reliable shared storage and analysis of large datasets using its Hadoop Distributed File System (HDFS) for storage and MapReduce for analysis.
Hadoop for High-Performance Climate Analytics - Use Cases and Lessons Learned
Scientific data services are a critical aspect of the NASA Center for Climate Simulation’s mission (NCCS). Hadoop, via MapReduce, provides an approach to high-performance analytics that is proving to be useful to data intensive problems in climate research. It offers an analysis paradigm that uses clusters of computers and combines distributed storage of large data sets with parallel computation. The NCCS is particularly interested in the potential of Hadoop to speed up basic operations common to a wide range of analyses. In order to evaluate this potential, we prototyped a series of canonical MapReduce operations over a test suite of observational and climate simulation datasets. The initial focus was on averaging operations over arbitrary spatial and temporal extents within Modern Era Retrospective- Analysis for Research and Applications (MERRA) data. After preliminary results suggested that this approach improves efficiencies within data intensive analytic workflows, we invested in building a cyberinfrastructure resource for developing a new generation of climate data analysis capabilities using Hadoop. This resource is focused on reducing the time spent in the preparation of reanalysis data used in data-model intercomparison, a long sought goal of the climate community. This paper summarizes the related use cases and lessons learned.
Harnessing Hadoop: Understanding the Big Data Processing Options for Optimizi...
This document discusses big data processing options for optimizing analytical workloads using Hadoop. It provides an overview of Hadoop and its core components HDFS and MapReduce. It also discusses the Hadoop ecosystem including tools like Pig, Hive, HBase, and ecosystem projects. The document compares building Hadoop clusters to using appliances or Hadoop-as-a-Service offerings. It also briefly mentions some Hadoop competitors for real-time processing use cases.
Hadoop Presentation - PPT
This document outlines the objectives, key concepts, and curriculum for a Big Data and Hadoop training module. The objectives are to understand what Big Data is, the Hadoop ecosystem and its features, career opportunities, and the training curriculum. It defines Big Data, Hadoop, and the Hadoop ecosystem. It discusses the V's of Big Data and domains where Big Data is applicable. It also outlines job roles in the Big Data industry, potential employers, career paths, and the 10-module training curriculum covering topics like Hadoop, MapReduce, Pig, Hive, HBase, Zookeeper and Oozie.
What's hot (19)
Harnessing Hadoop and Big Data to Reduce Execution Times
Harnessing Hadoop and Big Data to Reduce Execution Times
Facebooks Petabyte Scale Data Warehouse using Hive and Hadoop
Facebooks Petabyte Scale Data Warehouse using Hive and Hadoop
Building a geospatial processing pipeline using Hadoop and HBase and how Mons...
Building a geospatial processing pipeline using Hadoop and HBase and how Mons...
Hadoop for High-Performance Climate Analytics - Use Cases and Lessons Learned
Hadoop for High-Performance Climate Analytics - Use Cases and Lessons Learned
Harnessing Hadoop: Understanding the Big Data Processing Options for Optimizi...
Harnessing Hadoop: Understanding the Big Data Processing Options for Optimizi...
Viewers also liked
Sermon slides lethal faith (ps 119.9-11) copy
To have a lethal faith in your battle against sin, you must store up Scripture deep within.
This message was given on March 20, 2016 at New City Church in Calgary by Pastor John Ferguson. For more info, please visit: www.newcitychurch.ca
February 2016 - Market Snapshot - Santa Clara County
The document reports on real estate trends in Santa Clara County, California from January 1998 to February 2016, including statistics on housing inventory levels, closed housing sales, median and average home prices, percentage of sale price to listing price, months of inventory, and average days on market for single family homes and condo/townhouses. The data is provided by MLSListings Inc and covers a period of 18 years.
February 2016 - Market Snapshot - San Benito County
The document contains 12 sections analyzing real estate trends in San Benito County, California from January 1998 to February 2016, including housing inventory levels, home sales, median and average home prices, percentage of homes selling at or below asking price, months of inventory, and average days on market for single family homes and condos. The data was compiled by MLSListings Inc and examines key real estate market indicators over the 18-year period.
Fotos GIMP
Este documento presenta tres ejemplos de fotomontajes originales y modificados. Las imágenes originales incluyen una mujer con un perro y un gato, una felicitación de niños y la Gioconda. Las versiones modificadas alteran sutilmente los elementos o expresiones de las imágenes originales.
Gtaby
El documento habla sobre las etiquetas más importantes del lenguaje HTML. Menciona etiquetas como <html>, <head>, <body>, <title>, <b>, <table> e <img> y explica brevemente para qué sirve cada una. También define qué son las etiquetas en HTML y explica que HTML es el lenguaje utilizado para crear páginas web, compuesto por etiquetas que el navegador interpreta para mostrar contenido formato como texto, imágenes e hipervínculos.
Encuesta Plaza Pública Cadem N° 113
Plaza Pública Cadem: Estudio N° 113, con la evaluacion de la Presidenta Michelle Bachelet, las reformas y la renuncia de Pablo Longueira a la UDI.
PERIODEllipses
The document promotes a website and social media accounts for Nikki Nigl and her company NikkiNigl.com. It emphasizes using periods at the end of statements rather than ellipses by stating "We will end our statements with a PERIOD." It also includes the hashtag "WomenUNTIE" which seems to reference empowering women.
COSMOPOLITAN PROJECT PLAN and PRO FORMA
Groom Energy Solutions will replace the lighting at the Cosmopolitan Hotel and Casino in Las Vegas to reduce energy consumption by 1.45 million kWh per year, replacing over 34,000 lighting elements and installing motion sensors over a projected 60 day period. The project will involve replacing lighting across various areas of the property in a seamless manner while minimizing disruption to hotel operations and guests. A detailed project plan and pro forma valuation are provided outlining the work scope, means and methods, safety protocols, crew organization, schedule, costs, and projected cash flow.
Economic sectors 4th grade
The document defines and describes the primary, secondary, tertiary, and quaternary economic sectors. The primary sector involves direct exploitation of natural resources like agriculture, livestock, fishing, and mining. The secondary sector transforms primary products through industries and energy production. The tertiary sector provides services like education, medicine, and transport. The quaternary sector consists of virtual media like the internet, social media, cell phones, and mail.
Factors affecting the growth of microbes
This document discusses the physical factors that influence the growth of microorganisms, including temperature, pH, osmotic pressure, hydrostatic pressure, and radiation. It describes how each factor affects microbial growth and membranes. Temperature is the most important physical factor, as it can damage enzymes and membranes at extremes. Microbes are classified based on their optimal temperature ranges, such as psychrophiles, mesophiles, thermophiles, and hyperthermophiles. Optimal pH and osmotic pressure ranges also determine bacterial classifications like acidophiles, alkalophiles, halophiles, and osmotolerant microbes. Higher hydrostatic pressures and radiation can also impact microbial growth.
Viewers also liked (16)
February 2016 - Market Snapshot - Santa Clara County
February 2016 - Market Snapshot - Santa Clara County
February 2016 - Market Snapshot - San Benito County
February 2016 - Market Snapshot - San Benito County
More from harmonylab
Generating Automatic Feedback on UI Mockups with Large Language Models
Generating Automatic Feedback on UI Mockups with Large Language Modelsを和訳紹介したものです
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matching
公開URL：https://arxiv.org/pdf/2404.19174
出典：Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. ascimento: XFeat: Accelerated Features for Lightweight Image Matching, Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
概要：リソース効率に優れた特徴点マッチングのための軽量なアーキテクチャ「XFeat（Accelerated Features）」を提案します。手法は、局所的な特徴点の検出、抽出、マッチングのための畳み込みニューラルネットワークの基本的な設計を再検討します。特に、リソースが限られたデバイス向けに迅速かつ堅牢なアルゴリズムが必要とされるため、解像度を可能な限り高く保ちながら、ネットワークのチャネル数を制限します。さらに、スパース下でのマッチングを選択できる設計となっており、ナビゲーションやARなどのアプリケーションに適しています。XFeatは、高速かつ同等以上の精度を実現し、一般的なラップトップのCPU上でリアルタイムで動作します。
【修士論文】代替出勤者の選定業務における依頼順決定方法に関する研究 千坂知也
シフト勤務制の職場における従業員の急な欠勤は業務に支障をきたし，顧客などに対する信頼の失墜などにより，事業そのものに深刻な支障をきたす恐れがある．
このような急な欠勤に対し，管理者は代わりの出勤を他の従業員に依頼し，代替出勤者の選定を行う必要がある．この代替出勤者の選定業務における従来手法では，代替出勤者の選定後に発生した勤務表制約違反を解消するために勤務表全体を再作成するアプローチがとられてきた．このアプローチは厳しい勤務表制約条件が課される病院のような職場では有効とされてきたが，管理者は再作成した勤務表を再度全従業員に周知する必要があり，従業員側からしても自分以外の従業員の欠勤により自身の勤務予定が変更される可能性があるため，欠勤者が多発する職場では扱いにくい．そのため欠勤者が多発する職場では，代替出勤を行っても勤務表制約条件に違反しない従業員を事前に割り出しておき，それらの従業員に対し代替出勤依頼を行う手法がとられている．この手法では管理者の従業員に対する依頼順が業務の安定性，管理者や従業員の負担に直結する．
以上より本研究では管理者による従業員の代替出勤依頼の依頼順決定方法を提案し，代替出勤シミュレーターを構築して評価を行う．シミュレーターでは，従業員の休み希望のもと，複数の制約条件に基づき勤務表を作成し，勤務表上で確率的に欠勤を発生させ，管理者による代替出勤依頼をシミュレーションする．各従業員は管理者からの依頼に対し，確率的に可否を応答するものとする．本研究ではこのシミュレーター上で複数のパラメータセットに対し，従業員の受諾確率に基づいた方法，各従業員の代替出勤した回数に基づいた方法，代替出勤可能な日数に基づいた方法の提案、評価を行う．
【修士論文】経路探索のための媒介中心性に基づく道路ネットワーク階層化手法に関する研究
本研究では，ドライバーの細街路に対する選好を考慮した経路探索における道路ネットワークの階層化手法を提案する．提案手法では，ネットワーク分析の中心性指標の一つである媒介中心性の高いノードを特定して，各階層の非連結なサブネットワークを連結する．提案手法の有効性を検証するために，複数のドライバーの細街路への選好を用意し，札幌市の中心市街14km四方の道路ネットワークを対象とした計算機実験をおこなう．提案手法および先行手法における経路探索にかかる計算時間と得られた経路のコストを比較した結果，提案手法の優位性が確認された．
A Study on Decision Support System for Snow Removal Dispatch using Road Surfa...
This study focuses on addressing the challenges associated with decision-making in winter road snow removal operations, aiming to alleviate the burden on snow removal personnel. Specifically, we propose an approach to develop a system that collects and visualizes information on road snow conditions and weather data to support decision-making by personnel. Additionally, by sharing the collected information, we aim to facilitate the sharing of premonitions about changes in decision-making among snow removal personnel, reducing the need for physical inspections.We have validated the effectiveness of the system and confirmed its efficacy.
【卒業論文】印象タグを用いた衣服画像生成システムに関する研究
オーダーメイド服は個性を表現できる利点があるが、専門家以外は希望やこだわりが曖昧であったり、想像しているデザインの説明が難しかったりするために、デザインの注文が難しいという課題がある。
そこで、印象タグを元に衣服画像を作成する、対話型の衣服画像生成システムの開発を行い、個人の好みを反映した衣服のデザイン画を専門家以外が作成する場合の支援を試みた。
本研究ではシステムの構築を行い、性能評価として被験者に実際に操作してもらうことで、
ユーザーの発想やデザインの伝達に関する支援が可能であることを示した。
【卒業論文】大規模言語モデルを用いたマニュアル文章修正手法に関する研究
マニュアルに記載する文章では，読み手が内容を素早く明確に理解できることが重要である．そのための文章表現の技術を習得することは容易ではない．本論文では，マニュアルに適する文章を作成する支援のために，入力文章にライティングルールを適用する文章変換の手法を提案する．結果として，ライティングルールに基づく修正箇所の指摘と，修正候補の文章の生成ができた．これによって，マニュアルに適した文章を書きやすくなる．
DLゼミ: MobileOne: An Improved One millisecond Mobile Backbone
公開URL：https://openaccess.thecvf.com/content/CVPR2023/html/Vasu_MobileOne_An_Improved_One_Millisecond_Mobile_Backbone_CVPR_2023_paper.html
出典：Vasu, Pavan Kumar Anasosalu, et al.: MobileOne: An Improved One Millisecond Mobile Backbone, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023)
概要：モバイル端末向けのニューラルネットワークは多くの場合、FLOPsやパラメータ数で最適化されています。しかし、これらの最適化は実際のモバイルデバイスで実行した場合のネットワークの応答時間に相関しない場合があります。我々は昨今のニューラルネットワークの最適化のボトルネックを特定・分析し、その結果をもとにした新たな効率的なバックボーンMobileOneを設計しました。結果はMobileFormerと同等の性能を得ながら、38倍高速であり、最先端の効率性を達成しました。
DLゼミ: Llama 2: Open Foundation and Fine-Tuned Chat Models
公開URL：https://arxiv.org/pdf/2307.09288.pdf
出典：Touvron Hugo, et al.: Llama 2: Open foundation and fine-tuned chat models, arXiv preprint arXiv:2307.09288 (2023)
概要：70億から700億のパラメータを持つ大規模言語モデル（LLM）の事前学習であるLlama 2を開発し、リリースしました。Llama 2-Chatと呼ばれるファインチューニングされたLLMは、対話のユースケースに最適化されています。提案モデルは、検証したほとんどのベンチマークにおいて、オープンソースのチャットモデルを凌駕しており、有用性と安全性に関する人間による評価に基づいて、クローズドソースのモデルの適切な代替となる可能性があります。コミュニティが我々の研究を基に、LLMの責任ある開発に貢献できるようにすることも目的にあります。
DLゼミ: ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation
公開URL：https://proceedings.neurips.cc/paper_files/paper/2022/file/fbb10d319d44f8c3b4720873e4177c65-Paper-Conference.pdf
出典：Yufei Xu, Jing Zhang, Qiming Zhang, Dacheng Tao : ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation, Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pp. 38571-38584 (2022)
概要：姿勢推定はコンピュータビジョンの1タスクとして知られています。本論文ではプレーンなVision Transformerを使った姿勢推定手法「ViTPose」を提案します。ViTPoseは非階層的なVision Transformerを特徴抽出のために使用し、100Mから1Bのパラメータにスケールアップ可能です。このモデルは事前学習や、複数のポーズタスク対応などにおいて柔軟性を持ち、大規模モデルの知識を小規模モデルに転送することも可能です。実験結果は、ViTPoseがMS COCO Keypoint DetectionベンチマークにおいてSoTAを達成しました。
Voyager: An Open-Ended Embodied Agent with Large Language Models
公開URL：https://arxiv.org/abs/2305.16291
出典：Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, Anima Anandkumar : Voyager: An Open-Ended Embodied Agent with Large Language Models, arXiv: 2305.16291 (2023)
概要：本論文では、Minecraftの世界を持続的に探索し、多様なスキルを獲得し、新たな発見を人間の介入なく行う最初の大規模言語モデル（LLM）を駆使した具現化エージェントであるVoyagerを紹介する。Voyagerは、探索を最大化する自動カリキュラム、複雑な行動を格納・取得するためのスキルライブラリ、そして環境のフィードバック、実行エラー、プログラム改善のための自己確認を組み込む新たな反復的なプロンプトメカニズムの3つの主要なコンポーネントから成り立つ。
DLゼミ: Ego-Body Pose Estimation via Ego-Head Pose Estimation
公開URL：https://arxiv.org/abs/2212.04636
出典：Jiaman Li, C. Karen Liu, Jiajun Wu
: Ego-Body Pose Estimation via Ego-Head Pose Estimation, arXiv preprint arXiv:2212.04636 (2022)
概要：人間の行動理解やVR/ARへの応用において、一人称映像から人の3次元姿勢 (3D human motion)を推定することは重要である。提案手法EgoEgoは、中間表現として頭部モーションを採用した新たな手法である。問題を2分割したことにより、既存のデータセットが活用でき、他のSoTA手法より優れた性能を示した。さらにベンチマークのための一人称映像と人の3次元推定データセットARESを開発した。
ReAct: Synergizing Reasoning and Acting in Language Models
公開URL：https://arxiv.org/abs/2210.03629
出典：Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao : ReAct: Synergizing Reasoning and Acting in Language Models, arXiv: 2210.03629 (2022)
概要：本論文では大規模言語モデルにおいて推論(Reasoning)と行動(Acting)を組合わせるprompt手法であるReActを提案した。知識集約型の推論タスク（QAタスク・事実検証タスク）と意思決定タスク（テキストゲーム、ウェブナビゲーション）でReActの性能を評価した。推論タスクにおいてReActはWikipedia APIとの対話によりChain of ThoughtのHallucinationを軽減し、意思決定タスクにおいては模倣学習や強化学習による手法を上回る性能を示した。
形態素解析を用いた帝国議会議事速記録の変遷に関する研究
対話形式議事録は，口語での会話内容が記録されており，言語研究において重要な資料となる．対話形式議事録の1つである帝国議会議事速記録は，記録期間が長く分量も多いという特徴があり，近代における言語表現の変化を分析するのに適した資料であるといえる．
このようなテキストデータに対して分析の第一段階である表現抽出を自動で行う手法については知見が少なく，抽出作業のほとんどが手作業で行われているのが現状である．大規模なデータに対して手作業での該当表現の抽出を行うのには多大な労力がかかり，見逃しも発生しやすく，分析を行う上でのボトルネックとなる．
本研究では，抽出段階の自動化を目的とし，可能の意味を表す可能表現について，品詞の組み合わせマッチングによる自動抽出を試みた．さらに，可能表現の出現頻度や変遷と会議の話題の関係性を分析するための先駆けとして，重要単語抽出による話題の自動タグ付けを行った．話題タグの自動付与においては，TF-IDFを用いたシンプルな特徴単語の抽出を試みた．可能表現の抽出においては，文型に注目した品詞のパターンマッチングによる抽出を試みた．
【卒業論文】深層生成モデルを用いたユーザ意図に基づく衣服画像の生成に関する研究
「かわいい」や「フェミニン」といった，印象を表す抽象的なファッション用語に基づいて，衣服画像の生成や編集をすることで，ユーザ意図の正確な伝達を支援することを目的とした研究．
灯油タンク内の液面高計測を用いた 灯油残量推定システムに関する研究
近年，家庭への灯油配送においてリモートセンシング化が進んでおり，灯油タンク内の残量計測が可能な通信型レーザセンサの開発が進んでいる．しかし，屋外にある灯油タンクにセンサを設置するため計測環境が不安定で，コスト面から導入可能なデバイスの性能にも制約があるため，センサの故障や異常値の計測が多く発生する．このような背景を踏まえて，本論文では，センサを管理・運用するための運用手法の確立を目的として，2つの異常検知手法と1つの残量推定手法を提案し，実データを用いた有効性の検証を行う．
深層自己回帰モデルを用いた俳句の生成と評価に関する研究
論文では，人工知能による創作の第一歩として俳句，その中でも特に一般的な有季定型句を対象とし，深層学習モデルを用いた生成と評価を行う.
既存の俳句や日本語テキストを用いてモデルを学習し，俳句生成器を構築する.
また，作成したモデルの俳句生成器としての性能を評価するために，俳人による評価済みの俳句からなる評価用データセットを作成して，有季定型句の生成における各モデルの性能を比較する.
評価用データセットの作成においてはまず，有季定型句の評価項目として句会における評価方式と俳句内の句節のつながりに着目し，これらを段階的評価の階層構造として定義した.
定義した階層構造に基づくラベル付を複数の俳人に依頼し，モデル間の性能評価に用いる.
また，各モデルが生成した文字列に対するアンケート調査を行い，現状のモデルの到達点を明らかにする.
競輪におけるレーティングシステムを用いた予想記事生成に関する研究
本研究では，競輪のレース結果予測の初心者が車券を購入する際の意思決定を補助することを目的として，レース結果を予測して，予測結果を用いた選手紹介文を生成するシステムを構築する．選手の戦績から的確に反映する特徴量を算出する手法として，多数人の同時対戦を考慮した拡張イロレーティングシステムを提案して，競輪のレース結果予測に適用する．実際の競輪のレース結果を用いた検証実験において，既存のレース結果予測手法に比べて，拡張イロレーティングシステムを用いることで予測精度を向上可能であることを確認した．さらに，拡張イロレーティングシステムを紹介文生成に用いることで，既存の手法に比べて，初心者の車券購入により有用な文章を生成可能であることを示す．
【卒業論文】B2Bオークションにおけるユーザ別 入札行動予測に関する研究
中古ブランド品の B2B オークションでは，多数のアイテム
のオークションが並列して実施されるため，参加者はすべてのアイテムの価値を検討することが困難であり，入札するアイテムの選択や，留保価格の適切な設定が課題である．本稿では，こうした課題を入札アイテムの推薦や留保価格に対する不落札率の推定により解決するため，オークションにおける多関係推薦モデル MultiRec を用いたユーザ別入札行動予測をオークションデータに適用し，その精度から利用可能性を検証した．
More from harmonylab (20)
Generating Automatic Feedback on UI Mockups with Large Language Models
Generating Automatic Feedback on UI Mockups with Large Language Models
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matching
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matching
A Study on Decision Support System for Snow Removal Dispatch using Road Surfa...
A Study on Decision Support System for Snow Removal Dispatch using Road Surfa...
DLゼミ：Primitive Generation and Semantic-related Alignment for Universal Zero-S...
DLゼミ：Primitive Generation and Semantic-related Alignment for Universal Zero-S...
DLゼミ: MobileOne: An Improved One millisecond Mobile Backbone
DLゼミ: MobileOne: An Improved One millisecond Mobile Backbone
DLゼミ: Llama 2: Open Foundation and Fine-Tuned Chat Models
DLゼミ: Llama 2: Open Foundation and Fine-Tuned Chat Models
DLゼミ: ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation
DLゼミ: ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation
Voyager: An Open-Ended Embodied Agent with Large Language Models
Voyager: An Open-Ended Embodied Agent with Large Language Models
DLゼミ: Ego-Body Pose Estimation via Ego-Head Pose Estimation
DLゼミ: Ego-Body Pose Estimation via Ego-Head Pose Estimation
ReAct: Synergizing Reasoning and Acting in Language Models
ReAct: Synergizing Reasoning and Acting in Language Models
Kin b abst
- 1. Hadoop DB An Analysis of Newspaper Database Using Hadoop